A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908887404249088 |
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| author | Brodeur, Peter Koshy, Jacob M. Palepu, Anil Saab, Khaled Homiar, Ava Ruparel, Roma Wu, Charles Tanno, Ryutaro Xu, Joseph Wang, Amy Stutz, David Weng, Wei-Hung Ferrera, Hannah M. Barrett, David Crowley, Lindsey Lee, Jihyeon Rittner, Spencer E. Wulczyn, Ellery Zhang, Selena K. Vedadi, Elahe Kohn, Christine G. Kulkarni, Kavita Kadiyala, Vinay Mahdavi, Sara Du, Wendy Williams, Jessica M. Feinbloom, David Wong, Renee Tu, Tao Sirkovic, Petar Orlandi, Alessio Semturs, Christopher Liu, Yun Gottweis, Juraj Webster, Dale R. Barral, Joëlle Chou, Katherine Kohli, Pushmeet Hassidim, Avinatan Matias, Yossi Manyika, James Fields, Rob Li, Jonathan X. Cohen, Marc L. Natarajan, Vivek Schaekermann, Mike Karthikesalingam, Alan Rodman, Adam |
| author_facet | Brodeur, Peter Koshy, Jacob M. Palepu, Anil Saab, Khaled Homiar, Ava Ruparel, Roma Wu, Charles Tanno, Ryutaro Xu, Joseph Wang, Amy Stutz, David Weng, Wei-Hung Ferrera, Hannah M. Barrett, David Crowley, Lindsey Lee, Jihyeon Rittner, Spencer E. Wulczyn, Ellery Zhang, Selena K. Vedadi, Elahe Kohn, Christine G. Kulkarni, Kavita Kadiyala, Vinay Mahdavi, Sara Du, Wendy Williams, Jessica M. Feinbloom, David Wong, Renee Tu, Tao Sirkovic, Petar Orlandi, Alessio Semturs, Christopher Liu, Yun Gottweis, Juraj Webster, Dale R. Barral, Joëlle Chou, Katherine Kohli, Pushmeet Hassidim, Avinatan Matias, Yossi Manyika, James Fields, Rob Li, Jonathan X. Cohen, Marc L. Natarajan, Vivek Schaekermann, Mike Karthikesalingam, Alan Rodman, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08448 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic Brodeur, Peter Koshy, Jacob M. Palepu, Anil Saab, Khaled Homiar, Ava Ruparel, Roma Wu, Charles Tanno, Ryutaro Xu, Joseph Wang, Amy Stutz, David Weng, Wei-Hung Ferrera, Hannah M. Barrett, David Crowley, Lindsey Lee, Jihyeon Rittner, Spencer E. Wulczyn, Ellery Zhang, Selena K. Vedadi, Elahe Kohn, Christine G. Kulkarni, Kavita Kadiyala, Vinay Mahdavi, Sara Du, Wendy Williams, Jessica M. Feinbloom, David Wong, Renee Tu, Tao Sirkovic, Petar Orlandi, Alessio Semturs, Christopher Liu, Yun Gottweis, Juraj Webster, Dale R. Barral, Joëlle Chou, Katherine Kohli, Pushmeet Hassidim, Avinatan Matias, Yossi Manyika, James Fields, Rob Li, Jonathan X. Cohen, Marc L. Natarajan, Vivek Schaekermann, Mike Karthikesalingam, Alan Rodman, Adam Human-Computer Interaction Artificial Intelligence Computation and Language Machine Learning 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. |
| title | A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2603.08448 |