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REVIEW 2 major objections 4 minor 88 references

Towards Expert-level Medical AI for Real-time Video Consultations

T0 review · 2 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read AMIE (Video), a real-time video consultation AI, was rated by clinicians on par with or better than primary care physicians in simulated telehealth visits.

desk verdict Strong, well-designed OSCE study showing a video-based AI beating PCPs on simulated telehealth encounters, but the central 'real-time expert-level' claim is partly confounded by an offline post-questionnaire step where the AI gets extra compute. read the letter →

arxiv 2608.09861 v1 pith:KSNCKWEV submitted 2026-08-10 cs.AI cs.CLcs.CV

Mahvish Nagda , Jihyeon Lee , Matthew Thompson , Chunjong Park , Tim Strother , Valentin Liévin , Roma Ruparel , Akshay Goel
show 32 more authors
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
This is my paper · ORCID
classification cs.AIcs.CLcs.CV
keywords medicalAIvideoconsultationstelehealthmulti-agentsystemOSCEaudio-visualperceptionclinicaldiagnosispatientactors
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that an AI system, AMIE in a video configuration, can conduct real-time clinical video consultations at a level that independent clinical evaluators rate on par with or better than primary care physicians. The evidence is a randomized OSCE study of 100 scripted telehealth scenarios, enacted by 15 professional patient actors, with 10 board-certified PCPs consulting via video and 20 independent PCPs evaluating the recordings. AMIE (Video) scored 83% overall on case-specific rubrics versus 68% for PCPs ($p=1.3\times10^{-9}$), matched the reference diagnosis at top-1 in 91% of cases versus 77% ($p=0.039$), and was rated higher on physical observation and guided examination. Patient actors preferred AMIE (Video) over text chat for communication, convenience, and feeling understood, while PCPs retained a non-significant preference for rapport and partnership. If the result transfers beyond actors and scripted cases, it would be the first demonstration of expert-level AI in real-time video consultations, a step toward AI that can see and hear patients rather than only read text.

What carries the argument

The central mechanism is an asynchronous three-agent harness. A Talker agent responds to the patient at low latency; a Planner agent maintains clinical goals, a running differential, and management milestones; and a Perception agent watches and listens over a longer video window while keeping a persistent memory of audio-visual cues. The agents run in parallel, so the Talker can reply within about 2.6 seconds per turn while the Planner and Perception agents continue deeper reasoning and perception in the background. This decoupling is what the paper argues makes expert-level real-time video consultation possible, and automated ablations show the Perception and Planner agents each meaningfully raise rubric scores over the Talker-only baseline.

What would settle it

Run the same 100 cases with PCP cameras on and with unscripted real patients or real telehealth recordings, then compare top-1 diagnostic match and overall rubric scores; if PCPs reach AMIE's 91% top-1 or 83% overall scores, the expert-level claim is falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that AMIE in its video configuration—AMIE (Video)—is the first AI system to demonstrate expert-level performance in real-time clinical video consultations. In a randomized OSCE with 100 telehealth scenarios, 15 professional patient actors, 10 consulting board-certified PCPs, and 20 independent PCP evaluators, clinical evaluators rated AMIE (Video) at 83% overall on case-specific rubrics versus 68% for PCPs ($p=1.3\times10^{-9}$), and AMIE matched the reference diagnosis at top-1 in 91% of cases versus 77% for PCPs ($p=0.039$). The largest advantage was in perception and examination (74% vs 47%) and guided physical examination (72% vs 39%). Patient actors preferred AMIE (Video) over text chat for communication, convenience, and feeling understood, while PCPs were preferred, though without statistical significance, for rapport and partnership. The authors state the system is not ready for real-world deployment, with limitations in fine anatomical precision, subtle affective cues, high-frequency movements, and the use of actors rather than real patients.

