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Mahvish Nagda, Jihyeon Lee, Matthew Thompson, Chunjong Park, Tim Strother, Valentin Liévin, Roma Ruparel, Akshay Goel, Teya Bergamaschi, Suhana Bedi, Meet Shah, Pavel Dubov, Liviu Panait, Toshiyuki Fukuzawa, Sam Schmidgall, Craig Schiff, Joseph Xu, Aliya Rysbek, Yana Lunts, Jan Freyberg, Rebecca Hemengway, Sunny Virmani, David Racz, Carey Radebaugh, Joëlle Barral, Kavi Goel, Dale R. Webster, Katherine Chou, Avinatan Hassidim, Yossi Matias, James Manyika, Gregory Wayne, Tao Tu, Yun Liu, Ethan Goh, Christina Chen, Ryutaro Tanno, Po-Hsuan Cameron Chen, Mike Schaekermann, Anil Palepu
Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text-based AI has shown promise, it discards essential perceptual dimensions and limits patients who cannot articulate symptoms in writing. Early efforts to extend medical AI to audio-visual interaction have demonstrated feasibility but not reached clinician-level performance. Here, we provide the first demonstration of expert-level AI in real-time clinical video consultations using AMIE (Articulate Medical Intelligence Explorer) in a video configuration. AMIE (Video) is a Gemini-based multi-agent system integrating low-latency dialogue, clinical reasoning, and real-time audio-visual perception. To guide development, we established a taxonomy and automated evaluations for clinical audio-visual cues in telehealth settings. In a randomized Objective Structured Clinical Examination (OSCE) study with 30 primary care physicians (PCPs), 15 patient actors and 100 clinical scenarios, we compared AMIE (Video), its text-only counterpart AMIE (Text), and PCPs consulting via video. Clinical evaluators rated AMIE (Video) on par or better than PCPs in history-taking, diagnosis, management, and physical observation and examination. Patient actors preferred AMIE's approach to assessing and explaining conditions, while PCPs were preferred for rapport and partnership building. In modality ablation, patient actors preferred AMIE (Video)'s interface over text chat for communicative effectiveness, convenience, and feeling understood. Limitations remain in fine anatomical precision, subtle affective nuances, and high-frequency movements. While further research is needed before real-world translation, these results mark an important milestone toward AI systems capable of augmenting care across the sensory complexity of clinical practice.
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@article{nagda2026expert,
title = {Towards Expert-level Medical AI for Real-time Video Consultations},
author = {Mahvish Nagda and Jihyeon Lee and Matthew Thompson and Chunjong Park and Tim Strother and Valentin Liévin and Roma Ruparel and Akshay Goel and Teya Bergamaschi and Suhana Bedi and Meet Shah and Pavel Dubov and Liviu Panait and Toshiyuki Fukuzawa and Sam Schmidgall and Craig Schiff and Joseph Xu and Aliya Rysbek and Yana Lunts and Jan Freyberg and Rebecca Hemengway and Sunny Virmani and David Racz and Carey Radebaugh and Joëlle Barral and Kavi Goel and Dale R. Webster and Katherine Chou and Avinatan Hassidim and Yossi Matias and James Manyika and Gregory Wayne and Tao Tu and Yun Liu and Ethan Goh and Christina Chen and Ryutaro Tanno and Po-Hsuan Cameron Chen and Mike Schaekermann and Anil Palepu},
year = {2026},
abstract = {Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text-based AI has shown promise, it discards essential perceptual dimensions and limits patients who cannot articulate symptoms in writing. Early efforts to extend medical AI to audio-visual interaction have demonstrated feasibility but not reached clinician-level performance. Here, we provide the first demonstration of ex},
url = {https://arxiv.org/abs/2608.09861},
keywords = {cs.AI, cs.CL, cs.CV},
eprint = {2608.09861},
archiveprefix = {arXiv},
}
{}