SymptomAI：迈向日常症状评估的对话式 AI Agent

英文标题：SymptomAI: Towards a conversational AI agent for everyday symptom assessment

SymptomAI 是一项探索性研究，旨在评估对话式 AI Agent 通过远程访谈开展日常症状评估的能力。研究显示，该系统可在真实世界人群样本中实现端到端的鉴别诊断（DDx）评估，并可结合可穿戴设备生物信号分析群体尺度的疾病相关生理特征。研究同时强调，症状评估和鉴别诊断具有时间性与不确定性，所有研究中生成的诊断、标签及疾病关联均为 AI 研究分析结果，不构成临床确诊或正式医疗评估。对开发者和行业而言，这表明对话式 AI 在症状采集与公共健康信号分析方面具有潜力，但仍无法替代包含追问、体格观察、病历信息及医患关系在内的临床诊疗过程。

打开原文相关 3 · 机会 1

中文

局限性

SymptomAI 是一项探索性研究工作，可能代表 AI 症状评估领域的重要研究进展，并展示了其为寻求理解自身症状的公众所能提供的潜力。尽管一项面向人群部署的评估揭示了其通过远程患者访谈进行症状评估的准确性，但与临床医生的评估相比，仍存在一些细微的局限。

首先，鉴别诊断本身是一项具有模糊性的任务，即使已报告的诊断结果也可能随着时间推移而发生变化和发展。症状评估只是某一时间点的快照，反映的是症状在当时的表现。由于我们的部署规模较大，无法控制症状报告的频率和时间。因此，部分参与者可能在更具代表性的症状指标出现之前很早就报告了症状；另一些参与者则可能在多年慢性疾病经验形成的知情背景下，报告了明显的症状指标。未来的研究可聚焦于症状发展特定阶段的特定疾病，例如早发性代谢综合征，或呼吸道感染初期讨论的症状。研究期间生成的所有诊断、标签和疾病关联均由 AI 产生，仅用于研究分析，不构成已确认的临床诊断或正式医疗评估。

其次，在我们的评估中，临床医生审阅的是静态聊天记录，未被赋予自行提出后续问题的主动权。如果由临床医生主导症状访谈，他们可能会凭直觉获取不同的信息。此外，尽管近期研究表明，对话式 AI 系统能够以达到临床医生水平的细节和准确度获取临床数据，但此类系统仍可能遗漏其他信号，例如肢体语言、视觉评估、医疗记录，或在初级医疗场景中患者与医生之间既有的信任关系。

总之，我们介绍了 SymptomAI：一种用于开展真实世界患者访谈和症状评估的研究型对话式 AI Agent。我们通过人群样本中的 DDx 准确率展示了 SymptomAI 端到端的真实世界表现，并说明 SymptomAI 生成的诊断结果如何支持对群体尺度信号的分析，例如通过可穿戴设备生物信号识别生理信号与已报告疾病之间的关联。

English / 原文

Limitations

SymptomAI is an exploratory research effort that could represent a significant research advancement in AI-based symptom assessment and demonstrates the potential it could provide for the general public seeking understanding of their symptoms. While a population deployment evaluation reveals the accuracy of symptom assessment through remote patient interviews, there are nuanced limitations when comparing against clinician’s assessments.

Firstly, differential diagnosis itself is an ambiguous task and even reported diagnoses may change and develop longitudinally. A symptom assessment is a snapshot in time and captures the symptoms as they present in that moment. Due to the scale of our deployment, we were unable to control for frequency and timing of symptom reporting. As a result, some participants may have reported their symptoms well before more representative indicators developed, while others may have reported obvious indicators from an informed context after years of experience with chronic illness. Future work may focus on specific illnesses at specific points during symptom development such as early-onset metabolic syndrome or symptoms discussed at the start of respiratory infections. All diagnoses, labels, and disease associations generated during the study are AI-derived for research analysis only and do not constitute confirmed clinical diagnoses or official medical assessments.

Secondly, in our evaluation the clinicians reviewed static chat transcripts and were not given agency to ask their own follow-up questions. Clinicians may have intuitively sourced different information had they directed the symptom interview. Moreover, while recent research has shown that conversational AI systems can source clinical data with a clinician-level of detail and accuracy, such systems may miss alternative signals like body language, visual assessment, medical records, or in the context of primary care, existing rapport with the patient.

In conclusion, we introduce SymptomAI, an investigational conversational AI agent for conducting real-world patient interviews and symptom assessments. We demonstrate SymptomAI’s end-to-end real-world performance through DDx accuracy on a population sample, and show how SymptomAI diagnoses can enable analysis of population-scale signals like wearable biosignals for identifying associations in physiological signals with reported illness.