Artificial Intelligence in Clinical Medicine: A ComprehensiveReview of Diagnostic Algorithms, Therapeutic Decision-Making,and Future Frontiers

Abstract

Artificial intelligence (AI) has emerged as a transformative force in clinical medicine, reshaping how diseases are diagnosed, treatments are selected, and patient care is delivered. This comprehensive review examines the current state and future trajectory of AI applications across the clinical spectrum, from diagnostic algorithms that match or exceed human expert performance in medical imaging and pathology, to therapeutic decision support systems that optimize treatment selection, drug dosing, and surgical planning. We synthesize evidence from recent studies demonstrating that deep learning models have achieved diagnostic accuracy comparable to or exceeding that of board-certified specialists across multiple specialties, including radiology (AUC 0.92–0.98), pathology (AUC 0.90–0.97), dermatology (AUC 0.91–0.96), and ophthalmology (AUC 0.94–0.99). In therapeutics, AI-Driven dosing algorithms have reduced adverse drug events by 30–45% in prospective studies, while machine learning-based treatment response prediction has demonstrated AUC values of 0.78–0.88 across oncology, cardiology, and psychiatry. However, significant challenges persist, including the gap between algorithmic performance and clinical outcome improvement, the “black box” problem of model interpretability, algorithmic bias, and the paucity of prospective validation studies. We critically examine these barriers and outline future frontiers, including multimodal AI integration, foundation models, causal inference, and Human-AI collaboration. We conclude that while AI has demonstrated remarkable capabilities in controlled research settings, successful clinical translation requires rigorous prospective validation, workflow integration, and a commitment to addressing ethical and practical challenges.