Training Diagnostic Specialists to Use Artificial Intelligence Safely: The DISCORD-Dx Study

This randomized study evaluated whether a structured reasoning strategy called DISCORD-Dx could help specialist doctors use artificial intelligence (AI) recommendations more safely during diagnostic decision-making. The strategy was designed to help specialists benefit from correct AI advice while resisting plausible but incorrect AI recommendations. Forty-six consultant specialists in pathology and diagnostic radiology from two hospitals in Bahawalpur, Pakistan, were randomly assigned to receive either DISCORD-Dx training or time-matched conventional AI-literacy training. During assessment, participants first recorded and locked their own diagnosis before seeing a standardized AI recommendation. They then reviewed the AI advice and entered a final diagnosis. The AI recommendations included both correct recommendations and deliberately generated plausible errors that had been independently reviewed by specialists. The main outcome was appropriate reliance on AI, defined as following correct AI advice or resisting erroneous AI advice immediately after training. Other outcomes included harmful switching from a correct diagnosis to an incorrect diagnosis after erroneous AI advice, over-reliance, under-reliance, final diagnostic accuracy, decision time, and appropriate reliance at 8 weeks. No participant interacted with a live AI system, and study responses did not affect patient care.

Inclusion Criteria: * Practicing consultant specialist with current independent diagnostic responsibility. * Specialty in chemical pathology, hematology, microbiology, histopathology, or diagnostic radiology. Exclusion Criteria: * Trainee status. * Absence of independent reporting responsibility. * Direct involvement in constructing or validating the participant's specialty case bank. * Prior access to assessment cases or scoring keys. * Inability to complete the digital assessment.