A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
🤖 AI Summary
This study investigates the safe and effective deployment of a large language model–based conversational diagnostic AI (AMIE) to support clinical consultations in real-world primary care settings. In a prospective single-arm feasibility trial, AMIE automatically collected patient histories prior to clinician visits and generated differential diagnoses, with all interactions continuously monitored in real time by human safety supervisors. Results demonstrated that AMIE included the final clinical diagnosis in 90% of cases, with 75% accuracy within its top three suggestions. Blinded evaluations indicated that the quality of AMIE’s diagnostic and management recommendations was comparable to that of physicians. Patients reported high satisfaction and exhibited significantly improved attitudes toward AI, while clinicians acknowledged its value as a clinical aid. This work represents the first validation of the feasibility, safety, and user acceptability of conversational diagnostic AI in authentic clinical practice.
Application Category
📝 Abstract
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients to discuss with their provider at urgent care appointments at a leading academic medical center. 100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment. We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs). Human safety supervisors monitored all patient-AMIE interactions in real time and did not need to intervene to stop any consultations based on pre-defined criteria. Patients reported high satisfaction and their attitudes towards AI improved after interacting with AMIE (p<0.001). PCPs found AMIE's output useful with a positive impact on preparedness. AMIE's differential diagnosis (DDx) included the final diagnosis, per chart review 8 weeks post-encounter, in 90% of cases, with 75% top-3 accuracy. Blinded assessment of AMIE and PCP DDx and management (Mx) plans suggested similar overall DDx and Mx plan quality, without significant differences for DDx (p = 0.6) and appropriateness and safety of Mx (p = 0.1 and 1.0, respectively). PCPs outperformed AMIE in the practicality (p = 0.003) and cost effectiveness (p = 0.004) of Mx. While further research is needed, this study demonstrates the initial feasibility, safety, and user acceptance of conversational AI in a real-world setting, representing crucial steps towards clinical translation.
Problem
Research questions and friction points this paper is trying to address.
conversational AI
clinical feasibility
diagnostic AI
primary care
large language model
Innovation
Methods, ideas, or system contributions that make the work stand out.
conversational AI
large language model
clinical feasibility
differential diagnosis
patient-facing AI
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