The logic of medical reasoning: toward an integrated inductive, deductive, and abductive approach to clinical practices

Abstract

INTRODUCTION: This study explored the logical underpinnings of medical reasoning, focusing on the integration of abduction, deduction, and induction within clinical decision-making. It aimed to highlight the role of abduction in generating hypotheses, particularly in complex cases that defy standard protocols, and to examine the synergy between human expertise and AI-assisted tools in enhancing diagnostic accuracy. METHODS: The research employed a qualitative approach, analyzing philosophical theories and integrating them with clinical case studies. The study examined the interplay of logical processes in medical diagnostics and the application of abduction in rare and novel cases. Additionally, the potential of AI-assisted tools to support clinical reasoning and reduce diagnostic noise was explored. RESULTS: Abduction was identified as a critical yet often underappreciated element in medical reasoning essential for hypothesis generation. Deduction refines hypotheses against established medical knowledge, while induction validates decisions through empirical data. AI-assisted tools were found to enhance diagnostic accuracy by reducing noise, although they did not engage in the musement or genuine abductions that characterize human clinical reasoning. DISCUSSION: The study concluded that a triadic approach to clinical reasoning, incorporating abduction, deduction, and induction, is essential for effective medical diagnostics. In particular, abduction plays a pivotal role in navigating the complexities of clinical decision-making. The integration of AI tools can reduce noise and improve diagnostic processes, but the essential human elements of insight and judgment remain irreplaceable in patient care.

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