2026-07-12T10:43:33Z https://ijerams.org/index.php/files/oai

oai:ojs2.ijerams.org:article/3 2026-02-18T06:01:04Z files:ART

Optimizing IoT-Based Smart Grids with AI and Blockchain: A New Approach for Real-Time Energy Management Ramesh Bingi Iot, Smart Grids, Artificial Intelligence, Blockchain Real-Time Energy Management The appearance of the Internet of Things (IoT) and the advancement of the energy systems provided a chance to the emergence of the smart grids which allow to arrange the effective management of the energy in real-time. Despite the smart grids being useful when the energy consumption distribution is optimized, they demand substantial challenges in terms of their scalability, security and effective analysis of millions of participated devices-generated data. Blockchain and Artificial Intelligence (AI) have been advanced as the remedy to such concerns. Blockchain can be effectively combined with AI in the work of smart grids with its decentralized immutable traceability and ability to forecast, suggest, and identify anomalies, and with data security, transparency, and integrity, on another level, making smart grids capable of functioning autonomously and efficiently. The feasibility of integrating AI and Blockchain to optimise smart grids on a platform of IoT with the view of employing them in the real-time management of energy is discussed in the paper. The study gives the benefits of using the AI in predictive maintenance, management and management of demand, and demand-response forecasting and demand-response loads, and the Blockchain in safe transaction relating to safe exchange of data and transparent transaction. A conceptual scheme of the way these technologies could be applied in smart grid systems is proposed, and after it, the discussion of the potential use of these technologies, their challenges, and solutions is presented. Based on the study, smart grids that rely on the IoT will be more efficient, secure, and scalable and combining them with the AI and the Blockchain may offer a chance of attaining sustainability in energy management within smart cities. Kaleido Research Publications LLC 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/3 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 9-16 3108-2599 eng https://ijerams.org/index.php/files/article/view/3/2 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/4 2026-02-18T06:02:01Z files:ART

Towards a Unified Blockchain and AI Architecture for IoT-Enabled Autonomous Vehicles: A Machine Learning Approach Banthi lal Bhukya Autos Vehicles, Block Challange, AI, IoT, Machine Learning, Security and V2V communication AVs are coming into the future of transportation in which the utilization of IoT devices which have the capabilities to drive self-driving cars in live time determining all the collection, decision-making and communication with all the other vehicles as well as the infrastructure is extremely essential. However, security, privacy, data integrity and scalability are key variables of the IoT-enabled AV systems. It is decentralized, secure and transparent manner of the Blockchain technology to attend to these problems and there is the requirement of the artificial intelligence (AI) that provides the necessary functionality of the real-time decision-making and optimization. The paper makes the proposal of the integration of Blockchain and AI to reach one architecture to enhance the performance, security and efficiency of the IoTs that have automated vehicles. The specified architecture is suggested to be applied with the support of the Machine Learning (ML) technique to manage data processing and consensus mechanism in the Blockchain so that it was able to comply with the avenues of scalability and real-time aspects of the AVs. In this research a case will be made of the advantages and limitations and the potential application of such an integrated strategy in autonomous vehicle systems like safe data sharing to autonomous decision making to vehicle to vehicle (V2V) communication. Results of experimental research and case studies prove the effectiveness of such combined work to increase the safety and expandability and efficiency of unmanned auto vehicles running. Kaleido Research Publications LLC 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/4 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 17-24 3108-2599 eng https://ijerams.org/index.php/files/article/view/4/3 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/5 2026-02-18T06:02:52Z files:ART

A Novel Machine Learning Algorithm for Enhancing Blockchain Consensus Mechanisms in IoT-Enabled Smart Cities Saddam Hussain MD Contracts Consensus Mechanisms, Internet of Things (IoT), Smart Cities, Scalability, Energy Efficiency, Transaction Speed, Blockchain Optimization, IoT Data Handling, Smart The need to have more effective and secure Blockchain consensus mechanisms has been brought about by the fact that the Internet of Things (IoT) devices have become increasingly deployed in the smart cities. Though Blockchain brings a decentralized, non-reversible platform to implement the IoT applications, the conventional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS) are questionable in its scalability, energy expense, and latency particularly in the IoT systems with extremely large number of devices. To address these challenging concerns in IoT-powered smart cities, this paper proposes a new Machine Learning (ML) algorithm that would generate the best Blockchain consensus mechanism. The ML algorithm is dynamically flexible with time, and modifies the consensus strategy based on the real-time network health, way in which the IoT appliances behave, and load due to transactions giving the solution more scalability, which is more energyefficient and permits to handle transactions at a faster rate. The proposed approach is addressed on the simulated case of a smart city where data, such as those transmitted by smart meters, traffic control sensors, and environmental sensors, are collected on a realtime basis using the IoT technology. The experimental results confirm that the integrated combination of ML and the Blockchain agreement protocols provides the opportunity of refining the functioning IoT systems in the smart cities in relation to the resources optimization, the reduction of latency, the safety of transaction, and the transparency. With the aid of the Internet of Things, the study introduces an easy answer to the issue of scalability and efficiency that the current models of Blockchain pose to smart cities framework in the sense of being able to scale. Kaleido Research Publications LLC 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/5 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 25-31 3108-2599 eng https://ijerams.org/index.php/files/article/view/5/4 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/6 2026-02-18T06:04:56Z files:ART

AI-Driven IoT Security Enhancements Using Blockchain Technology: A DataDriven Approach Sai prasanna Pilli AI, Blockchain, IoT, Security, and Anomaly detection, Data Integrity. The utilities of the IoT devices that industries are currently using are increasing at a very alarming rate based on the latest statistics meaning that very large figures of security issues are raised in terms of management and safeguarding of information. The traditional security systems would fail in an IoT network whereby more and more objects would connect to each other to generate massive amount of data. The paper will discuss the ways in which embracing Artificial Intelligence (AI) and Blockchain can enhance security of the IoT. Artificial Intelligence can identify bug lapses, prevent infiltrations and provide predictive security claims and Blockchain can secure messages and guarantee data continuity and decentralized loyalty. The AI based and blockchain used security protection comes with the security protection provided by the proposed IoT security protection that gives the real time identification and efficacy of the IoT security threats in the IoT systems. In this context, the use of data-driven approach can be implemented and this aspect in itself makes this framework dynamic as pertains to the security environment. This way, it provides a decent cover to a variety of hazards. The paper now looks into realities on how this holistic approach can be applied in the real life of actual implementation of the same in the real life of actual implementation of the IoT system and how effective it can go to the point of making the IoT systems more efficient in the realms of enhancing the security and scalability of the IoT systems. Any case on information theft, unauthorized access to the systems and vulnerability of systems are traced the answer as the results of the experiment show that the AI and Blockchain will be able to provide powerful tool in the securing of IoT devices. Kaleido Research Publications LLC 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/6 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 32-38 3108-2599 eng https://ijerams.org/index.php/files/article/view/6/5 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/8 2026-02-18T06:10:38Z files:ART

AI-Powered Blockchain for Decentralized IoT Applications: Enhancing Security and Efficiency in Edge Computing Dasari Karthik Kumar AI, Blockchain, IoT, Edge computing, Security, decentralization The Internet of Things (IoT) disrupted all the industries that allowed controlling the processes of all businesses in the real-time regime using automatization and optimization. Security issues, centralized control as well as inefficient processing of data on the other hand may face the IoT networks. On edge computing, this is aggravated by the fact that the data being processed is done near the source to minimize latency and bandwidths utilized by the IoT devices. This can be solved because the decentralized blockchain technology is able to offer security and transparency to the process of using the IoT. Besides it, the Artificial Intelligence (AI) had an opportunity to enhance the quality of decision-making and create opportunities to predict and identify anomalies. The paper will address the combination of AI and Blockchain as one of the ways of making the decentralized IoT system much safer and faster in an edge computing environment. The strategy therefore helps in ensuring that the IoT applications will be secure and more productive whereby; BlockChain will be used to handle the data securely and; AI will be used to process and make real-time decisions on how the data will be used. The paper suggests the theoretical framework considering the presence of such technologies in the decentralised IoT platforms and the possible implications to the security, scalability as well as the effectiveness. The possible future applications and trends as well as the real world applications are provided, especially of the smart cities, healthcare and industrial IoT. Kaleido Research Publications LLC 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/8 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 1-7 3108-2599 eng https://ijerams.org/index.php/files/article/view/8/6 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/9 2026-02-18T06:16:18Z files:ART

