Clinical Ai | Techlist.io

googleAug 11, 20263 min readCurated summary

Advancing AMIE towards expert-level audio-visual clinical consultations

AMIE (Video) is Google’s real-time audiovisual medical AI system, designed to overcome the limitations of text-only clinical conversations. Built on Gemini and Project Astra, it observes visual and auditory cues, guides patients through virtual examinations, and performs diagnostic reasoning during live consultations. In a randomized study involving 300 simulated consultations, the system was evaluated against text-only AMIE and board-certified primary care physicians. ## Why Audio-Visual Consultation Matters - Traditional text-based systems lose important clinical information, including: - Gait and visible physical symptoms - Breathing patterns and signs of distress - Vocal and auditory cues - Patient responses during physical examination maneuvers - Requiring patients to describe symptoms in writing can reduce diagnostic accuracy, particularly for people with limited digital or health literacy. - Audiovisual interaction may also improve trust, communication, and access to medical expertise. ## AMIE’s Broader Development - Earlier versions of AMIE demonstrated expert-level performance in: - Text-based diagnostic dialogue - Differential diagnosis support - Disease treatment and longitudinal management - Specialist evaluations in oncology, cardiology, and ophthalmology - Reasoning over medical images and clinical documents - Google has also explored physician oversight and real-world clinical feasibility studies. ## Asynchronous Multi-Agent Architecture AMIE (Video) divides the consultation among three agents operating in parallel: - **Talker agent** - Maintains natural, low-latency spoken conversation. - Incorporates information and recommendations from the other agents. - **Planner agent** - Performs deeper clinical reasoning in the background. - Updates differential diagnoses and management plans. - Identifies missing information and reprioritizes clinical objectives. - **Perception agent** - Continuously analyzes audio and video. - Detects non-verbal findings such as visible distress, physical signs, and auditory abnormalities. - Interprets observations in the context of the conversation. This separation allows AMIE to reason deeply without creating long conversational pauses. Automated tests indicated that the agents contributed to improvements in history-taking, clinical reasoning, treatment recommendations, communication quality, and response latency. ## Automated Evaluation Framework - Google created a taxonomy of audiovisual clinical competencies based on medical literature. - The taxonomy covered: - Non-verbal visual cues - Auditory signals - Physical examination maneuvers - The evaluation suite included: - Single-turn tests targeting specific perception and reasoning abilities - Multi-turn simulated consultations assessing complete conversational performance - Simulations injected visual findings as textual descriptions, such as a patient holding handwriting samples up to the camera. - These tests helped identify capabilities and failure modes before human evaluation. ## Randomized Video Study - The study used a synchronous video consultation interface and an Objective Structured Clinical Examination format. - It included: - 100 clinical scenarios - Five body systems: cardiopulmonary, abdominal, HEENT, neurological/psychiatric, and musculoskeletal - 15 trained patient actors - 300 standardized consultations - Three study arms were compared: - **AMIE (Video):** Real-time audiovisual consultations - **AMIE (Text):** Text-only AMIE used to isolate the value of audiovisual capabilities - **PCP (Video):** Board-certified primary care physicians using the same video interface - An independent panel of 20 experienced primary care physicians assessed the consultations using established clinical rubrics. AMIE (Video) represents a move from text-based medical dialogue toward interactive, multimodal consultations. Its multi-agent design and audiovisual perception are intended to preserve conversational responsiveness while supporting richer clinical reasoning, though the reported findings come from simulated consultations and require further validation in real-world clinical care.

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Clinical Ai Gemini Multimodal AI Telehealth+3

googleJul 22, 20263 min readCurated summary

SymptomAI: Towards a conversational AI agent for everyday symptom assessment

SymptomAI explores whether conversational AI can conduct realistic symptom interviews and generate useful differential diagnoses outside curated medical vignettes. In a randomized national study of 13,917 participants, SymptomAI agents often performed as well as or better than clinician-generated differentials according to expert reviewers, particularly when they actively asked follow-up questions. The study also found that diagnoses associated with infectious illnesses corresponded with shifts in participants’ Fitbit biosignals, suggesting potential for large-scale health research. ## Moving Beyond Curated Medical Cases - Existing language-model evaluations often use detailed, synthetic, or highly structured patient vignettes. - Real patients may provide incomplete information, have varying medical literacy, or describe symptoms unpredictably during conversation. - SymptomAI was designed to test end-to-end symptom assessment in a more natural setting, while making clear that its outputs were research results rather than clinical diagnoses. ## National-Scale Study Design - 13,917 consenting participants were randomly assigned to one of five Gemini Flash 2.0 SymptomAI agents. - Participants described their symptoms, answered follow-up questions, received a differential diagnosis (DDx), and were given next-step recommendations. - Two weeks later, participants reported diagnoses received from healthcare providers. - Three board-certified clinicians reviewed the conversations, created their own differentials, and blindly ranked SymptomAI’s DDx against clinician-generated alternatives. ## SymptomAI Compared Favorably with Clinicians - Clinical reviewers preferred SymptomAI’s differential diagnosis over those from other clinicians in more than 50% of cases. - SymptomAI’s DDx was more likely to be ranked as the highest-quality option. - Using top-five accuracy—whether the eventual provider diagnosis appeared among five proposed diagnoses—reviewers found SymptomAI’s differentials accurate more often than the comparison clinician differentials. ## Follow-Up Questions Improved Accuracy - The study tested five interview strategies: - Dynamic Live and Dynamic Final agents could ask unrestricted follow-up questions. - Fixed Canonical and Flexible Canonical agents used standardized medical history questions. - The Base condition represented a user-led interaction with an unprompted language model. - Every agent-driven strategy significantly outperformed the Base condition. - The findings indicate that actively eliciting additional information is more effective than relying solely on what users initially choose to disclose. ## Strongest Results in Uncertain Cases - SymptomAI’s advantage over clinician baselines was greatest when clinicians expressed low confidence in their own differentials. - This suggests conversational AI may be especially useful as a second opinion or support tool in ambiguous cases, though the study does not establish that it can replace professional diagnosis. ## Connecting Diagnoses with Wearable Data - The researchers used SymptomAI’s diagnostic outputs as potential reference labels for analyzing population-scale physiological data. - Participants provided up to 30 days of Fitbit biometric data before their SymptomAI interaction. - Acute respiratory infection cases showed noticeable biosignal changes in the days leading up to symptom reporting. - These shifts appeared consistent with symptom onset and possible immune responses, although the provided text ends before presenting the full analysis. SymptomAI’s results support building conversational systems that ask structured follow-up questions and assist with differential diagnosis. Any practical deployment should retain clinician oversight, communicate uncertainty clearly, and treat AI-generated assessments as decision support rather than confirmed medical diagnoses.

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Clinical Ai Gemini Conversational Ai Differential Diagnosis+3