# A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

arXiv: https://arxiv.org/abs/2603.08448 · HTML: https://mlsanity.com/paper/2603.08448/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2026.

## Built on by (most cited first, 6 of 6)

- [Towards Conversational Medical AI with Eyes, Ears and a Voice](https://mlsanity.com/paper/2605.09272/) (2026)
- [ResidencyRL: Reinforcement Learning in Simulated Clinical Environments](https://mlsanity.com/paper/2608.07418/) (2026)
- [Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients](https://mlsanity.com/paper/2603.17234/) (2026)
- [Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models](https://mlsanity.com/paper/2604.16980/) (2026)
- [Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support](https://mlsanity.com/paper/2607.28677/) (2026)
- [Towards Expert-level Medical AI for Real-time Video Consultations](https://mlsanity.com/paper/2608.09861/) (2026)

## Abstract (arXiv)

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.