Load-bearing premise

The claim rests on the assumption that board-certified PCPs consulting through video with their cameras off, in scripted encounters with professional actors, are a fair proxy for real clinical performance; if real patients, real environments, or a visible physician change the comparison, the expert-level result may not transfer.

Editorial extensions

If this is right

  • If the central claim holds, video-based medical AI can be evaluated against physicians on the modality patients actually use for remote care, not just text chat.
  • The perception-and-examination gain (74% vs 47%) suggests the video configuration specifically recovers information that text-only AI discards.
  • The asynchronous architecture cuts mean turn latency from 21.4 seconds to 2.6 seconds, making real-time dialogue practical while retaining clinical reasoning gains.
  • The comparable top-3 diagnostic accuracy (98% vs 90%) implies the AI's edge is in ranking the right diagnosis first rather than in carrying a broader differential.
  • Patient actors preferred AMIE (Video) over text chat for communicative effectiveness, convenience, and feeling understood, while PCPs remained preferred for rapport and partnership, so human connection is a separate axis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: In a real clinic, PCPs would keep cameras on, which may narrow or erase the reported rapport and empathy gaps, making the expert-level framing depend on the cameras-off design.
  • Editorial inference: Because AMIE (Text) already scores near AMIE (Video) on diagnostic and management rubrics, the paper's own data suggest the video modality mainly buys perception, examination, and patient experience rather than raw diagnostic accuracy.
  • Editorial inference: A natural next experiment would use full-duplex interaction, letting the AI speak up when a patient performs a maneuver incorrectly; this directly targets the missed-correction failures the paper documents.
  • Editorial inference: Since dermatologic and other non-actable cases were excluded, testing on real patient video with visible lesions would be a fast way to see whether the perception advantage generalizes beyond actors.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper presents AMIE (Video), a Gemini-based multi-agent system for real-time audio-visual clinical consultations, and reports a randomized OSCE-style study comparing AMIE (Video), AMIE (Text), and 10 primary care physicians (PCPs) across 100 actable clinical scenarios enacted by 15 professional patient actors. A separate panel of 20 clinical evaluators rated consultations using general and case-specific rubrics, and patient actors provided communication and modality preferences. The central claim is that AMIE (Video) achieves expert-level performance in real-time video consultations, with clinical evaluators rating it on par or better than PCPs in history-taking, diagnosis, management, and physical observation and examination, while PCPs retain an edge in rapport and partnership building. The paper also contributes a taxonomy of telehealth audio-visual cues and an automated evaluation suite used to guide system development.

Significance. If the headline claim holds, this would be a notable milestone for medical AI: a controlled, randomized comparison of a real-time video-capable system against board-certified PCPs with independent clinical evaluators, pre-specified rubrics, scenario-level blocking, bootstrap confidence intervals, and FDR-corrected tests. The design has real strengths: the scenario packs were developed externally, the evaluator panel was independent of the consulting PCPs, and the multi-arm structure with a text-only ablation is appropriate for isolating modality effects. The paper also ships a useful taxonomy of audio-visual telehealth competencies. However, two load-bearing asymmetries prevent the current evidence from fully supporting the abstract's 'expert-level real-time video consultation' claim: the post-encounter questionnaire gives AMIE an offline inference-compute advantage that is not available to the PCPs, and the PCP baseline was forced to keep cameras off, which differentially affects communication-related ratings. The central claim is defensible in principle, but it needs re-analysis or reframing before publication.

