Abstract

Cancer remains one of the most pressing global health challenges, with rising incidence and mortality rates underscoring the urgent need for timely and accurate diagnostics.

Traditional approaches such as histopathology, biopsy, and imaging interpretation are limited by subjectivity, time constraints, and human error. This study presents a comprehensive case analysis of Artificial Intelligence (AI) in cancer diagnostics, synthesizing evidence from clinical trials, peer-reviewed studies, and technology applications. AI's integration into oncology spans three major domains: imaging-based detection, predictive modelling in genomics, and Clinical Decision Support Systems (CDSS). Findings reveal that AI-driven tools, particularly deep learning models, outperform conventional diagnostic methods in sensitivity, specificity, and scalability, while also facilitating personalized treatment strategies through predictive genomics.

Furthermore, AI-enabled CDSS demonstrate significant potential in optimizing clinical workflows and im proving evidence-based decision-making. However, the study also identifies critical barriers to adoption, in cluding algorithmic bias, limited data representativeness, regulatory uncertainty, and global disparities in access, especially between high-income and low- and middle-income countries. Ethical concerns surrounding data privacy, interpretability, and accountability further complicate clinical integration. Despite these chal lenges, opportunities exist through open-access datasets, federated learning, human-AI collaboration, and policy harmonization. Overall, the study highlights AI's transformative potential in reshaping cancer diag nostics, while emphasizing the need for equitable adoption strategies, robust governance frameworks, and interdisciplinary collaboration to ensure its responsible and inclusive application in global healthcare.

DOI: doi.org/10.63721/25JPAIR0112

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