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SymptomAI, a set of conversational AI agents, was deployed within the Fitbit app to conduct end-to-end patient interviews and differential diagnosis on a cohort of 13,917 participants. Results showed that SymptomAI's diagnoses were significantly more accurate than those of independent clinicians when given the same dialogue (OR = 2.47, p<0.001), and that agentic strategies eliciting additional symptom information outperformed user-guided conversations (p<0.001). The study also identified strong associations between acute infections and physiological shifts using wearable metrics, demonstrating the potential of AI-driven symptom assessment in real-world settings.
LLMs beat doctors at everyday symptom diagnosis, but only when they proactively interview patients instead of passively answering questions.
Language models excel at diagnostic assessments on currated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. However, existing studies focus on complex scenarios with rich context making it difficult to draw conclusions about how these systems perform for patients reporting symptoms in everyday life. We deployed SymptomAI, a set of conversational AI agents for end-to-end patient interviewing and differential diagnosis (DDx), via the Fitbit app in a study that randomized participants (N=13,917) to interact with five AI agents. This corpus captures diverse communication and a realistic distribution of illnesses from a real world population. A subset of 1,228 participants reported a clinician-provided diagnosis, and 517 of these were further evaluated by a panel of clinicians during over 250 hours of annotation. SymptomAI DDx were significantly more accurate (OR = 2.47, p<0.001) than those from independent clinicians given the same dialogue in a blinded randomized comparison. Moreover, agentic strategies which conduct a dedicated symptom interview that elicit additional symptom information before providing a diagnosis, perform substantially better than baseline, user-guided conversations (p<0.001). An auxiliary analysis on 1,509 conversations from a general US population panel validated that these results generalize beyond wearable device users. We used SymptomAI diagnoses as labels for all 13,917 participants to analyze over 500,000 days of wearable metrics across nearly 400 unique conditions. We identified strong associations between acute infections and physiological shifts (e.g., OR>7 for influenza). While limited by self-reported ground truth, these results demonstrate the benefits of a dedicated and complete symptom interview compared to a user-guided symptom discussion, which is the default of most consumer LLMs.