major comments (2)
  1. [§2.3.1, §2.3.3, §3.1] The headline diagnostic and clinical-reasoning comparisons do not isolate real-time consultation skill. Section 2.3.1 states that after each encounter AMIE answers a structured post-questionnaire while 'leveraging inference-time compute scaling via multi-draft synthesis' to improve recommendation quality, whereas PCPs complete the same questionnaire without that capability. Section 2.3.3 and Appendix A.6.7 state that clinical evaluators assessed consultation recordings, transcripts, and post-questionnaire responses, and the top-1 diagnostic accuracy reported in Section 3.1 is based on the generated differential diagnosis, which comes from the post-questionnaire. The large observed advantages for AMIE (Video) over PCPs in top-1 diagnosis (91% vs 77%), clinical reasoning (90% vs 76%), and treatment planning (79% vs 67%) are therefore consistent with an offline, latency-unconstrained advantage rather than a real-time video consultation advantage. The paper should report scores based only on the live video/transcript, or at minimum provide a sensitivity analysis that separates the post-questionnaire contribution and a discussion of how much of the gap survives when the post-encounter step is removed. Without this, the abstract's claim of 'expert-level AI in real-time clinical video consultations' exceeds what the data support.
  2. [§2.3.1, §A.6.1, §5.1] The human baseline is not a fair comparator for communication-related claims because PCPs were required to keep their cameras off, as described in Section 2.3.1 and Appendix A.6.1. This removes gaze, facial expression, and other non-verbal channels that are normal components of video consultations, and Section 5.1 acknowledges that it reduces the ecological validity of the human baseline. Several headline findings concern communication, empathy, rapport, and 'on par or better' ratings from both clinical evaluators and patient actors; the camera-off requirement may therefore inflate AMIE's relative performance on those dimensions. The authors should either include a visible-physician condition, perform a sensitivity analysis that excludes or down-weights communication items, or substantially temper the communication and rapport comparisons in the abstract and Section 3.1. The current discussion, while candid about the limitation, does not carry the caveat into the interpretation of the main comparative claims.
minor comments (4)
  1. [§3.2] The text says 'While not significant, directionally AMIE (Video) scored higher than AMIE (Text) on all criteria, as reflected by the rating for "Happy to see again" (91% vs 74%, p=0.038)', but a p-value of 0.038 is below the conventional 0.05 threshold; clarify whether this p-value survived FDR correction and state the significance criterion explicitly.
  2. [§3.1, Figure 5B] The text refers to 'six case-specific rubric domains' but the case-specific rubrics described in Appendix A.6.9 comprise five domains (history taking, perception and examination, clinical reasoning, treatment planning, communication) plus an overall score; please reconcile the count in the figure and prose.
  3. [§2.3.1] The statement that mean conversation duration was comparable (8.94 minutes for AMIE Video vs 9.30 minutes for PCPs) is presented without confidence intervals or a statistical test; please add these if the similarity is intended as a claim, or mark it as descriptive only.
  4. [Abstract, §2.3] The abstract reports '30 primary care physicians (PCPs)' while the main text distinguishes 10 consulting PCPs and 20 clinical evaluator PCPs; please clarify this in the abstract to avoid conflating the two roles.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central OSCE comparison is externally grounded, author-built auto-evaluations are development tools, and self-citations are not load-bearing.

full rationale

The paper's central result is an empirical comparison against an external benchmark, not a derivation from fitted parameters or author-defined constructs. Scenario packs were developed by an external OSCE laboratory and verified by US board-certified PCPs (Section A.6.2), and a distinct set of 100 scenarios was used in the main study. Clinical evaluators were 20 board-certified PCPs separate from the baseline PCP cohort (Section A.6.7), and they assessed general and case-specific rubrics whose content is clinical checklist criteria (Section A.7). The author-built taxonomy and automated evaluations (Sections 2.2, A.1-A.4) are used to guide development and characterize components, not to generate the headline OSCE scores. The paper's self-citations to prior AMIE work [10, 11, 19] describe architectural lineage and rubric adaptation, but the load-bearing comparison (AMIE vs PCPs on OSCE rubrics) does not reduce to those citations. One non-circular caveat worth noting: Section 2.3.1 states AMIE used inference-time multi-draft synthesis for the post-encounter questionnaire while PCPs did not, which is a potential construct-validity confound for the real-time claim, but it is an asymmetric capability rather than an equation-level reduction or fitted-parameter prediction. The camera-off PCP baseline is likewise disclosed as reducing ecological validity (Section 5.1). Overall, no circular step is exhibited.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The main result rests on assumptions: the OSCE rubrics are valid measures, actors are faithful proxies for patients, evaluators are reliable, and the proprietary Gemini models behave consistently. The architecture itself is described and does not introduce new physical or theoretical entities. There are no fitted free parameters in the central comparison, but the automated evaluation thresholds and rubric weights are design choices made by the authors.