Blockchain and IoT Integration for Secure and Transparent Supply Chain Management Athram Mahesh Athram Mahesh Block chain, IoT supply chain controls, information Security, transparencies Even the tone of proposing the idea of Internet of Things (IoT) to the supply chain management process has already made quite a difference in the sense of promising a more efficient process in the operations process, inventory management and actual tracking of various goods in real time. But with regards to data security, data privacy and data transparency of the data, the question of the data appears in the case of IoT system in the supply chain. The block chain technology has been one of the solutions to such issues as it is an immutable transparent and decentralised technology. As it is a collaborative process, data integrity, traceability, transparent and secure transaction along the supply chain with use of IoT and the Blockchain can be contributed even by the organization. The paper has raised the issue of the combination of the Blockchain and IOT into the supply chain management especially its secure and transparent supply chain management and how blockchain can be used as a means of mesh-up of exchanges, the provenance of products and security of regulatory compliance. The condition of the application of the IoT devices in the context of the data collection and data transfer and the method that Blockchain can be employed in the context of the security of the collected data and its real-time transparency is also stated in the paper. The case studies and simulation represented by the document demonstrate that, it is possible to combine both Blockchain and IoT in a bid to enhance an effectiveness of the operations, and eliminate frauds, as well as allow general visibility of the supply chain. It also looks through the problems that are stepped on along with the implementation of such collective actions, and it is basing on it that answers are given to the questions on how to win the problems. Kaleido Research Publications LLC 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/9 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 8-14 3108-2599 eng https://ijerams.org/index.php/files/article/view/9/7 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/11 2026-02-18T06:21:22Z files:ART

Blockchain-Enabled AI Frameworks for Predictive Analytics in IoT-Driven Environmental Monitoring Systems Deekonda pranaykumar Deekonda pranaykumar Blockchain, AI, Internet things, Environmental Monitoring, predictive analytics, Security. Monitoring of the environment systems that have emerged on the basis of the Internet of Things (IoT) give in real time some of the environmental parameters like the temperature, humidity, air and pollution quality. Nevertheless, there are certain issues associated with the presence of the systems due to massive real-time data streaming through the devices of the IoT and they are data integrity, security, and scaling. Blockchain has a read-only access to a distributed ledger; a ledger whose data management under a distributed ledger is both secure, transparent and cannot be mutilated. Completely to the opposite, with the help of the Artificial Intelligence ( AI ), it is possible to make the anomalies and decisions out of the data with the help of the predictions and anomalies. The article gives a clue not only how to integrate Blockchain and AI into the system containing the track of IoT-driven solutions but also how to enhance the procedure monitoring IoT-driven solutions, including those of them encouraged to turn to predictive analytics. The model will also improve the level of precision, safety and efficiency of an environment observation by using a rather advanced technology-Blockchain that helps to store and transport safe data; Artificial intelligence (AI) to predict, generate appearance and optimize it. All these challenges that the alliance will have to encounter and all the applications in the future related to an association such as this and the benefits related to the alliance have all been given out in the paper. It is possible to use an idea of conceptual framework and use of case study simulation that is employed in the current paper in utilisation of blockchain-based artificial intelligence in the two ways; creating some add on case studies as model in prediction of the environmental risk, a model to simulate the use of case study. Kaleido Research Publications LLC 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/11 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 15-21 3108-2599 eng https://ijerams.org/index.php/files/article/view/11/8 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/12 2026-02-18T06:32:32Z files:ART

Blockchain-Integrated Machine Learning for Autonomous IoT Networks: A Paradigm Shift in Data Security Adigoppula Ashwitha Machine Learning, Blockchain, Internet of things, Data security, Autonomous system. With the introduction of the idea of the IoT (the Internet of Things), new opportunities and the issues that come along with it in the area of data safety are created. The greater the number of devices connected to the Internet of Things network, the more complex and bulky the field of securing data becomes. The Blockchain and Machine Learning (ML) technology can provide a solution to that end in order to eliminate such challenges. Blockchain offers privacy, transparency and scalable features through which data integrity is improved, and the ML brain is a solution to facilitate autonomous decision and detecting anomalies inside the IoT network. In this paper, the author explains how Blockchain and ML may be used to create a secure and stable IoT ecosystem. It talks about how the integration can maximize data security and the optimum resources and scalability of independent operations of the networks IoT. The study also speaks about the possible benefits, difficulties and the future consequences of such paradigm shift on the security of the data. Through its critical investigation, the following paper is going to provide an outline of future studies and potential application of Machine Learning uses Blockchain in self-managing internet-of-things networks. Kaleido Research Publications LLC 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/12 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 22-29 3108-2599 eng https://ijerams.org/index.php/files/article/view/12/10 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/13 2026-02-18T06:41:41Z files:ART

Exploring Blockchain-Based Trust Models in IoT-Driven Healthcare Systems: A Machine Learning Approach Karabathula Keerthi Machine learning, ( IoT ) Healthcare, Blockchain, Trust Models, Healthcare. The IoT devices are experiencing growth in the healthcare industry as they are being used to track the health status of patients, to treat them and also to perform other functions. However, the IoT-based healthcare system has enormous problems regarding data security, confidentiality, and trust, especially when salient medical information is being transferred across various networks. One of the potential answers to these hiccups is the blockchain technology that is not centralized, immutable and transparent. Together with that, trust models could be enhanced by utilising the capabilities of Machine Learning (ML), through predictive analytics, anomaly detection, and real-time decision-making, which will further increase the security and efficiency of the IoT healthcare system. The article explains the process of the integration of such tools as Blockchain-based trust models, and Machine Learning used in IoT-powered healthcare environments. This research paper aims at developing an efficient solution on how to protect the IoT health networks, open data sharing, and develop confidence between the devices and the healthcare provider and, patients. Using the case studies and simulations, we represent how Blockchain and ML can be applied jointly so that to develop safe, effective, and scalable health applications. The paper also disadvantages and provides a glimpse of what will be done on the future research of using the technologies to enhance the delivery of the healthcare. Kaleido Research Publications LLC 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/13 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 30-35 3108-2599 eng https://ijerams.org/index.php/files/article/view/13/11 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/16 2026-02-18T05:59:20Z files:ART

The Utilization of Machine Learning and Blockchain in the Safe Handling of a Supply Chain in IoT Ecosystems Avinash Gurram blockchain, Machine Learning, IoT Ecosystems, SCM, security The revolution in the management of supply chain through introduction of the Internet of Things (IOT) devices will be pushed by automation since the devices can be tracked and monitored in real time. Storing much information that is highly secured however is a very hectic process. The databases that were established with the old systems of the supply chain are of a centralized variety and can be easily tampered with, hence, susceptible to in-efficient processes besides being vulnerable to security risk. The given paper is devoted to the domain of the supply chain security, transparency, and efficiency in regard to the opportunities of the Blockchain and Machine Learning (ML). Blockchain has also been termed as irreconcilable decent and distributed ledger that creates certain transactions that bode well in respect to the other end of the scale is data quality and ML that provides an idea on the supply chain, tracking anomaly in addition to intelligent decision-making. The two technologies are capable of coming up with an empowering technology that will further increase the level of trust the company will enjoy, reduce the importance of the money used in the course of operation and the outcome of it will be the real time decision making. The hypothesis of the paper posits the descriptions of the application, the perceived gains and the challenges and how they are going to be incorporated in the current supply chains of the technologies. The work integrates the possibilities of the Machine Learning and the Blockchain phenomenon and explains the conceptual model, and after that, the synergetic dilation can be an adequate, efficient, and extensive solution to the problem of supply chain management in the contemporary world. Kaleido Research Publications LLC 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/16 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 1-8 3108-2599 eng https://ijerams.org/index.php/files/article/view/16/12 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/19 2026-02-18T07:32:30Z files:ART

