迈向以医生为中心的对话式诊断AI监督
Towards physician-centered oversight of conversational diagnostic AI
AI总结：
本文提出g-AMIE多智能体系统，在防护栏内完成病史采集并对个体化医疗建议弃权，将评估异步传达给监督医生，实验表明其接诊质量和决策效率优于NPs/PAs及PCPs，验证了专家异步监督诊断AI的可行性。
摘要：
近期研究已展示对话式AI系统在诊断对话中的潜力。然而，在现实世界中保障患者安全意味着，提供个体化诊断和治疗方案被视为须由持证专业人员执行的受监管活动。此外，医生通常在此类活动中监督其他团队成员，包括执业护士（NPs）或医师助理/助理医师（PAs）。受此启发，我们提出一个框架，用于对Articulate Medical Intelligence Explorer（AMIE）AI系统进行有效的异步监督。我们提出guardrailed-AMIE（g-AMIE），这是一个多智能体系统，在防护栏内执行病史采集，并对个体化医疗建议采取弃权（不执行）。之后，g-AMIE在临床医生驾驶舱界面中将评估结果传达给监督的主治全科医生（PCP）。PCP提供监督并保留对临床决策的责任。这有效地将监督与接诊解耦，因此可以异步进行。在一项随机、设盲的虚拟客观结构化临床考试（OSCE）中，针对带异步监督的文本咨询，我们将g-AMIE与在相同防护栏下的NPs/PAs或一组PCPs进行了比较。在60个场景中，g-AMIE在执行高质量接诊、总结病例以及为监督PCP提出诊断和管理计划以供审查方面均优于两组。这带来了更高质量的综合决策。与先前工作中的独立PCP咨询相比，对g-AMIE的PCP监督在时间上也更高效。虽然我们的研究并未复现现有临床实践，且可能低估了临床医生的能力，但我们的结果表明，异步监督作为一种可行范式具有前景，使诊断AI系统能够在专家人类监督下运行，以提升现实世界的医疗照护。
英文摘要：
Recent work has demonstrated the promise of conversational AI systems for diagnostic dialogue. However, real-world assurance of patient safety means that providing individual diagnoses and treatment plans is considered a regulated activity by licensed professionals. Furthermore, physicians commonly oversee other team members in such activities, including nurse practitioners (NPs) or physician assistants/associates (PAs). Inspired by this, we propose a framework for effective, asynchronous oversight of the Articulate Medical Intelligence Explorer (AMIE) AI system. We propose guardrailed-AMIE (g-AMIE), a multi-agent system that performs history taking within guardrails, abstaining from individualized medical advice. Afterwards, g-AMIE conveys assessments to an overseeing primary care physician (PCP) in a clinician cockpit interface. The PCP provides oversight and retains accountability of the clinical decision. This effectively decouples oversight from intake and can thus happen asynchronously. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) of text consultations with asynchronous oversight, we compared g-AMIE to NPs/PAs or a group of PCPs under the same guardrails. Across 60 scenarios, g-AMIE outperformed both groups in performing high-quality intake, summarizing cases, and proposing diagnoses and management plans for the overseeing PCP to review. This resulted in higher quality composite decisions. PCP oversight of g-AMIE was also more time-efficient than standalone PCP consultations in prior work. While our study does not replicate existing clinical practices and likely underestimates clinicians' capabilities, our results demonstrate the promise of asynchronous oversight as a feasible paradigm for diagnostic AI systems to operate under expert human oversight for enhancing real-world care.
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