基于大语言模型的实时复合诊断医疗AI界面与医生在模拟患者常见内科病例中的比较
Comparisons between a Large Language Model-based Real-Time Compound Diagnostic Medical AI Interface and Physicians for Common Internal Medicine Cases using Simulated Patients
AI总结：
本研究开发了基于大语言模型的实时复合诊断医疗AI界面，通过非随机临床试验与医生比较，发现其在常见内科病例中诊断准确率更高（80% vs 50-70%）、时间更短（缩短44.6%）、成本更低（降低98.1%），患者满意度相当，显示出辅助初级保健的潜力。
摘要：
目的：开发一种基于大语言模型（LLM）的实时复合诊断医疗AI界面，并进行一项临床试验，将该界面与医生在美国医学执照考试（USMLE）第二步临床技能（CS）风格考试中的常见内科病例表现进行比较。方法：于2024年8月20日进行了一项非随机临床试验。我们招募了一名全科医生、两名内科住院医师（二年级和三年级）以及五名模拟患者。临床病例改编自USMLE第二步CS风格考试。我们基于真实患者开发了10个代表性内科病例，并纳入了初次诊断评估时可获得的信息。主要结局是首次鉴别诊断的准确性。可重复性根据一致比例进行评估。结果：医生的首次鉴别诊断准确率在50%至70%之间，而实时复合诊断医疗AI界面达到了80%的准确率。首次鉴别诊断的一致比例为0.7。医生首次和第二次鉴别诊断的准确率在70%至90%之间，而AI界面的准确率达到了100%。AI界面的平均时间（557秒）比医生（1006秒）短44.6%。AI界面的成本（0.08美元）也比医生的平均成本（4.2美元）降低了98.1%。患者对医生护理的满意度评分在4.2至4.3之间，对AI界面为3.9。结论：基于LLM的实时复合诊断医疗AI界面在诊断准确性和患者满意度方面与医生相当，同时所需时间和成本更低。这些发现表明，AI界面可能具有协助常见内科病例初级保健咨询的潜力。
英文摘要：
Objective To develop an LLM based realtime compound diagnostic medical AI interface and performed a clinical trial comparing this interface and physicians for common internal medicine cases based on the United States Medical License Exam (USMLE) Step 2 Clinical Skill (CS) style exams. Methods A nonrandomized clinical trial was conducted on August 20, 2024. We recruited one general physician, two internal medicine residents (2nd and 3rd year), and five simulated patients. The clinical vignettes were adapted from the USMLE Step 2 CS style exams. We developed 10 representative internal medicine cases based on actual patients and included information available on initial diagnostic evaluation. Primary outcome was the accuracy of the first differential diagnosis. Repeatability was evaluated based on the proportion of agreement. Results The accuracy of the physicians' first differential diagnosis ranged from 50% to 70%, whereas the realtime compound diagnostic medical AI interface achieved an accuracy of 80%. The proportion of agreement for the first differential diagnosis was 0.7. The accuracy of the first and second differential diagnoses ranged from 70% to 90% for physicians, whereas the AI interface achieved an accuracy rate of 100%. The average time for the AI interface (557 sec) was 44.6% shorter than that of the physicians (1006 sec). The AI interface ($0.08) also reduced costs by 98.1% compared to the physicians' average ($4.2). Patient satisfaction scores ranged from 4.2 to 4.3 for care by physicians and were 3.9 for the AI interface Conclusion An LLM based realtime compound diagnostic medical AI interface demonstrated diagnostic accuracy and patient satisfaction comparable to those of a physician, while requiring less time and lower costs. These findings suggest that AI interfaces may have the potential to assist primary care consultations for common internal medicine cases.
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