assumptions (4)
  • domain assumption The OSCE scenario rubric scores and ground-truth diagnoses, authored by clinicians, are valid measures of clinical competence.
    Sections 2.3.3, A.6.2, and A.7 assume the 20-point case-specific rubrics capture meaningful clinical skill. If rubrics reward thorough scripting over genuine clinical judgement, 'expert-level' may be an artifact of rubric design.
  • domain assumption Professional patient actors can realistically simulate clinical presentations, and their enactments are faithful to real patient encounters.
    Section A.6.3 and the Limitations confirm that only 'actable' presentations were included. If actors omit key contextual or behavioral elements of real patients, the comparison does not reflect real-world practice.
  • domain assumption Independent clinical evaluators can score video consultations reliably with the provided interface and materials.
    Section A.6.7 assumes 20 PCP evaluators using the review interface (Figure A.5) produce consistent, unbiased ratings. No inter-rater reliability metrics are reported, so rating variance is unmeasured.
  • standard math Gemini 3 Flash and Gemini 3.1 Pro provide stable base-model capabilities that do not degrade or change during the study.
    The entire system is built on these proprietary models (Section 2.1). Their behavior is not reproducible by readers and could vary by deployment.

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Cite this review

Pith. "Pith review of Towards Expert-level Medical AI for Real-time Video Consultations." pith.science (2026). https://pith.science/paper/KSNCKWEV

@misc{pith2026260809861,
  author       = {Pith},
  title        = {Pith review of: Towards Expert-level Medical AI for Real-time Video Consultations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KSNCKWEV}},
  note         = {Machine review of arXiv:2608.09861}
}
read the original 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 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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Reference graph

Works this paper leans on

88 extracted references · 45 canonical work pages

  1. [1]

    BMJ Open , volume=

    International variations in primary care physician consultation time: a systematic review of 67 countries , author=. BMJ Open , volume=. 2017 , publisher=

    2017

  2. [2]

    British Journal of General Practice , volume=

    The benefits of longer consultations in general practice , author=. British Journal of General Practice , volume=. 2005 , publisher=

    2005

  3. [3]

    Nature , pages=

    Towards accurate differential diagnosis with large language models , author=. Nature , pages=. 2025 , publisher=

    2025

  4. [4]

    Nature , pages=

    Towards conversational diagnostic artificial intelligence , author=. Nature , pages=. 2025 , publisher=

    2025

  5. [5]

    arXiv preprint arXiv:2507.16947 , year=

    AI-based clinical decision support for primary care: A real-world study , author=. arXiv preprint arXiv:2507.16947 , year=

    arXiv

  6. [6]

    Annals of Internal Medicine , volume=

    Comparison of initial artificial intelligence (AI) and final physician recommendations in AI-assisted virtual urgent care visits , author=. Annals of Internal Medicine , volume=. 2025 , publisher=

    2025

  7. [7]

    arXiv preprint arXiv:2503.06074 , year=

    Towards conversational ai for disease management , author=. arXiv preprint arXiv:2503.06074 , year=

    arXiv

  8. [8]

    Nature , pages=

    Towards Conversational AI for Disease Management , author=. Nature , pages=. 2026 , publisher=

    2026

Show all 88 references
  1. [9]

    arXiv preprint arXiv:2505.04653 , year=

    Advancing Conversational Diagnostic AI with Multimodal Reasoning , author=. arXiv preprint arXiv:2505.04653 , year=

    arXiv

  2. [10]

