Exploring the feasibility of conversational diagnostic AI in a real-world clinical study
The study evaluated Google’s conversational medical AI, AMIE, in a real-world primary care workflow rather than simulated cases. In a prospective, IRB-approved study at Beth Israel Deaconess Medical Center, AMIE conducted supervised pre-visit history-taking with 100 patients. Results suggested that supervised deployment was feasible and conversationally safe, while AMIE’s diagnostic and management-plan quality was broadly comparable to that of primary care physicians, with physicians performing better on practicality and cost effectiveness. ## Study Design and Clinical Workflow - Patients with new, non-emergency, episodic complaints used AMIE through a secure web link before an in-person or telehealth appointment. - A physician supervised each AI-patient interaction through live video and screen-sharing. - AMIE produced a transcript and summary for the patient’s primary care physician. - Independent clinical evaluators assessed: - The quality of the AMIE conversation - AMIE’s differential diagnoses - AMIE’s management plans - Comparable outputs from physicians - The study was prospective, single-center, single-arm, pre-registered, and IRB approved. ## Participants - 100 adults completed the AMIE interaction. - 98 attended their scheduled primary care appointments. - Participants represented varied ages, racial and ethnic groups, health literacy, technology literacy, and prior chatbot experience. - Compared with all 1,452 urgent care visits during the study period, participants tended to be younger, although the sample reflected the broader population’s female and white demographic skew. ## Safety Oversight - Human supervisors could stop an interaction if they observed: - Immediate risk of harm to the patient or others - Significant emotional distress related to the AI interaction - Potential clinical harm - A patient’s explicit request to end the session - No safety stops were required across the study. - The authors interpret this as evidence that supervised AMIE interactions were conversationally safe in this setting. ## Clinical Reasoning Performance - Three independent clinical evaluators reviewed each case using blinded, randomized assessments. - AMIE and physicians showed similar overall quality for: - Differential diagnoses - Management plans - Management-plan appropriateness and safety - Physicians performed better on the practicality and cost effectiveness of management plans. - AMIE’s differential-diagnosis accuracy was reported as high, including cases where the final diagnosis was confirmed through diagnostic testing. ## Patient and Clinician Experience - The study measured trust, perceptions, and acceptance among both patients and clinicians. - Patient trust in AI increased after interacting with AMIE. - Overall findings indicated that the system was well received within the supervised pre-visit workflow. The study supports cautious, supervised testing of conversational diagnostic AI in clinical environments. It does not establish that AMIE can independently replace clinicians; rather, it suggests that pre-visit information gathering may be a practical early use case, provided rigorous oversight, safety protocols, and further evaluation in larger and more diverse settings.
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