Towards physician-centered oversight of conversational diagnostic AI
- URL: http://arxiv.org/abs/2507.15743v1
- Date: Mon, 21 Jul 2025 15:54:36 GMT
- Title: Towards physician-centered oversight of conversational diagnostic AI
- Authors: Elahe Vedadi, David Barrett, Natalie Harris, Ellery Wulczyn, Shashir Reddy, Roma Ruparel, Mike Schaekermann, Tim Strother, Ryutaro Tanno, Yash Sharma, Jihyeon Lee, Cían Hughes, Dylan Slack, Anil Palepu, Jan Freyberg, Khaled Saab, Valentin Liévin, Wei-Hung Weng, Tao Tu, Yun Liu, Nenad Tomasev, Kavita Kulkarni, S. Sara Mahdavi, Kelvin Guu, Joëlle Barral, Dale R. Webster, James Manyika, Avinatan Hassidim, Katherine Chou, Yossi Matias, Pushmeet Kohli, Adam Rodman, Vivek Natarajan, Alan Karthikesalingam, David Stutz,
- Abstract summary: Real-world assurance of patient safety means that providing individual diagnoses and treatment plans is considered a regulated activity by licensed professionals.<n>Inspired by this, we propose a framework for effective, asynchronous oversight of the Articulate Medical Intelligence Explorer (AMIE) AI system.
- Score: 40.583050959984995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent work has demonstrated the promise of conversational AI systems for diagnostic dialogue. However, real-world assurance of patient safety means that providing individual diagnoses and treatment plans is considered a regulated activity by licensed professionals. Furthermore, physicians commonly oversee other team members in such activities, including nurse practitioners (NPs) or physician assistants/associates (PAs). Inspired by this, we propose a framework for effective, asynchronous oversight of the Articulate Medical Intelligence Explorer (AMIE) AI system. We propose guardrailed-AMIE (g-AMIE), a multi-agent system that performs history taking within guardrails, abstaining from individualized medical advice. Afterwards, g-AMIE conveys assessments to an overseeing primary care physician (PCP) in a clinician cockpit interface. The PCP provides oversight and retains accountability of the clinical decision. This effectively decouples oversight from intake and can thus happen asynchronously. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) of text consultations with asynchronous oversight, we compared g-AMIE to NPs/PAs or a group of PCPs under the same guardrails. Across 60 scenarios, g-AMIE outperformed both groups in performing high-quality intake, summarizing cases, and proposing diagnoses and management plans for the overseeing PCP to review. This resulted in higher quality composite decisions. PCP oversight of g-AMIE was also more time-efficient than standalone PCP consultations in prior work. While our study does not replicate existing clinical practices and likely underestimates clinicians' capabilities, our results demonstrate the promise of asynchronous oversight as a feasible paradigm for diagnostic AI systems to operate under expert human oversight for enhancing real-world care.
Related papers
- Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation [9.84660526673816]
This study investigated the feasibility and value of using a Large Language Model (LLM)-based multi-agent system for safer therapy recommendations.<n>We designed a single agent and a MAS framework simulating multidisciplinary team (MDT) decision-making.<n>We compared MAS performance with single-agent approaches and real-world benchmarks.
arXiv Detail & Related papers (2025-07-15T02:01:38Z) - Toward the Autonomous AI Doctor: Quantitative Benchmarking of an Autonomous Agentic AI Versus Board-Certified Clinicians in a Real World Setting [0.0]
Globally we face a projected shortage of 11 million healthcare practitioners by 2030.<n>No end-to-end autonomous large language model (LLM)-based AI system has been rigorously evaluated in real-world clinical practice.
arXiv Detail & Related papers (2025-06-27T19:04:44Z) - Advancing Conversational Diagnostic AI with Multimodal Reasoning [44.1996223689966]
Articulate Medical Intelligence Explorer (AMIE)<n>System implements a state-aware dialogue framework, where conversation flow is dynamically controlled by intermediate model outputs.<n>We compared AMIE to primary care physicians (PCPs) in a randomized, blinded, OSCE-style study of chat-based consultations with patient actors.
arXiv Detail & Related papers (2025-05-06T20:52:01Z) - TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews [54.35097932763878]
Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data.<n>Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis framework using Multi-Agent LLMs for clinical interviews.<n>We demonstrate that TAMA outperforms existing LLM-assisted TA approaches, achieving higher thematic hit rate, coverage, and distinctiveness.
arXiv Detail & Related papers (2025-03-26T15:58:16Z) - Towards Conversational AI for Disease Management [29.189384095061722]
Articulate Medical Intelligence Explorer (AMIE) is an agentic system optimised for clinical management and dialogue.<n>AMIE is non-inferior to PCPs in management reasoning as assessed by specialist physicians.<n>AMIE's strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management.
arXiv Detail & Related papers (2025-03-08T05:48:58Z) - Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
arXiv Detail & Related papers (2024-11-16T18:19:53Z) - RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules.
We develop a medical dialogue dataset comprising rule-based communications between patients and physicians.
Experimental results demonstrate the effectiveness of the proposed approach.
arXiv Detail & Related papers (2024-08-22T17:44:40Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - Towards Conversational Diagnostic AI [32.84876349808714]
We introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue.
AMIE uses a self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions.
AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors.
arXiv Detail & Related papers (2024-01-11T04:25:06Z) - A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks
and Datasets [70.32630628211803]
We propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction.
A new large medical dialogue dataset with multi-level fine-grained annotations is introduced.
We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.
arXiv Detail & Related papers (2022-04-19T16:43:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.