Towards Conversational Diagnostic AI

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   Towards conversational diagnostic artificial intelligence

   Tao Tu, Mike Schaekermann, Anil Palepu et al. · 2025 · Nature · cited 459 times · Not summarized yet

   Abstract

   At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust1,2. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self-play-based3 simulated environment with automated feedback for scaling learning across disease conditions, specialties and contexts. We designed a framework for evaluating clinically meaningful …

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   A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

   P. Brodeur, Jacob M. Koshy, Anil Palepu et al. · 2026 · arXiv.org · preprint, not peer reviewed · cited 12 times · Not summarized yet

   Abstract

   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 so…

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   Advancing Conversational Diagnostic AI with Multimodal Reasoning

   Khaled Saab, Jan Freyberg, Chunjong Park et al. · 2025 · arXiv.org · preprint, not peer reviewed · cited 31 times · Not summarized yet

   Abstract

   Large Language Models (LLMs) have demonstrated great potential for conducting diagnostic conversations but evaluation has been largely limited to language-only interactions, deviating from the real-world requirements of remote care delivery. Instant messaging platforms permit clinicians and patients to upload and discuss multimodal medical artifacts seamlessly in medical consultation, but the ability of LLMs to reason over such data while preserving other attributes of competent diagnostic conversation remains unknown. Here we advance the conversational diagnosis and management performance of the Articulate Medical Intelligence Explorer (AMIE) through a new capability to gather and interpret…

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   Towards Conversational AI for Disease Management

   Anil Palepu, Valentin Liévin, Wei-Hung Weng et al. · 2025 · arXiv.org · preprint, not peer reviewed · cited 15 times · Not summarized yet

   Abstract

   While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic response, and safe medication prescription - remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemi…

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   Sequential Diagnosis with Language Models

   Harsha Nori, Mayank Daswani, Christopher Kelly et al. · 2025 · Computation and Language · cited 132 times · AI summary available

   Abstract

   Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes and multiple-choice questions that fail to reflect the complexity and nuance of evidence-based medicine in real-world settings. In clinical practice, physicians iteratively formulate and revise diagnostic hypotheses, adapting each subsequent question and test to what they've just learned, and weigh the evolving evidence before committing to a final diagnosis. To emulate this iterative process, we introduce the Sequential Diagnosis Benchmark, which transforms 304 diagnostically challenging New England Journal of Medic…

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   Advancing conversational diagnostic AI with multimodal reasoning

   Khaled Saab, Chunjong Park, Tim Strother et al. · 2026 · Nature Medicine · cited 9 times · Not summarized yet

   Abstract

   Real-world clinical practice is inherently multimodal, relying on the synthesis of patient history with visual information such as medical imagery and clinical documents. Although large language models (LLMs) have shown promise in diagnostic dialogue, their evaluation has been largely restricted to text-only interactions, failing to capture the complexity of modern remote care delivery. Here we introduce a multimodal extension of the Articulate Medical Intelligence Explorer (multimodal AMIE), capable of gathering, interpreting and reasoning about multimodal data within a diagnostic conversation. To achieve this, we developed a state-aware dialogue framework that dynamically guides history-ta…

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   Evaluating Large Language Model Diagnostic Performance on JAMA Clinical Challenges via a Multi-Agent Conversational Framework

   K. L. Sangwon, J. Zhang, R. Steele et al. · 2025 · medRxiv · preprint, not peer reviewed · cited 3 times · Not summarized yet

   Abstract

   Background & Objective: Standard clinical LLM benchmarks use multiple-choice vignettes that present all information up front, unlike real encounters where clinicians iteratively elicit histories and objective data. We hypothesized that such formats inflate LLM performance and mask weaknesses in diagnostic reasoning. We developed and evaluated a multi-AI agent conversational framework that converts JAMA Clinical Challenge cases into multi-turn dialogues, and assessed its impact on diagnostic accuracy across frontier LLMs. Methods: We adapted 815 diagnostic cases from 1,519 JAMA Clinical Challenges into two formats: (1) original vignette and (2) multi-agent conversation with a Patient AI (subj…

