A Bayesian framework for LLM-enhanced history-taking in recurrent medical conditions to improve treatment outcomes: An empirical evaluation

This paper introduces a novel Bayesian framework integrating Large Language Models (LLMs) into medical history-taking specifically for recurrent medical conditions, aiming to overcome limitations of traditional methods and improve treatment outcomes. Unlike existing AI applications in healthcare that primarily focus on diagnostic classification or prediction in acute settings, our approach emphasizes iterative diagnostic refinement and explainable AI within a Bayesian probabilistic framework, offering a unique strategy for personalized management of recurrent conditions. We empirically evaluate this framework by analyzing the current limitations in clinical history-taking practices and leveraging the capabilities of modern LLMs to generate more comprehensive patient narratives, improve pattern recognition across longitudinal data, and enhance the identification of subtle disease precursors. Our review of preliminary implementations suggests that LLM integration into clinical workflows may reduce diagnostic errors, improve treatment adherence, and enable more personalized therapeutic interventions. However, significant challenges remain regarding clinical validation, privacy concerns, and integration with existing healthcare systems. We conclude that LLMs represent a promising tool for treating recurrent medical conditions when deployed as physician augmentation rather than replacement technologies. • Proposes a novel hybrid framework integrating a Large Language Model (LLM) with a Bayesian engine to enhance medical history-taking for complex recurrent conditions. • Demonstrates superior diagnostic accuracy through a retrospective real-world validation on a complex chronic back pain case, outperforming stratified conventional approaches (junior and senior clinicians). • Introduces a Standardized Curation Protocol to reduce clinician subjectivity, ensuring consistent and evidence-based integration of LLM-generated insights. • The framework enables a shift from symptom management to root-cause analysis by quantitatively identifying and weighting multifactorial drivers, including often-missed psychosocial factors.