Cognitive Outcomes in Post-COVID-19 Patients: A Cross-Sectional Neurological Assessment Medi Jyothi SARS-CoV-2, Post-infection cognitive dysfunction., Neurocognitive assessment, Urban Indian population, Neuropsychological testing, Neurological long-term COVID The cognitive impairment, also known as the brain fog, is also one of the severe consequences of post-acute COVID-19. Although, there are international data illustrating the high effect of neurocognitive, evidence shows that there is a shortage of evidence which illustrates the effect in the Indian populations. This paper was intended to compare cognitive functioning among COVID-19 epidemic survivors living in urban India, define and quantify the frequency, size and predictors of cognitive dysfunction using conventional neuropsychological tests. It was a cross-sectional study conducted in three tertiary hospitals in the time frame of January 2023 to March 2024 in Mumbai, Delhi and Bengaluru. They included four hundred and fifty-one COVID-19 recovered adults (at least 12 weeks after their infections) with a mean age of 1865 years. It was assessed using the cognitive ability which included Montreal Cognitive Assessment (MoCA), Digit Span Test and Trail Making Test (TMT). The severity of the disease, comorbidity and information on hospitalization together with demographics were received. To determine the variables that can be used to predict cognitive impairment, statistical tests were undertaken using t-tests, analytical one way ANOVA and multiple linear regression. The percentage of those whose thinking was impaired, mildly (MoCA < 26), was 38.7 percent. The patients who had the severe cases of COVID-19 recorded very low scores in the MoCA values (23.5 ± 2.8) than those who had mild cases (27.1 ± 1.9, p < 0.001). Predictive significant factors of cognitive decline were hospitalization, hypoxia, old age, and comorbidities. The areas of most prominent impairments were in the areas of attention, executive functioning and memory. Long-term cognitive dysfunction occurs at a high rate among post-COVID-19 patients in urban India several months post-recovery. It might also be significant to make sure that the neurological morbidity is delayed through routine cognitive screening and timely rehabilitation. Kaleido Research Publications LLC 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/19 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 1-6 3108-2599 eng https://ijerams.org/index.php/files/article/view/19/14 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/20 2026-02-18T07:32:30Z files:ART

Prevalence of Multidrug-Resistant Urinary Tract Infections and Their Antibiotic Susceptibility Patterns in Tertiary Care Hospitals Shaik Shukoor UTI, antimicrobial resistance, MDR bacteria, antimicrobial susceptibility, tertiary care hospital. UTIs are some of the most frequent cases of bacterial infections that are experienced in the community and in hospitals. The rising occurrence of multidrug-resistant (MDR) pathogens makes the process of treatment more complicated and contributes to morbidity. To establish the prevalence, bacterial profile and antimicrobial resistance patterns of the pathogens responsible of urinary tract infection in the tertiary care hospitals. The study was a crosssectional one conducted during six months (January to June 2024) and on 500 patients diagnosed with UTIs clinically. Middle urines were collected and cultured. Standard biochemical tests were used to identify bacterial isolates and antibiotic susceptibility testing was done using KirbyBauer disk diffusion method according to CLSI 2024 guidelines. Among 500 samples, 310 (62) of them had a significant bacteria growth. The most common isolate was Escherichia coli (48), then Klebsiella pneumoniae (22), Enterococcus spp. (15), Pseudomonas aeruginosa (8) and Proteus mirabilis (7). Forty six percent of the isolates were detected with MDR, mostly Gram-negative bacteria. The resistance was high against the Ceftriaxone (72%), Ciprofloxacin (64%), and Ampicillin (80%), but Nitrofurantoin (88%), and Imipenem (90%) were effective. The analysis demonstrates that MDR urinary pathogens are present at a very elevated rate, and the regular monitoring of antimicrobials usage and the reasonable use of antibiotics are necessary to avoid the further development of resistance. Kaleido Research Publications LLC 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/20 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 7-10 3108-2599 eng https://ijerams.org/index.php/files/article/view/20/15 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/21 2026-02-18T07:32:30Z files:ART

Prevalence and Antimicrobial Resistance Patterns of Hospital-Acquired Infections in Tertiary Care Centers Jarupla Aravind Hospital-acquired infections, Antimicrobial resistance, Tertiary care, MDR organisms, Infection control. HAIs are among the major global health problems, in terms of morbidity, mortality, and health care spending. The growing incidences of the multi-drug-resistant (MDR) organisms also make it difficult to manage and control infections among patients. The study aimed to determine the occurrence rates of HAIs, microbial profile and resistance to antimicrobials of HAIs in tertiary care units. It was cross-sectional observational study that was conducted in three tertiary hospitals. The clinical samples included inpatients who contracted an infection not less than 48 hours after admission: urine, sputum, pus, and blood. The bacterial isolates were identified by conventional microbiological techniques and antibiotic susceptibility with the assistance of the KirbyBauer disk diffusion technique as per the CLSI guidelines of 2024. Among inpatients (n=1000) surveyed, 286 (28.6) of them later suffered HAIs. The most common sites of infection were urinary tract (35%), surgical wounds (25) and respiratory tract (22). The gram negative bacteria (72 percent) were the most predominant, with Klebsiella pneumoniae (24 percent), Escherichia coli (18 percent), and Pseudomonas aeruginosa (16 percent) being the other common bacteria species. It was found that all the resistance rates were high against the cephalosporins (6578%), fluoroquinolones (60%), and colistin and carbapenems were still sensitive (>80%). The high rates and alarming rates of HAIs indicate the urgent need to possess antibiotic stewardship, strengthened infection control measures and surveillance programs to assist in the reduction of the spread of the resistant pathogens. Kaleido Research Publications LLC 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/21 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 11-15 3108-2599 eng https://ijerams.org/index.php/files/article/view/21/16 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/22 2026-02-18T07:32:30Z files:ART

Evaluating the Impact of Early Screening on the Prognosis of Type 2 Diabetes in Urban Populations of India Shameer shaik Type 2 Diabetes, Early screening, India, Prognosis, Urban Population, HbA1c, Complications. Type 2 Diabetes Mellitus (T2DM) is a rapidly increasing issue of the Indian population health and is particularly common in the urban areas of India where lifestyle causes encourage the development and progression of type 2 diabetes. Early screening plays a significant role in enhancing the outcome of the patient better since it offers timely interventions. This study was aimed at examining the impact of early screening on the clinical outcome and complications prevalence of T2DM in urban India. The design of the study was cross-sectional cohort study that was conducted in 3 big urban cities, i.e., Mumbai, Delhi, and Bengaluru. The patients were recruited (600 patients aged 30- 60 years in total) in two subgroups early-screened (diagnosed during the course of a regular screening before the symptoms appear, n = 300) and late-diagnosed (diagnosed when the symptoms appeared, n = 300). Medical records, laboratory findings, and structured interviews were used to obtain the data. The variables employed in the assessment of prognosis included the levels of HbA1c, complications, and adherence to the lifestyle modification programs. These statistical tests were chi-square tests, t-tests and multivariate logistic regression. The screened group showed significantly lower values of the mean HbA1c level (6.8 ± 0.7) as compared to the late-diagnosed group (8.3 ± 1.1, p = 0.001). The cases of complications such as neuropathy and retinopathy were significantly reduced in the early-screened cohort (12% vs. 28 p < 0.01). Multivariate analysis revealed that screening at a young age reduced the risk of acquiring complications by 45 percent (OR: 0.55; 95 percent CI: 0.38 -0.79). Diagnosis at an early stage is a significant contribution to the management of glycemia and a reduction in the rates of complications among urban Indian individuals with T2DM. Public health policies which promote periodic screening on the high-risk groups of individuals will bring about a substantial change in the long-run. Kaleido Research Publications LLC 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/22 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 16-22 3108-2599 eng https://ijerams.org/index.php/files/article/view/22/17 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/23 2026-02-18T07:32:30Z files:ART

Vitamin D Deficiency: The regularity and bivariate relationship with Anemia among Adults in Urban Populations: a cross sectional study Shanmukhi Bejagam Vitamin D deficiency, Anemia, Hemoglobin, 25(OH)D, Nutritional deficiency, Urban population. The most common diseases that give rise to nutritional diseases that are likely to attack the general society include the vitamin D deficiency and anemia that are more common in the developing states. The other process is the calcium metabolism which incorporates the vitamin D in addition to the erythropoiesis process which involves the regulation of the erythropoietin as well as the activity of the bone marrow. Nevertheless, they have not done adequate correlation of the amount of Vitamin D and anemia on the adult population in the urban population. The study aim was to identify the status of Vitamin D deficiency and association with anemia in adults in a tertiary care unit in an urban population. The research design will be cross-sectional study and based on a population of forty one thousand and one hundred adults between the age of 18-60 years who visit the outpatient departments of a tertiary hospital in the period between January and June of 2024. The quantities of hemoglobin (Hb) were explained utilizing the chemiluminescent immunoassay and the hematology automated analyser to quantify the amounts of serum 25- hydroxyvitamin D [25(OH)D] and the hematology automated analyser respectively. The anemia was within the WHO. Pearson correlation coefficient and regression analysis helped to consider the statistical correlation of Vitamin D and hemoglobin levels. Among the whole group of the study participants (260/118), 260 (65) and 118 ( 29.5) were not only ill of Vitamin D (<20 ng/mL), but also anaemic, respectively. This was also established to be the case since, the Vitamin deficient individuals had 85 (32.7) anaemic individuals and 33 (17.5) Vitamin D adequate individuals (p = 0.004). They have not demonstrated a statistically significant difference of the mean levels of Vitamin D (16.4 + 5.3 ng/mL in anemic and 24.2 + 7.6 ng/mL in non-anemic). There was a positive correlation between Vitamin D (Serum Vitamin D) and hemoglobin (r = 0.36, p < 0.001). The deficiency of vitamin D is also excessive in the case of the urban adults themselves and it becomes directly adopted against the anemia. The other diagnostic and therapeutic application that can be made is screening of anemic patients in vitamin D. The interventional studies are required further to provide the solution to consumption and treatment outcome. Kaleido Research Publications LLC 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/23 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 23-28 3108-2599 eng https://ijerams.org/index.php/files/article/view/23/18 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/27 2026-02-18T07:35:19Z files:ART

