AI-Driven Diagnostic Assistance in Medical Inquiry: Reinforcement Learning Algorithm Development and Validation

作者：Xuan Zou, Weijie He, Yu Huang, Yi Ouyang, Zhen Zhang, Yu Wu, Yongsheng Wu, Lili Feng, Sheng Wu, Mengqi Yang, Xuyan Chen, Yefeng Zheng, Rui Jiang, Ting Chen · 发表于：Journal of Medical Internet Research · 年份：2024 · DOI：10.2196/54616 · 被引用次数：19 · 研究领域：Clinical Reasoning and Diagnostic Skills、Artificial Intelligence in Healthcare and Education、COVID-19 diagnosis using AI

BACKGROUND: For medical diagnosis, clinicians typically begin with a patient's chief concerns, followed by questions about symptoms and medical history, physical examinations, and requests for necessary auxiliary examinations to gather comprehensive medical information. This complex medical investigation process has yet to be modeled by existing artificial intelligence (AI) methodologies. OBJECTIVE: The aim of this study was to develop an AI-driven medical inquiry assistant for clinical diagnosis that provides inquiry recommendations by simulating clinicians' medical investigating logic via reinforcement learning. METHODS: We compiled multicenter, deidentified outpatient electronic health records from 76 hospitals in Shenzhen, China, spanning the period from July to November 2021. These records consisted of both unstructured textual information and structured laboratory test results. We first performed feature extraction and standardization using natural language processing techniques and then used a reinforcement learning actor-critic framework to explore the rational and effective inquiry logic. To align the inquiry process with actual clinical practice, we segmented the inquiry into 4 stages: inquiring about symptoms and medical history, conducting physical examinations, requesting auxiliary examinations, and terminating the inquiry with a diagnosis. External validation was conducted to validate the inquiry logic of the AI model. RESULTS: This study focused on 2 retrospect...