    Nature Medicine , pages=

    Advancing conversational diagnostic AI with multimodal reasoning , author=. Nature Medicine , pages=. 2026 , publisher=

    2026

  3. [11]

    arXiv preprint arXiv:2507.15743 , year=

    Towards physician-centered oversight of conversational diagnostic AI , author=. arXiv preprint arXiv:2507.15743 , year=

    arXiv

  4. [12]

    arXiv preprint arXiv:2403.13313 , year=

    Polaris: A safety-focused llm constellation architecture for healthcare , author=. arXiv preprint arXiv:2403.13313 , year=

    arXiv

  5. [13]

    Telemedicine reports , volume=

    Patient perceptions of audio-only versus video telehealth visits: a qualitative study among patients in an academic medical center setting , author=. Telemedicine reports , volume=. 2024 , publisher=

    2024

  6. [14]

    The American journal of medicine , volume=

    The telehealth ten: a guide for a patient-assisted virtual physical examination , author=. The American journal of medicine , volume=. 2021 , publisher=

    2021

  7. [15]

    Journal of Telemedicine and Telecare , volume=

    Translatability of physical examination to teleconsultation in primary care settings , author=. Journal of Telemedicine and Telecare , volume=. 2026 , publisher=

    2026

  8. [16]

    arXiv preprint arXiv:2408.03314 , year=

    Scaling llm test-time compute optimally can be more effective than scaling model parameters , author=. arXiv preprint arXiv:2408.03314 , year=

    arXiv

  9. [17]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    s1: Simple test-time scaling , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    2025

  10. [18]

    Advances in Neural Information Processing Systems , volume=

    Chain-of-thought prompting elicits reasoning in large language models , author=. Advances in Neural Information Processing Systems , volume=

  11. [19]

    Advances in neural information processing systems , volume=

    Large language models are zero-shot reasoners , author=. Advances in neural information processing systems , volume=

  12. [20]

    arXiv preprint arXiv:2203.11171 , year=

    Self-consistency improves chain of thought reasoning in language models , author=. arXiv preprint arXiv:2203.11171 , year=

    arXiv

  13. [21]

    Advances in neural information processing systems , volume=

    Self-refine: Iterative refinement with self-feedback , author=. Advances in neural information processing systems , volume=

  14. [22]

    Project Astra: The future of AI assistants , year =

  15. [23]

    Journal of Graduate Medical Education , volume=

    The TeleHealth OSCE: preparing trainees to use telemedicine as a tool for transitions of care , author=. Journal of Graduate Medical Education , volume=. 2020 , publisher=

    2020

  16. [24]

    Fundamental Clinical Skills , publisher =

    McDonough, Karen and others , title =. Fundamental Clinical Skills , publisher =. 2024 , howpublished =

    2024

  17. [25]

    Telehealth Physical Exam Fact Sheet , year =

  18. [26]

    Journal of telemedicine and telecare , volume=

    The virtual physical exam in the 21st century , author=. Journal of telemedicine and telecare , volume=. 2021 , publisher=

    2021

  19. [27]

    2026 , eprint=

    A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic , author=. 2026 , eprint=

    2026

  20. [28]

    arXiv preprint arXiv:2605.09272 , year=

    Towards Conversational Medical AI with Eyes, Ears and a Voice , author=. arXiv preprint arXiv:2605.09272 , year=

    arXiv

  21. [29]

    arXiv preprint arXiv:2404.18416 , year=

    Capabilities of gemini models in medicine , author=. arXiv preprint arXiv:2404.18416 , year=

    arXiv

  22. [30]

    Nejm Ai , volume=

    Towards generalist biomedical AI , author=. Nejm Ai , volume=. 2024 , publisher=

    2024

  23. [31]

    arXiv preprint arXiv:2311.16452 , year=

    Can generalist foundation models outcompete special-purpose tuning? case study in medicine , author=. arXiv preprint arXiv:2311.16452 , year=

    arXiv

  24. [32]