Seroprevalence and Risk Factors of Hepatitis B and C Virus Infections among Pregnant Women in Kaduna State, Nigeria Sarah Nuhu Kase Christy Chinyere Fredrick Amaechi Dennis Esther Luka Abdulkarim Garba Ninani Jonah Danladi Haruna Danlami Sambo Magdaline Joseph Kwaji Jamila Ibrahim Suleiman Theophilus I. Ojemudia Mercy Kure Hepatitis, HBV, HCV and Pregnant women Background: Hepatitis is caused by Viruses that are all contagious and the infection can be transmitted from one person to another. Estimating the prevalence of hepatitis B and C viral infections among pregnant women will help reduce mortality and morbidity rates in these subjects. Methodology: A total of three hundred (300) blood samples were collected from three Hospitals in Saminaka, Lere and Gure based on the availability of samples. The sera samples were assayed using in-vitro diagnostic kit (dipsticks/strips) to detect the hepatitis B surface antigen (HBsAg) and antibodies to hepatitis C virus (anti-HCV). Results: Test strip revealed that 18/330 (6.00%) of pregnant women tested positive for HBV and 15/300 (5.00%) were infected with HCV. The Enzyme Linked Immunosorbent Assay (ELISA) kit confirmed 25/300 (8.33%) of HBV and 21/300 (7.00%) of HCV in the study population.. Conclusion: There is need for proper screening of blood before transfusion as most women that had blood transfusion once were found to be infected also there is need for proper sensitization about the disease as many participants were not aware of its capacity to spread and cause life threatening infection. Kaleido Research Publications LLC 2026-11-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/27 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 4, November 2025; 1-12 3108-2599 eng https://ijerams.org/index.php/files/article/view/27/19 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/28 2026-07-04T05:35:22Z files:ART

Gut Microbiota in Postprandial Immunometabolism: A Systematic Review Mahima Gut Microbiota, Postprandial Immunometabolism, Immune Modulation, Metabolic Health, Dysbiosis, Short-Chain Fatty Acids (SCFAs), Immune System, Postprandial State Gut microbiota is critical for human health, contributing to various biological processes, such as immune responses and metabolism. Postprandial immunometabolism is the metabolic and immune activity that occurs after a meal and is influenced by the gut microbiota. In this review, we discuss how gut microbiota influences postprandial immune responses and metabolism. We explore how short-chain fatty acids (SCFAs) and other microbial metabolites influence immune cell activation and metabolic pathways (nutrient breakdown) during the postprandial period. Imbalances in the gut microbiota (dysbiosis) can cause immune and metabolic dysfunction, which may contribute to metabolic diseases such as obesity and type 2 diabetes. Additionally, nutrition strategies such as probiotics and prebiotics can promote a healthy microbiota, enhancing postprandial metabolic and immune responses. This review discusses the latest research and the potential of microbiota manipulation to maintain metabolic health and prevent metabolic diseases. Kaleido Research Publications LLC 2026-05-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/28 10.65477/2026.v.2i.5.01 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 5, May 2026; 1-8 3108-2599 eng https://ijerams.org/index.php/files/article/view/28/20 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/29 2026-07-04T05:35:22Z files:ART

A Retrospective Medico-Legal Profiling Study of Drug-Related Suspects Examined by the Judicial Medical Officer at District General Hospital, Mullaitivu (2020–2025) Vaasuthevaa K Tennakoon A Drug-related crime; Judicial Medical Officer; medico-legal profiling; opioid withdrawal; Sri Lanka; Mullaitivu; retrospective cohort Background: Drug-related crime and substance abuse continue to escalate in Sri Lanka, including in post-conflict districts such as Mullaitivu. Judicial Medical Officers (JMOs) occupy a pivotal role in assessing suspects brought by police, yet empirical data from Northern Province JMO registries remain scarce. This study aimed to characterise the socio-demographic, clinical, and medicolegal profiles of drug-related suspects examined at the District General Hospital (DGH), Mullaitivu. Methods: A retrospective cohort study was conducted using archived medico-legal examination records of 50 suspects produced by police to the Office of the Judicial Medical Officer, DGH Mullaitivu, between August 2020 and July 2025. A structured pro-forma extracted socio-demographic data, substance-use histories, crime typologies, clinical examination findings, and medico-legal opinions. Data were analysed using SPSS version 22; categorical variables are expressed as frequencies and percentages. Results: All 50 suspects were male. The predominant age group was 20–29 years (68.0%). Most were married (56.0%), held only primary education (72.0%), and earned less than LKR 20,000 per month (46.0%). Unemployment and self-employment together accounted for 54.0% of the cohort. Illicit drugs (primarily opioids) constituted the arrest substance in 80.0% of cases. The mean age of first drug use was 23.8 years (range 13–36). Consumption was the most common crime type (56.0%), and 82.0% reported being under the influence at the time of the offence. Opioid withdrawal signs were documented in 86.0% of cases. The predominant medico-legal opinion was that the suspect was under the influence of a drug (46.0%), followed by clinical signs consistent with drug or alcohol abuse (38.0%). Conclusions: This study provides baseline medico-legal data on drug suspects in a Northern Province district previously understudied. Young, socio-economically marginalised males dominate the cohort; opioid use and withdrawal are the principal clinical features. Findings underscore the need for targeted rehabilitation frameworks and enhanced forensic documentation in postconflict settings. Kaleido Research Publications LLC 2026-05-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/29 10.65477/2026.v.2i.5.02 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 5, May 2026; 9-16 3108-2599 eng https://ijerams.org/index.php/files/article/view/29/21 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/30 2026-06-25T11:37:22Z files:ART

Multimodal Artificial Intelligence in Oncology: Integrating Radiomics, Pathomics, and Genomics Prof. Dr. A. D. Shinde Prof. Vikrant Popat Taware Mr. Shatrughna U. Nagrik Multimodal artificial intelligence; oncology; radiomics; pathomics; genomics; precision medicine; deep learning; transformer models; explainable AI; multi-omics integration. The emergence of artificial intelligence (AI) has fundamentally transformed modern oncology by enabling the computational interpretation of increasingly complex biomedical datasets derived from radiology, digital pathology, genomics, transcriptomics, and electronic health records. The integration of these heterogeneous data sources has facilitated the development of multimodal AI frameworks capable of generating biologically meaningful and clinically actionable insights across the entire cancer care continuum. Unlike unimodal computational approaches that analyze isolated data modalities, multimodal AI combines radiomics, pathomics, and genomics to comprehensively characterize tumor heterogeneity at anatomical, histopathological, molecular, and temporal levels. Recent advances in deep learning, transformer architectures, self-supervised learning, and foundation models have substantially enhanced the capacity of multimodal systems to identify latent biological patterns associated with tumor initiation, disease progression, therapeutic resistance, and patient survival. These technologies are increasingly being applied to early cancer detection, molecular subtyping, prognostic risk stratification, immunotherapy response prediction, and precision therapeutic decision-making. Despite these advances, several translational challenges remain, including inadequate data harmonization, algorithmic bias, limited model interpretability, regulatory uncertainty, and the lack of large-scale prospective clinical validation. In addition, ethical considerations surrounding data governance, privacy preservation, and equitable implementation continue to influence the clinical adoption of AI-driven oncology solutions. This review critically examines the evolution of multimodal AI in oncology, with particular emphasis on radiomics, pathomics, and genomics integration strategies, transformer-based architectures, explainable AI, and multimodal fusion techniques. The article further explores contemporary clinical applications, translational challenges, and emerging opportunities involving foundation models and federated learning within precision oncology. Collectively, multimodal AI represents a transformative paradigm with the potential to redefine oncologic diagnosis, personalize therapeutic interventions, strengthen clinical decision support, and accelerate the advancement of precision cancer medicine. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/30 10.65477/pspk4222 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 1-10 3108-2599 eng https://ijerams.org/index.php/files/article/view/30/22 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/31 2026-06-25T11:42:44Z files:ART

