Logic of Discovery and Diagnosis in Medicine provides an interdisciplinary exploration of the interplay between philosophy of science, artificial intelligence (AI), and clinical diagnostic processes. Stemming from a conference held at the University of Pittsburgh, this volume unites contributions from philosophers, physicians, and AI theorists to investigate the foundational and practical aspects of medical reasoning. The discussions center around "problem solving" and "heuristic search" as frameworks for understanding discovery and diagnosis in medicine. By integrating developments in AI, such as the INTERNIST-I program, and philosophical inquiries into logic and reasoning, the book aims to illuminate the complexities and evolving methodologies of medical diagnosis.
Key highlights include Herbert Simon's application of AI-based problem-solving theories to clinical diagnosis, with an emphasis on heuristic methods that optimize decision-making in complex scenarios. The book also delves into the limitations of branching logic and Bayesian probability models, advocating for innovative approaches such as causal linkages and adaptive classification systems. Through critiques and discussions of diagnostic tools like INTERNIST-I and its successor, CADUCEUS, contributors explore the challenges of modeling human reasoning and integrating pathophysiological data into AI systems. Ultimately, this volume is both a theoretical and practical resource for advancing the integration of AI in medicine while reflecting on the broader implications for scientific discovery and diagnostic reasoning.
This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1985.