    Clinical Gerontologist , volume=

    Overcoming barriers for older adults to maintain virtual community and social connections during the COVID-19 pandemic , author=. Clinical Gerontologist , volume=. 2022 , publisher=

    2022

  25. [33]

    JMIR aging , volume=

    Barriers to telemedicine video visits for older adults in independent living facilities: mixed methods cross-sectional needs assessment , author=. JMIR aging , volume=. 2022 , publisher=

    2022

  26. [34]

    Digital Health , volume=

    Patient-facing chatbots: Enhancing healthcare accessibility while navigating digital literacy challenges and isolation risks—a mixed-methods study , author=. Digital Health , volume=. 2025 , publisher=

    2025

  27. [35]

    PLOS digital health , volume=

    Digital literacy as a new determinant of health: a scoping review , author=. PLOS digital health , volume=. 2023 , publisher=

    2023

  28. [36]

    Nature , volume=

    Large language models encode clinical knowledge , author=. Nature , volume=. 2023 , publisher=

    2023

  29. [37]

    Nature medicine , volume=

    Toward expert-level medical question answering with large language models , author=. Nature medicine , volume=. 2025 , publisher=

    2025

  30. [38]

    arXiv preprint arXiv:2410.08328 , year=

    Agents thinking fast and slow: A talker-reasoner architecture , author=. arXiv preprint arXiv:2410.08328 , year=

    arXiv

  31. [39]

    Nature , pages=

    Accelerating scientific discovery with Co-Scientist , author=. Nature , pages=. 2026 , publisher=

    2026

  32. [40]

    , author=

    Assessment of clinical competence using objective structured examination. , author=. British medical journal , volume=. 1975 , publisher=

    1975

  33. [41]

    Putting them through their PACES

    Gentles, Helen. Putting them through their PACES . Practical Assessment of Clinical Examination Skills. BMJ

  34. [42]

    Assessment of communication skills using telehealth: considerations for educators

    Wright, Hattie H and O'Shea, Marie-Claire and Sekula, Julia and Mitchell, Lana J. Assessment of communication skills using telehealth: considerations for educators. Frontiers in Medicine

  35. [43]

    Jama , volume=

    Practices to foster physician presence and connection with patients in the clinical encounter , author=. Jama , volume=

  36. [44]

    JAMA internal medicine , volume=

    Assessing telemedicine unreadiness among older adults in the United States during the COVID-19 pandemic , author=. JAMA internal medicine , volume=

  37. [45]

    Systematic Reviews , volume=

    A scoping review of the unassisted physical exam conducted over synchronous audio-video telemedicine , author=. Systematic Reviews , volume=. 2022 , publisher=

    2022

  38. [46]

    Patient education and counseling , volume=

    On the importance of nonverbal communication in the physician--patient interaction , author=. Patient education and counseling , volume=. 2007 , publisher=

    2007

  39. [47]

    Applied & Preventive Psychology , volume=

    Nonverbal behavior in clinician-patient interaction , author=. Applied & Preventive Psychology , volume=. 1995 , publisher=

    1995

  40. [48]

    CMAJ: Canadian medical association journal , volume=

    Effective physician-patient communication and health outcomes: a review , author=. CMAJ: Canadian medical association journal , volume=

  41. [49]

    The case-control study consensus and controversy , pages=

    Bias in analytic research , author=. The case-control study consensus and controversy , pages=. 1979 , publisher=

    1979

  42. [50]

    2025 , publisher=

    Bates' guide to physical examination and history taking , author=. 2025 , publisher=

    2025

  43. [51]

    Nature , volume=

    Foundation models for generalist medical artificial intelligence , author=. Nature , volume=. 2023 , publisher=

    2023

  44. [52]

    2025 , url=

    Veo 3.1 , author=. 2025 , url=

    2025

  45. [53]

    2026 , url=

    Gemini Omni , author=. 2026 , url=

    2026

  46. [54]