Advancing Predictive Oncology: Artificial Intelligence for Early Cancer Detection, Risk Stratification, and Personalized Treatment Damiki Laloo Mr. Shatrughna U. Nagrik Predictive Oncology; Artificial Intelligence; Cancer Risk Prediction; Digital Biomarkers; Precision Medicine; Multimodal AI; Radiogenomics; Multi-Omics Integration; Explainable AI; Federated Learning; Early Cancer Detection; Personalized Cancer Therapy. The growing complexity of cancer biology, characterized by extensive molecular heterogeneity, temporal evolution, therapeutic resistance, and variable clinical outcomes, has created significant challenges for conventional oncology practice. Traditional cancer management strategies have primarily relied on imaging findings, histopathological assessment, and individual molecular markers, often providing an incomplete representation of the multidimensional processes that drive tumor development and progression. In response, predictive oncology has emerged as an innovative paradigm that utilizes artificial intelligence (AI), digital biomarkers, multi-omics integration, radiogenomics, and continuously generated clinical data to support early cancer detection, individualized risk stratification, and precision-guided therapeutic decision-making. By leveraging machine learning, deep learning, transformer-based models, and multimodal foundation architectures, predictive oncology enables the analysis of complex datasets derived from radiological imaging, digital pathology, genomics, transcriptomics, proteomics, liquid biopsies, wearable health technologies, and electronic health records. These advanced computational approaches facilitate the identification of subtle disease-associated patterns, prediction of tumor progression, assessment of treatment responsiveness, and optimization of personalized cancer care pathways. Recent developments in AI-enabled oncology have substantially improved cancer screening programs, prognostic modeling, recurrence prediction, survival forecasting, digital pathology interpretation, immunotherapy response evaluation, and adaptive treatment planning. Deep learning algorithms, including convolutional neural networks and transformer-based frameworks, have demonstrated high-performance capabilities in the automated analysis of medical images and histopathological specimens, often approaching expert-level accuracy. Furthermore, multimodal AI systems integrating molecular, radiological, and clinical information have accelerated the discovery of predictive biomarkers that support precision medicine across diverse malignancies. Nevertheless, important challenges remain, including issues related to model transparency, algorithmic fairness, data harmonization, privacy protection, regulatory validation, and integration into routine clinical workflows. This review examines the emergence of predictive oncology and explores the expanding role of artificial intelligence in early cancer detection, digital biomarker identification, clinical risk prediction, and precision therapy. Additionally, the article discusses emerging technologies including multimodal learning frameworks, explainable AI systems, federated oncology infrastructures, and future translational opportunities that may shape the next generation of intelligent precision oncology. Kaleido Research Publications LLC 2025-12-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/31 10.65477/bq8wtj80 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 5, December 2025; 01-09 3108-2599 eng https://ijerams.org/index.php/files/article/view/31/23 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/32 2026-07-02T10:28:15Z files:ART

Advances in Hybrid PET/CT Imaging: Emerging Molecular Tracers and AI Applications in Precision Oncology Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Miss. Bindu Gajanan Dr. Vishal Rastogi PET/CT, oncology, tumor staging, treatment monitoring, digital PET, time-of-flight, PET/MRI, PSMA, FAPI, DOTATATE, FLT, radiomics, SUV, metabolic tumor volume, deep learning. Positron Emission Tomography–Computed Tomography (PET/CT) has become integral to modern oncology, offering combined functional-metabolic and anatomical information. Recent technological advances – including digital PET detectors, highperformance time-of-flight (TOF) systems, and hybrid PET/MRI platforms – have markedly enhanced PET image quality and quantification. Concurrently, novel PET radiotracers beyond standard ^18F-FDG (fluorodeoxyglucose) are emerging, targeting specific tumor features: for example, PSMA ligands for prostate cancer, FAPI ligands for fibroblast-rich tumors, somatostatin analogues (DOTATATE) for neuroendocrine tumors, FLT for proliferation, and amino-acid probes for brain and prostate cancers. These tracers often detect lesions that FDG misses. Quantitative PET biomarkers have advanced from simple SUVs (standardized uptake values) to volumetric measures (metabolic tumor volume, total lesion glycolysis) and high-dimensional radiomic features. Artificial intelligence (AI) and deep-learning tools now enable automated tumor segmentation, noise reduction, and prognostic modeling from PET images. Clinically, extensive evidence shows PET/CT improves staging and response assessment in major cancers. For example, PSMA PET/CT detects bone metastases in prostate cancer with ~98% sensitivity versus ~85% for traditional bone scintigraphy, and FDG PET/CT upstages breast and lung cancers in a significant fraction of cases. Compared to conventional imaging (CT, MRI, bone scan), PET/CT often demonstrates higher sensitivity and comparable specificity for nodal and distant metastases. PET/MRI may further improve lesion detection in selected cancers while reducing radiation dose. Workflow improvements from total-body PET and AI can accelerate scans and reduce dose. Cost-effectiveness studies suggest PET/CT can be economically justified by avoiding unnecessary biopsies or treatments, although access and reimbursement vary. Radiation dose and tracer safety remain important; FDA approvals exist for many tracers (e.g. ^68Ga-PSMA-11, ^18F-DCFPyL, ^68Ga-DOTATATE) while novel agents (e.g. FAPI) are investigational. This comprehensive review synthesizes recent literature and guidelines (SNMMI, EANM, NCCN) to provide an updated analysis of PET/CT innovations in oncology. It details technical advances, new tracers, quantitative imaging, and clinical evidence across major tumor types, compares PET/CT to other modalities, and discusses economic and regulatory considerations. Kaleido Research Publications LLC 2026-04-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/32 10.65477/j56gk292 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 4, April 2026; 1-9 3108-2599 eng https://ijerams.org/index.php/files/article/view/32/24 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/33 2026-07-02T10:28:15Z files:ART

Therapeutic Use of Tolvaptan in Cancer-Related Hyponatremia: Benefits, Risks, and Clinical Guidance Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Miss. Bindu Gajanan Dr. Vishal Rastogi PET/CT, oncology, tumor staging, treatment monitoring, digital PET, time-of-flight, PET/MRI, PSMA, FAPI, DOTATATE, FLT, radiomics, SUV, metabolic tumor volume, deep learning. Positron Emission Tomography–Computed Tomography (PET/CT) has become integral to modern oncology, offering combined functional-metabolic and anatomical information. Recent technological advances – including digital PET detectors, highperformance time-of-flight (TOF) systems, and hybrid PET/MRI platforms – have markedly enhanced PET image quality and quantification. Concurrently, novel PET radiotracers beyond standard ^18F-FDG (fluorodeoxyglucose) are emerging, targeting specific tumor features: for example, PSMA ligands for prostate cancer, FAPI ligands for fibroblast-rich tumors, somatostatin analogues (DOTATATE) for neuroendocrine tumors, FLT for proliferation, and amino-acid probes for brain and prostate cancers. These tracers often detect lesions that FDG misses. Quantitative PET biomarkers have advanced from simple SUVs (standardized uptake values) to volumetric measures (metabolic tumor volume, total lesion glycolysis) and high-dimensional radiomic features. Artificial intelligence (AI) and deep-learning tools now enable automated tumor segmentation, noise reduction, and prognostic modeling from PET images. Clinically, extensive evidence shows PET/CT improves staging and response assessment in major cancers. For example, PSMA PET/CT detects bone metastases in prostate cancer with ~98% sensitivity versus ~85% for traditional bone scintigraphy, and FDG PET/CT upstages breast and lung cancers in a significant fraction of cases. Compared to conventional imaging (CT, MRI, bone scan), PET/CT often demonstrates higher sensitivity and comparable specificity for nodal and distant metastases. PET/MRI may further improve lesion detection in selected cancers while reducing radiation dose. Workflow improvements from total-body PET and AI can accelerate scans and reduce dose. Cost-effectiveness studies suggest PET/CT can be economically justified by avoiding unnecessary biopsies or treatments, although access and reimbursement vary. Radiation dose and tracer safety remain important; FDA approvals exist for many tracers (e.g. ^68Ga-PSMA-11, ^18F-DCFPyL, ^68Ga-DOTATATE) while novel agents (e.g. FAPI) are investigational. This comprehensive review synthesizes recent literature and guidelines (SNMMI, EANM, NCCN) to provide an updated analysis of PET/CT innovations in oncology. It details technical advances, new tracers, quantitative imaging, and clinical evidence across major tumor types, compares PET/CT to other modalities, and discusses economic and regulatory considerations. Kaleido Research Publications LLC 2026-04-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/33 10.65477/j29hcv64 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 4, April 2026; 10-18 3108-2599 eng https://ijerams.org/index.php/files/article/view/33/25 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/34 2026-07-02T10:28:15Z files:ART

