谷歌把医疗AI做到视频问诊了，AMIE视频版在100个临床场景里和真人医生打平甚至更好，患者还更喜欢它的解释方式，想看看AI看病到底行不行可以读这篇。
谷歌研究团队推出AMIE（Video），一个基于Gemini的多智能体系统，支持低延迟对话、临床推理和实时视听感知。在包含30名初级保健医生、15名患者演员和100个临床场景的随机OSCE研究中，AMIE（Video）在病史采集、诊断、管理和体格检查方面与医生评分相当或更优。患者演员更偏好AMIE的病情评估和解释方式，但医生在建立融洽关系和伙伴关系上更受青睐。模态消融显示，患者更偏好视频界面而非纯文本聊天。研究指出在精细解剖精度、细微情感表达和高频动作方面仍有局限。
Towards Expert-level Medical AI for Real-time Video Consultations
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.