    Gemini 3.1 Pro Model Card , author=

  47. [55]

    Gemini 3 Flash Model Card , author=

  48. [56]

    arXiv preprint arXiv:2505.08775 , year=

    Healthbench: Evaluating large language models towards improved human health , author=. arXiv preprint arXiv:2505.08775 , year=

    arXiv

  49. [57]

    arXiv preprint arXiv:2604.27470 , year=

    HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats , author=. arXiv preprint arXiv:2604.27470 , year=

    arXiv

  50. [58]

    arXiv preprint arXiv:2606.26083 , year=

    Real-Time Voice AI Hears but Does Not Listen , author=. arXiv preprint arXiv:2606.26083 , year=

    arXiv

  51. [59]

    Family practice , volume=

    The effectiveness of teleconsultations in primary care: systematic review , author=. Family practice , volume=. 2022 , publisher=

    2022

  52. [60]

    The Lancet Digital Health , volume=

    Virtual primary care: fragmentation or integration? , author=. The Lancet Digital Health , volume=. 2019 , publisher=

    2019

  53. [61]

    , author=

    Relative contributions of history-taking, physical examination, and laboratory investigation to diagnosis and management of medical outpatients. , author=. Br Med J , volume=. 1975 , publisher=

    1975

  54. [62]

    Western Journal of Medicine , volume=

    Contributions of the history, physical examination, and laboratory investigation in making medical diagnoses , author=. Western Journal of Medicine , volume=

  55. [63]

    Journal of medical Internet research , volume=

    Comparison of telephone and video telehealth consultations: systematic review , author=. Journal of medical Internet research , volume=. 2023 , publisher=

    2023

  56. [64]

    International journal of medical informatics , volume=

    Videoconference compared to telephone in healthcare delivery: a systematic review , author=. International journal of medical informatics , volume=. 2018 , publisher=

    2018

  57. [65]

    2023 , publisher=

    Macleod's Clinical Examination-E-Book: Macleod's Clinical Examination-E-Book , author=. 2023 , publisher=

    2023

  58. [66]

    arXiv preprint arXiv:2605.12460 , year=

    Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs , author=. arXiv preprint arXiv:2605.12460 , year=

    arXiv

  59. [67]

    Interaction Models: A Scalable Approach to Human-AI Collaboration , author=

  60. [68]

    2026 , note=

    Kuroki, So and Kubo, Yotaro and Akiba, Takuya and Tang, Yujin and Yaguchi, Manato , booktitle=. 2026 , note=

    2026

  61. [69]

    arXiv preprint arXiv:2409.16382 , year=

    Towards synthetic data generation for improved pain recognition in videos under patient constraints , author=. arXiv preprint arXiv:2409.16382 , year=

    arXiv

  62. [70]

    Medical Education , volume =

    Hodges, Brian and McIlroy, Jodi Herold , title =. Medical Education , volume =. doi:https://doi.org/10.1046/j.1365-2923.2003.01674.x , url =. https://asmepublications.onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2923.2003.01674.x , year =

    2003

  63. [71]

    Applied Sciences , volume=

    What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams , author=. Applied Sciences , volume=

  64. [72]

    Can large language models reason about medical questions?

    Liévin, Valentin and Hother, Christoffer Egeberg and Motzfeldt, Andreas Geert and Winther, Ole. Can large language models reason about medical questions?. Patterns (N. Y.). doi:10.1016/j.patter.2024.100943

    2024

  65. [73]

    AgentClinic : a multimodal agent benchmark to evaluate AI in simulated clinical environments

    Schmidgall, Samuel and Ziaei, Rojin and Harris, Carl and Reis, Eduardo and Jopling, Jeffrey and Moor, Michael. AgentClinic : a multimodal agent benchmark to evaluate AI in simulated clinical environments. arXiv [cs.HC]. arXiv:2405.07960

    arXiv

  66. [74]