Current Advances in Lung Cancer Chemotherapy and Systemic Treatment: A Comprehensive Review Dr. Vishal Rastogi Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Miss. Bindu Gajanan Lung cancer, chemotherapy, treatment, cancer, drugs, side effects, surgery, radiation therapy, targeted therapy, immunotherapy, healthcare Lung cancer is a leading cause of cancer-related deaths worldwide. Chemotherapy, a systemic treatment, plays a crucial role in managing this disease. This review delves into the principles of chemotherapy for lung cancer, focusing on commonly used drugs, administration methods, and potential side effects. Additionally, it explores the integration of chemotherapy with other treatment modalities, such as surgery, radiation therapy, targeted therapy, and immunotherapy, in a comprehensive approach to lung cancer management. While chemotherapy offers significant benefits, it?s essential to acknowledge its potential side effects and discuss strategies for their management. By understanding the nuances of chemotherapy and its role in the broader context of lung cancer treatment, healthcare providers can optimize patient care and improve outcomes for individuals affected by this disease. Kaleido Research Publications LLC 2026-04-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/34 10.65477/fbsawc03 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 4, April 2026; 19-23 3108-2599 eng https://ijerams.org/index.php/files/article/view/34/26 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/36 2026-07-04T05:35:22Z files:ART

Contemporary Approaches to Ovarian Cancer Screening and Early Diagnosis: A Review Dr. Vishal Rastogi Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Miss. Bindu Gajanan Ovarian cancer, Early diagnosis, Screening methods, CA-125, Transvaginal sonography Ovarian cancer has emerged as one of the most common malignancies affecting women in India and the most lethal gynecological malignancy, is a significant global health challenge, with >324,000 new cases and >200,000 deaths being reported annually. OC is characterized by late-stage diagnosis, a poor prognosis, and 5-year survival rates ranging from 93% (early stage) to 20% (advanced stage). Despite advances in genomics and proteomics, effective early-stage diagnostic tools and population-wide screening strategies remain elusive, contributing to high mortality rates. Novel screening methods including organoids and multiplex panels are being explored to overcome current diagnostic limitations. It is not curable but three most extensively evaluated screening methods for ovarian cancer are pelvic examination, serum CA 125, and transvaginal sonography (TVS). The lack of sensitivity of pelvic examination and serum CA 125 has limited their use in ovarian cancer screening. Currently, the most effective screening method for ovarian cancer is TVS. Genetic testing for gene mutations that affect treatment is the standard of care for all women with epithelial ovarian cancer. Nearly all women will have a recurrence, and the treatment of recurrent ovarian cancer continues to be nuanced and requires extensive review of up-to-date modalities that balance efficacy with the patient?s quality of life. This review highlights the critical need for continued research and innovation to enhance early diagnosis, reduce mortality, and improve patient outcomes in ovarian cancer. Kaleido Research Publications LLC 2026-05-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/36 10.65477/x221cr25 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 5, May 2026; 17-23 3108-2599 eng https://ijerams.org/index.php/files/article/view/36/27 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/37 2026-07-04T05:35:22Z files:ART

Nanocellulose as a Multifunctional Platform for Targeted Cancer Therapy and Molecular Imaging Miss. Bindu Gajanan Dr. Vishal Rastogi Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Nanocellulose, cancer therapy, drug delivery, photothermal therapy, theranostics, biocompatibility. The recent development of nanocellulose as a biomaterial platform to treat cancer is based on the excellent physicochemical characteristics, biocompatibility, and the ability to functionalize it in diverse ways. This review examines the current state of nanocellulose-based systems in oncological applications, focusing on three major areas: drug delivery, photothermal therapy, and theranostic approaches. Cellulose nanocrystals, cellulose nanofibrils, and bacterial nanocellulose are nanocellulose materials with high surface area, tunable surface chemistry, and biodegradability and therefore are excellent targets in targeted cancer therapeutics. We address the latest developments in strategies of nanocellulose modification, loading mechanisms for anticancer agents, and their efficacy in preclinical trials. Moreover, we emphasize the combination of nanocellulose with photothermal agents and image modalities to apply in therapeutic and diagnostic purposes. Current challenges and future perspectives for clinical translation are discussed as well. Kaleido Research Publications LLC 2026-05-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/37 10.65477/tc4fyp19 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 5, May 2026; 24-30 3108-2599 eng https://ijerams.org/index.php/files/article/view/37/28 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/38 2026-07-04T05:35:22Z files:ART

Molecular Profiling of Gallbladder Cancer: Comparative Genomic Analysis and Therapeutic Targets in the Chilean Population Miss. Bindu Gajanan Gallbladder Cancer, Genomic Profiling, TP53 Mutations, Precision Oncology, Somatic Variants. Introduction: Gallbladder cancer (GBC) is a highly aggressive malignancy with one of the highest incidence rates reported in Chile. Despite its clinical impact, molecular characterization of GBC in Latin American populations remains limited, and the absence of effective targeted therapies underscores the urgent need for new therapeutic strategies. Methods: We collected 118 tumor samples, of which 56 passed sequencing quality control using the Oncomine™ Comprehensive Assay v1. Somatic variants were identified with ANNOVAR and Cancer Genome Interpreter, and ancestry was inferred using ADMIXTURE and PCA with ancestry-informative markers. Comparative analyses were performed with Japanese, Singaporean, and U.S. cohorts. Results: A total of 535 somatic mutations were detected in 43 genes, with TP53 (30%), TSC2 (29%), and NOTCH1 (27%) being the most frequently mutated. We identified 121 clinically actionable variants in ATM, BRCA1/2, EGFR, ERBB2, and other genes. Exploratory analysis suggested an association between higher Mapuche ancestry and TP53 mutations. Comparative analyses revealed distinct mutational patterns in the Chilean cohort relative to Asian and U.S. datasets. Conclusion: This ancestry-informed genomic analysis provides the first comprehensive landscape of Chilean GBC, identifying actionable alterations with potential therapeutic relevance and supporting the development of population-specific precision oncology strategies. Kaleido Research Publications LLC 2026-05-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/38 10.65477/hh8ws083 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 5, May 2026; 31-46 3108-2599 eng https://ijerams.org/index.php/files/article/view/38/29 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/39 2026-07-03T07:40:04Z files:ART

Modern Nanotechnology Approaches for Drug Delivery: Recent Progress and Emerging Opportunities Miss. Bindu Gajanan Nanoparticle-based drug delivery, Lipid-based nanocarriers, Polymeric nanoparticles, Targeted nanomedicine, Stimuli-responsive nanocarriers, Blood-brain barrier delivery, FDA-approved nanodrugs. Nanotechnology has emerged as a transformative force in the field of drug delivery, offering innovative solutions to longstanding challenges, including poor bioavailability, off-target effects, and multidrug resistance. By enabling the design of nanoscale carriers that can encapsulate, protect, and precisely release therapeutic agents, this technology has the potential to revolutionise how drugs are administered and function within the human body. This review explores the diverse array of nanocarriers currently under investigation or in clinical use, including liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles, and inorganic nanostructures such as gold nanoparticles and quantum dots. Each of these platforms possesses unique physicochemical properties that can be tailored for specific therapeutic applications, ranging from oncology and infectious diseases to neurological and autoimmune disorders. Recent breakthroughs in targeted drug delivery, including stimuli-responsive nanocarriers and surface functionalisation with ligands for active targeting, have significantly enhanced the precision and efficacy of treatments. Several nanotechnology-based formulations have advanced to clinical trials or received regulatory approval, underscoring the growin clinical relevance of this field. Despite these advances, key challenges remain, including issues of large-scale manufacturing, longterm biocompatibility, and regulatory hurdles. Addressing these barriers is crucial for the broader translation of nanotechnologybased drug delivery systems from the bench to the bedside. Future directions point toward the integration of artificial intelligence and personalised medicine with nanotechnology to create more innovative, adaptive delivery platforms tailored to individual patient profiles. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/39 10.65477/kbx80963 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 11-21 3108-2599 eng https://ijerams.org/index.php/files/article/view/39/30 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/40 2026-07-03T07:42:27Z files:ART

