Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance

arXiv:2606.18613 · cs.CL, cs.AI · Submitted 2026-06-17 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-06-17

Updated: 2026-08-31

Code: https://github.com/qgpmztmf/PhysAssistBench

Terminology

• Benchmark — 57× in this paper · explained in 2 episodes
• Clinical Reasoning — 20× in this paper
• Information Lookup — 17× in this paper
• Data Gathering — 16× in this paper
• Nominal Anaphora — 11× in this paper
• Predicate Ellipsis — 11× in this paper
• Abstract Event Anaphora — 6× in this paper
• Adam — 5× in this paper · explained in 2 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Cooperation — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Beth Israel Deaconess Medical Center — 1× in this paper
• Commentary — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 5 episodes
• Partially observable Markov decision process (POMDP) — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Text-to-SQL — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM
• GLM-5: from Vibe Coding to Agentic Engineering
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Kimi K2: Open Agentic Intelligence
• Seed1.8 Model Card: Towards Generalized Real-World Agency
• ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
• EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries
• LLMs Get Lost In Multi-Turn Conversation
• FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answering
• ER-Reason: A Benchmark Dataset for LLM Clinical Reasoning in the Emergency Room
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
• Qwen3 Technical Report
• Qwen3.5-Omni Technical Report
• RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game
• OpenAI GPT-5 System Card
• Benchmarking LLM Tool-Use in the Wild
• Towards Expert-Level Medical Question Answering with Large Language Models
• MedicalAgentsBench for Complex Medical Reasoning: Comparing Internalized Reasoning Models versus Externalized Agent-based Frameworks

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