    Agent Hospital: A simulacrum of hospital with evolvable medical agents

    Li, Junkai and Lai, Yunghwei and Li, Weitao and Ren, Jingyi and Zhang, Meng and Kang, Xinhui and Wang, Siyu and Li, Peng and Zhang, Ya-Qin and Ma, Weizhi and Liu, Yang. Agent Hospital: A simulacrum of hospital with evolvable medical agents. arXiv [cs.AI]. arXiv:2405.02957

    arXiv

  67. [75]

    Moshi: a speech-text foundation model for real-time dialogue

    Défossez, Alexandre and Mazaré, Laurent and Orsini, Manu and Royer, Amélie and Pérez, Patrick and Jégou, Hervé and Grave, Edouard and Zeghidour, Neil. Moshi: a speech-text foundation model for real-time dialogue. arXiv [eess.AS]. doi:10.48550/arXiv.2410.00037. arXiv:2410.00037

    doi arXiv

  68. [76]

    Mini-Omni: Language models can hear, talk while thinking in streaming

    Xie, Zhifei and Wu, Changqiao. Mini-Omni: Language models can hear, talk while thinking in streaming. arXiv [cs.AI]. doi:10.48550/arXiv.2408.16725. arXiv:2408.16725

    doi arXiv

  69. [77]

    Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum

    Ayers, John W and Poliak, Adam and Dredze, Mark and Leas, Eric C and Zhu, Zechariah and Kelley, Jessica B and Faix, Dennis J and Goodman, Aaron M and Longhurst, Christopher A and Hogarth, Michael and Smith, Davey M. Comparing physician and artificial intelligence chatbot respo...

  70. [78]

    Patient education and counseling , volume=

    Association between nonverbal communication during clinical interactions and outcomes: a systematic review and meta-analysis , author=. Patient education and counseling , volume=. 2012 , publisher=

    2012

  71. [79]

    Machine learning for health (ML4H) , pages=

    Med-flamingo: a multimodal medical few-shot learner , author=. Machine learning for health (ML4H) , pages=. 2023 , organization=

    2023

  72. [80]

    Advances in neural information processing systems , volume=

    Llava-med: Training a large language-and-vision assistant for biomedicine in one day , author=. Advances in neural information processing systems , volume=

  73. [81]

    Nature medicine , volume=

    An evaluation framework for clinical use of large language models in patient interaction tasks , author=. Nature medicine , volume=. 2025 , publisher=

    2025

  74. [82]

    NEJM AI , volume=

    Exploring large language models for specialist-level oncology care , author=. NEJM AI , volume=. 2025 , publisher=

    2025

  75. [83]

    Nature medicine , volume=

    A large language model for complex cardiology care , author=. Nature medicine , volume=. 2026 , publisher=

    2026

  76. [84]

    Brodeur and Thomas A

    Peter G. Brodeur and Thomas A. Buckley and Zahir Kanjee and Ethan Goh and Evelyn Bin Ling and Priyank Jain and Stephanie Cabral and Raja-Elie Abdulnour and Adrian D. Haimovich and Jason A. Freed and Andrew Olson and Daniel J. Morgan and Jason Hom and Robert Gallo and Liam G. M...

    2026

  77. [85]

    The assessment of clinical skills/competence/performance

    Miller, George E. The assessment of clinical skills/competence/performance. Academic Medicine

  78. [86]

    Defining and assessing professional competence

    Epstein, Ronald M and Hundert, Edward M. Defining and assessing professional competence. JAMA. doi:10.1001/jama.287.2.226

    doi

  79. [87]

    Nature Health , pages=

    Reduced symptom reporting quality during human--chatbot versus human--physician interactions , author=. Nature Health , pages=. 2026 , publisher=

    2026

  80. [88]

    2026 , eprint=

    ResidencyRL: Reinforcement Learning in Simulated Clinical Environments , author=. 2026 , eprint=

    2026

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.