The Rise of Predictive Oncology: Artificial Intelligence, Digital Biomarkers, and Personalized Cancer Care Mr. Vikas Yadav Predictive oncology; artificial intelligence; digital biomarkers; precision oncology; multimodal learning; risk modeling; precision therapy. Cancer continues to represent one of the most significant global health challenges due to its molecular heterogeneity, dynamic evolution, late-stage diagnosis, and variability in therapeutic response. Conventional oncology approaches have historically relied on population-based treatment paradigms, imaging interpretation, histopathological examination, and isolated molecular biomarkers, which frequently fail to capture the biological complexity underlying tumor initiation, progression, metastasis, and therapeutic resistance. The emergence of predictive oncology has introduced a transformative computational framework that integrates artificial intelligence (AI), digital biomarkers, multi-omics analytics, radiogenomics, and real-time clinical data to enable earlier cancer detection, individualized risk prediction, and precision therapeutic intervention. Predictive oncology combines machine learning, deep learning, transformer-based architectures, and multimodal foundation models to analyze highly heterogeneous datasets generated from radiology, histopathology, genomics, transcriptomics, proteomics, liquid biopsy profiling, wearable devices, and electronic health records. These computational systems are increasingly capable of identifying latent disease signatures, forecasting tumor behavior, predicting treatment response, and optimizing personalized clinical decision-making. Recent advances in AI-driven oncology have demonstrated remarkable improvements in cancer screening, recurrence prediction, survival estimation, digital pathology, immunotherapy response assessment, and adaptive therapeutic planning. Convolutional neural networks and transformer architectures have enabled automated interpretation of medical imaging and histopathological slides with performance approaching expert-level diagnostic accuracy. Simultaneously, multimodal AI systems integrating molecular and clinical datasets have facilitated the development of predictive biomarkers capable of guiding precision medicine strategies across multiple cancer types. Despite these advancements, substantial challenges remain regarding model interpretability, algorithmic bias, data standardization, privacy preservation, regulatory approval, and large-scale clinical implementation. This review comprehensively discusses the rise of predictive oncology and the evolving role of artificial intelligence in early cancer detection, digital biomarker discovery, risk modeling, and precision therapy. The article further highlights emerging computational architectures, multimodal learning systems, explainable AI frameworks, federated oncology networks, and future translational directions that may redefine the next generation of precision oncology. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/40 10.65477/q13zg515 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 22-31 3108-2599 eng https://ijerams.org/index.php/files/article/view/40/31 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/43 2026-07-03T07:48:30Z files:ART

Radiologic Growth Patterns and Diagnostic Accuracy of Biopsy-Proven Pulmonary Tumors in a Low-Dose CT Screening Program Dr. Vishal Rastogi lung cancer screening, neoplasm progression, volume doubling time, image-guided biopsy, false-negative. Introduction: Low-dose computed tomography (LDCT) reduces lung cancer mortality but may lead to increased resection of benign tumors. We aimed to characterize growth patterns, pathologic diagnoses, diagnostic timelines, and CT-guided biopsy accuracy in screening-detected lung tumors. Methods: We retrospectively analyzed LDCT screening data from 6,997 participants at Taipei Tzu Chi Hospital (2013-2018) with follow-up through 2023. Clinical, radiologic, and pathological features of biopsied lung tumors were evaluated. Volume doubling time (VDT) was calculated for progressive lesions. Results: Among the 128 patients who underwent biopsy, 84 were diagnosed with lung cancer, resulting in a detection rate of 1.2%. Of these, 86% were stage 0-I, and 95% were adenocarcinomas. Of the 157 biopsied tumors, 34% were benign. The most common benign pathological finding was fibrosis, followed by anthracosis. Lobulation and subsolid attenuation were significantly associated with malignancy. Time to diagnosis did not differ significantly between benign and malignant tumors (HR = 1.1, p = 0.53). Notably, 7% of benign tumors showed interval growth and 13% were detected de novo. Benign tumors exhibited a faster growth rate (3.8 vs. 1.5 mm/year, p = 0.28) and shorter VDT (347 vs. 565 days, p = 0.14) than malignant tumors. CT-guided biopsy had a 23% false-negative rate and a 78% negative predictive value. Conclusion: Benign tumors with interval growth or de novo presentation remain a diagnostic challenge. VDT alone is insufficient for distinguishing benign from malignant tumors. Given the substantial false-negative rate of CT-guided biopsy in small lung tumors, integrating radiologic features with clinical context is essential for guiding subsequent surveillance strategies. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/43 10.65477/yq413k58 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 32-42 3108-2599 eng https://ijerams.org/index.php/files/article/view/43/33 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/44 2026-07-06T05:29:09Z files:ART

Artificial Intelligence–Integrated Cancer Ecosystems: Multimodal Digital Biomarkers for Precision, Personalized, and Preventive Oncology Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Miss. Bindu Gajanan Dr. Vishal Rastogi Artificial intelligence; precision oncology; digital biomarkers; radiomics; pathomics; genomics; multimodal learning; foundation models; transformer architectures; explainable AI; federated learning; digital twins; computational pathology; preventive oncology. Artificial intelligence (AI) is reshaping precision oncology by integrating multimodal digital biomarkers—including radiomics, pathomics, genomics, transcriptomics, and liquid biopsy—into unified computational ecosystems that support accurate cancer diagnosis, prognostic stratification, treatment selection, and longitudinal disease monitoring. Recent advances in transformer-based deep learning, vision-language models, self-supervised learning, and foundation models have enabled AI systems to achieve diagnostic performance approaching that of expert radiologists and pathologists while uncovering complex biological patterns beyond conventional analytical methods. Multimodal data fusion further enhances early cancer detection through the simultaneous interpretation of imaging phenotypes, molecular alterations, histopathological features, and clinical variables, facilitating personalized therapeutic decision-making and dynamic digital twin–based disease modeling. Despite these advances, widespread clinical implementation remains constrained by challenges related to model interpretability, algorithmic bias, data privacy, regulatory approval, interoperability, and integration into existing clinical workflows. This review provides a comprehensive overview of AI-integrated cancer ecosystems, highlighting recent developments in deep learning architectures, multimodal digital biomarkers, explainable AI, federated learning, and foundation models. We examine their emerging clinical applications in cancer screening, diagnosis, prognosis, treatment optimization, and response monitoring while discussing ethical, technical, and regulatory considerations for responsible implementation. Current evidence indicates that AI-enabled radiomics and computational pathology are approaching routine clinical deployment as companion diagnostic technologies, whereas privacy-preserving federated learning and multimodal foundation models are expected to accelerate large-scale validation across diverse healthcare systems. Collectively, AI-integrated cancer ecosystems represent a transformative paradigm for precision and preventive oncology, with the potential to improve diagnostic accuracy, personalize cancer management, expand equitable access to advanced analytics, and ultimately enhance patient outcomes worldwide. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/44 10.65477/xqkj4106 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 43-58 3108-2599 eng https://ijerams.org/index.php/files/article/view/44/34 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/45 2026-07-06T05:37:59Z files:ART

Artificial Intelligence, Digital Biomarkers, and Precision Oncology: Emerging Technologies for Early Detection and Personalized Intervention Dr. Rakesh Sharma Dr. Swati Joshi Mr. Vikas Yadav Mr. Shatrughna U. Nagrik Miss. Bindu Gajanan Dr. Vishal Rastogi Artificial intelligence; digital biomarkers; precision oncology; liquid biopsy; radiomics; computational pathology; multi-omics; deep learning; foundation models; explainable AI; federated learning; digital twins. Cancer remains a leading cause of global morbidity and mortality, highlighting the urgent need for earlier diagnosis, accurate risk stratification, and individualized therapeutic strategies. Advances in artificial intelligence (AI) and digital biomarkers have transformed oncology by enabling the integration of molecular, imaging, and clinical data into comprehensive computational frameworks for precision cancer care. Digital biomarkers—including circulating tumor DNA (ctDNA), circulating tumor cells, radiomic signatures, computational pathology, wearable sensor data, and multi-omics profiles—provide dynamic and minimally invasive measures of tumor biology throughout the disease continuum. Simultaneously, deep learning, transformer architectures, foundation models, and multimodal AI have substantially improved the extraction of clinically meaningful information from heterogeneous biomedical datasets. Recent studies demonstrate that AI-assisted liquid biopsy, radiomics, and computational pathology significantly enhance early cancer detection, prognostic assessment, treatment selection, and longitudinal monitoring compared with conventional diagnostic approaches. Emerging technologies such as federated learning, explainable AI, digital twins, and autonomous clinical decision-support systems further facilitate secure, interpretable, and personalized oncology. Nevertheless, widespread clinical implementation remains limited by challenges related to data heterogeneity, algorithmic bias, model interpretability, regulatory approval, interoperability, and prospective clinical validation. This review provides a comprehensive overview of AI-enabled digital biomarkers across the oncology continuum, discusses recent advances in multimodal data integration and computational intelligence, and examines future directions toward adaptive, patient-centered precision oncology. Continued collaboration among clinicians, computational scientists, regulatory agencies, and healthcare systems will be essential to translate these technologies into routine clinical practice while ensuring safety, equity, and clinical effectiveness. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/45 10.65477/jy34zt06 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 59-68 3108-2599 eng https://ijerams.org/index.php/files/article/view/45/35 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/46 2026-07-06T06:04:51Z files:ART