8. Very close Not relevant

   Towards conversational artificial intelligence for disease management

   Valentin Liévin, Anil Palepu, Wei-Hung Weng et al. · 2026 · Nature · cited 18 times · Not summarized yet

   Abstract

   Although large language models have shown promise in diagnostic dialogue1, their capabilities for effective management reasoning, including disease progression, therapeutic response and safe medication prescription, have remained underexplored. We have advanced the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE)1, 2–3 using a new large-language-model-based agentic system optimized for multivisit clinical management and dialogue. To ground the reasoning of AMIE in authoritative clinical knowledge, we leveraged the long-context capabilities of Gemini4, combining in-context retrieval with structured reasoning to align its output with up-to-…

Prior work it builds on

1. Close Used in depth by this paper A method it uses Not relevant

   Towards Expert-Level Medical Question Answering with Large Language Models

   Karan Singhal, Tao Tu, Juraj Gottweis et al. · 2023 · Computation and Language · preprint, not peer reviewed · cited 838 times · AI summary available

   Abstract

   Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67.2% on the MedQA dataset. However, this and other prior work suggested significant room for improvement, especially when models' answers were compared to clinicians' answers. He…

2. Very close Cites this paper Not relevant

   Towards accurate differential diagnosis with large language models

   Daniel McDuff, Mike Schaekermann, Tao Tu et al. · 2023 · Nature · cited 443 times · Not summarized yet

   Abstract

   A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist and automate aspects of this process1. Here we introduce the Articulate Medical Intelligence Explorer (AMIE), a large language model that is optimized for diagnostic reasoning, and evaluate its ability to generate a differential diagnosis alone or as an aid to clinicians. Twenty clinicians evaluated 302 challenging, real-world medical cases sourced from published case reports. Ea…

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   Large language models encode clinical knowledge

   K. Singhal, Shekoofeh Azizi, Tao Tu et al. · 2022 · Nature · cited 5,770 times · Not summarized yet

   Abstract

   Med-PaLM, a state-of-the-art large language model for medicine, is introduced and evaluated across several medical question answering tasks, demonstrating the promise of these models in this domain. Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQ…

4. Close A method it uses Not relevant

   Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation

   Usman Naseem, A. Bandi, Shaina Raza et al. · 2022 · Workshop on Biomedical Natural Language Processing · cited 19 times · Not summarized yet

   Abstract

   Medical dialogue systems have the potential to assist doctors in expanding access to medical care, improving the quality of patient experiences, and lowering medical expenses. The computational methods are still in their early stages and are not ready for widespread application despite their great potential. Existing transformer-based language models have shown promising results but lack domain-specific knowledge. However, to diagnose like doctors, an automatic medical diagnosis necessitates more stringent requirements for the rationality of the dialogue in the context of relevant knowledge. In this study, we propose a new method that addresses the challenges of medical dialogue generation b…

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   Diagnostic Accuracy of Artificial Intelligence in Virtual Primary Care

   Dan Zeltzer, Lee Herzog, Yishai Pickman et al. · 2023 · Mayo Clinic proceedings. Digital health · cited 34 times · Not summarized yet

   Abstract

   Objective To evaluate the diagnostic accuracy of artificial intelligence (AI)-generated clinical diagnoses. Patients and Methods A retrospective chart review of 102,059 virtual primary care clinical encounters from October 1, 2022, to January 31, 2023 was conducted. Patients underwent an AI medical interview, after which virtual care providers reviewed the interview summary and AI-provided differential diagnoses, communicated with patients, and finalized diagnoses and treatment plans. Our accuracy measures were agreement between AI diagnoses, virtual care providers, and blind adjudicators. We analyzed AI diagnostic agreement across different diagnoses, presenting symptoms, patient demographi…

