Artificial Intelligence in Cancer Diagnosis: Current Applications and Future Perspectives

Abstract

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.