Artificial Intelligence–Driven Digital Twins in Precision Oncology: Transforming Personalized Cancer Diagnosis, Treatment, and Survivorship Mr. Rohan Deshmukh Dr. Nisha Verma Dr. Aarav Sharma Dr. Meera Kulkarni Digital twins, Precision oncology, Artificial intelligence, Computational oncology, Multimodal learning, Radiogenomics, Personalized medicine, Clinical decision support, Machine learning, Cancer informatics. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, precision medicine, and targeted therapeutics. The extraordinary biological heterogeneity observed among tumors, coupled with dynamic interactions between malignant cells, the immune system, and the tumor microenvironment, continues to challenge conventional treatment strategies. Recent developments in artificial intelligence (AI), computational modeling, and multimodal biomedical data integration have introduced the concept of digital twins as a promising framework for personalized oncology. A digital twin is a continuously updated virtual representation of an individual patient that integrates clinical, radiological, pathological, genomic, molecular, physiological, and longitudinal health data to simulate disease progression and therapeutic response. Unlike traditional predictive models that focus on isolated datasets, digital twins enable dynamic patientspecific modeling capable of supporting diagnosis, prognostic prediction, treatment optimization, toxicity assessment, and survivorship management. Advances in machine learning, deep learning, foundation models, graph neural networks, reinforcement learning, and generative AI have significantly enhanced the development of oncology digital twins by enabling real-time integration of heterogeneous biomedical information. These computational systems have demonstrated growing potential across tumor detection, radiogenomics, immunotherapy prediction, adaptive radiation therapy, surgical planning, clinical trial optimization, and precision drug development. Nevertheless, several challenges remain regarding data standardization, interoperability, computational complexity, ethical governance, model transparency, regulatory validation, and large-scale clinical implementation. This review provides a comprehensive overview of digital twin technologies in oncology, highlighting their computational foundations, current clinical applications, emerging innovations, and future role in advancing personalized cancer care.[1] Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/46 10.65477/yzw14p38 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 69-77 3108-2599 eng https://ijerams.org/index.php/files/article/view/46/36 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/47 2026-07-06T07:36:22Z files:ART

Artificial Intelligence in Cancer Diagnosis: Current Applications and Future Perspectives Dr. Karthik Reddy Mrs. Priya Nair Dr. Sandeep Patil Miss. Aditi Joshi Dr. Vivek Menon Artificial intelligence, Cancer diagnosis, Deep learning, Machine learning, Digital pathology, Medical imaging, Precision oncology, Radiomics, Histopathology, Clinical decision support. Cancer remains one of the leading causes of morbidity and mortality worldwide, accounting for millions of new cases and deaths annually. Despite substantial progress in molecular biology, imaging technologies, and targeted therapeutics, early and accurate cancer diagnosis continues to represent a major clinical challenge due to tumor heterogeneity, overlapping pathological characteristics, and variations in disease progression. Delayed diagnosis often results in advanced-stage disease, reduced treatment efficacy, and poorer patient outcomes. Recent advances in artificial intelligence (AI) have transformed the diagnostic landscape by enabling automated analysis of complex biomedical data with unprecedented speed and accuracy. Machine learning, deep learning, natural language processing, computer vision, and multimodal AI systems are increasingly being integrated into oncology to support cancer detection, classification, prognostic assessment, and clinical decision-making. These technologies facilitate the interpretation of radiological images, digital pathology slides, genomic profiles, liquid biopsy data, and electronic health records, providing comprehensive diagnostic insights beyond conventional analytical approaches. AI-assisted diagnostic systems have demonstrated remarkable performance across multiple malignancies, including breast, lung, colorectal, prostate, liver, brain, and skin cancers, while also improving workflow efficiency and reducing interobserver variability. Furthermore, emerging multimodal models capable of integrating imaging, molecular, and clinical information are paving the way toward highly personalized cancer diagnosis. Nevertheless, important challenges remain, including algorithmic bias, limited interpretability, data privacy concerns, regulatory approval, and clinical validation across diverse patient populations. This review discusses the current applications of artificial intelligence in cancer diagnosis, highlights recent technological advances, examines existing limitations, and explores future perspectives for AI-driven precision oncology. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/47 10.65477/3fbdw205 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 78-85 3108-2599 eng https://ijerams.org/index.php/files/article/view/47/37 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/48 2026-07-06T05:55:54Z files:ART

Machine Learning in Oncology: Transforming Cancer Detection and Treatment Dr. Ananya Iyer Mr. Harish Kumar Dr. Pooja Singh Dr. Arjun Malhotra Mrs. Kavya Rao Dr. Deepak Mishra Machine learning, Oncology, Cancer detection, Precision oncology, Artificial intelligence, Deep learning, Medical imaging, Digital pathology, Predictive analytics, Personalized medicine. Cancer continues to represent one of the greatest global health challenges, accounting for millions of new diagnoses and deaths annually. The biological complexity of malignant diseases, characterized by extensive genomic heterogeneity, dynamic tumor evolution, immune interactions, and variable therapeutic responses, presents substantial challenges for clinicians seeking to deliver individualized patient care. Conventional oncology relies heavily on histopathology, radiological imaging, molecular diagnostics, and clinical expertise; however, these approaches often face limitations related to diagnostic variability, delayed interpretation, and the inability to simultaneously analyze large-scale multidimensional datasets. Machine learning (ML), a major branch of artificial intelligence, has emerged as a transformative technology capable of overcoming many of these limitations by identifying complex patterns within biomedical data that are beyond human analytical capacity. Supervised, unsupervised, semi-supervised, and reinforcement learning algorithms have demonstrated remarkable capabilities across the entire cancer care continuum, including early detection, tumor classification, biomarker discovery, prognostic prediction, treatment selection, response assessment, and survival estimation. Recent advances in deep learning, ensemble learning, transfer learning, explainable artificial intelligence, and multimodal data integration have further expanded the clinical applicability of ML in oncology. Machine learning systems now integrate histopathological images, radiological scans, genomic and transcriptomic profiles, proteomic signatures, electronic health records, and real-world clinical data to support precision oncology and personalized treatment planning. Despite these advances, challenges including algorithmic bias, data privacy, interpretability, regulatory validation, and clinical implementation remain significant barriers to widespread adoption. This review discusses the evolution, methodologies, clinical applications, advantages, limitations, ethical considerations, and future perspectives of machine learning in transforming modern cancer detection and treatment. Kaleido Research Publications LLC 2026-06-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/48 10.65477/5sqnvk10 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 6, June 2026; 86-94 3108-2599 eng https://ijerams.org/index.php/files/article/view/48/38 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/53 2026-07-07T05:10:04Z files:ART

Longitudinal Growth Patterns and Diagnostic Challenges of Biopsy-Confirmed Pulmonary Tumors Detected on Low-Dose CT: A 10-Year Analysis Dr. Vikram Sinha Dr. Ananya Rao Dr. Karan Malhotra Dr. Priya Menon Low-dose CT, pulmonary tumors, lung cancer screening, tumor growth, volume doubling time, pulmonary nodules, biopsy-confirmed tumors, diagnostic accuracy, longitudinal imaging, early lung cancer. Low-dose computed tomography (LDCT) has transformed lung cancer screening by enabling detection of small pulmonary tumors before symptoms develop. However, the increased identification of indeterminate pulmonary nodules has created a major diagnostic challenge: not all growing lesions are malignant, and not all malignant tumors demonstrate rapid or predictable growth. Longitudinal assessment of pulmonary tumors therefore requires careful interpretation of imaging behavior, clinical context, and pathological confirmation. This review, titled Longitudinal Growth Patterns and Diagnostic Challenges of Biopsy-Confirmed Pulmonary Tumors Detected on Low-Dose CT: A 10-Year Analysis, examines how tumor biology, radiologic morphology, and follow-up imaging interact in the evaluation of biopsy-confirmed pulmonary lesions. Part 1 focuses on the clinical importance of LDCT, the biological basis of tumor growth, and the limitations of conventional growth-based malignancy assessment. Kaleido Research Publications LLC 2026-07-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/53 10.65477/38dkfh82 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 2, Issue 5, May 2026; 47-54 3108-2599 eng https://ijerams.org/index.php/files/article/view/53/39 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by-nc/4.0/