Why AI Performs Well on Exams but Struggles With Real-World Clinical Judgment

Artificial intelligence (AI) has gained increasing attention in medicine due to its rapidly expanding capabilities in analyzing and synthesizing medical information. While AI demonstrates strong performance in structured examination settings, this success does not necessarily translate into effective real-world clinical judgment. Clinical practice, particularly in high-risk fields such as cardiac surgery, is characterized by uncertainty, incomplete information, and dynamic decision-making. This editorial examines the discrepancy between exam-based performance and clinical reasoning, emphasizing key limitations of current AI systems, including a lack of contextual awareness, susceptibility to hallucinations, limited interpretability, and the absence of accountability. It also highlights the shortcomings of exam-centered validation methods, which may overestimate clinical readiness. Ultimately, AI is best positioned as a supportive tool that augments, rather than replaces, human clinical judgment.