Papers that cite it

1. Very close Uses this paper in depth Not relevant

   AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Data

   Sina Rashidian, Nan Li, Jonathan Amar et al. · 2025 · arXiv.org · preprint, not peer reviewed · cited 7 times · Not summarized yet

   Abstract

   Background: We present a Patient Simulator that leverages real world patient encounters which cover a broad range of conditions and symptoms to provide synthetic test subjects for development and testing of healthcare agentic models. The simulator provides a realistic approach to patient presentation and multi-turn conversation with a symptom-checking agent. Objectives: (1) To construct and instantiate a Patient Simulator to train and test an AI health agent, based on patient vignettes derived from real EHR data. (2) To test the validity and alignment of the simulated encounters provided by the Patient Simulator to expert human clinical providers. (3) To illustrate the evaluation framework o…

2. Close Uses this paper in depth Not relevant

   Conversational Disease Diagnosis via External Planner-Controlled Large Language Models

   Zhoujian Sun, Cheng Luo, Zi-Yi Liu et al. · 2024 · arXiv.org · preprint, not peer reviewed · cited 10 times · Not summarized yet

   Abstract

   The development of large language models (LLMs) has brought unprecedented possibilities for artificial intelligence (AI) based medical diagnosis. However, the application perspective of LLMs in real diagnostic scenarios is still unclear because they are not adept at collecting patient data proactively. This study presents a LLM-based diagnostic system that enhances planning capabilities by emulating doctors. Our system involves two external planners to handle planning tasks. The first planner employs a reinforcement learning approach to formulate disease screening questions and conduct initial diagnoses. The second planner uses LLMs to parse medical guidelines and conduct differential diagno…

3. Very close Not relevant

   Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator

   Yusheng Liao, Yu-Tong Meng, Yu-Hao Wang et al. · 2024 · arXiv.org · preprint, not peer reviewed · cited 52 times · Not summarized yet

   Abstract

   Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored. Previous works mainly focus on the performance of medical knowledge with examinations, which is far from the realistic scenarios, falling short in assessing the abilities of LLMs on clinical tasks. In the quest to enhance the application of Large Language Models (LLMs) in healthcare, this paper introduces the Automated Interactive Evaluation (AIE) framework and the State-Aware Patient Simulator (SAPS), targeting the gap between traditional LLM evaluations and the nuanced demands of clinical practice. Unlike prior methods …

4. Close Uses this paper in depth Not relevant

   Generative Artificial Intelligence in Mental Healthcare: An Ethical Evaluation

   C. Blease, A. Rodman · 2024 · Current Treatment Options in Psychiatry · cited 53 times · Not summarized yet

   Abstract

   Since November 2022, generative artificial intelligence (AI) chatbots, such as ChatGPT, that are powered by large language models (LLM) have been the subject of growing attention in healthcare. Using biomedical ethical principles to frame our discussion, this review seeks to clarify the current ethical implications of these chatbots, and to identify the key empirical questions that should be pursued to inform ethical practice. In the past two years, research has been conducted into the capacity of generative AI chatbots to pass medical school examinations, evaluate complex diagnostic cases, solicit patient histories, interpret and summarize clinical documentation, and deliver empathic care. …

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

   Samuel Schmidgall, Rojin Ziaei, Carl Harris et al. · 2024 · arXiv.org · preprint, not peer reviewed · cited 286 times · Not summarized yet

   Abstract

   Evaluating large language models (LLM) in clinical scenarios is crucial to assessing their potential clinical utility. Existing benchmarks rely heavily on static question-answering, which does not accurately depict the complex, sequential nature of clinical decision-making. Here, we introduce AgentClinic, a multimodal agent benchmark for evaluating LLMs in simulated clinical environments that include patient interactions, multimodal data collection under incomplete information, and the usage of various tools, resulting in an in-depth evaluation across nine medical specialties and seven languages. We find that solving MedQA problems in the sequential decision-making format of AgentClinic is c…