Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
arXiv:2606.18613v1 Announce Type: cross Abstract: The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR system interaction, or patient communication. Physician assistance instead requires coordinating these capabilities within the same interaction, where physicians issue underspecified requests, patients describe symptoms ambiguously, and EHR systems demand precise tool use. We introduce PhysAssistBench, a benchmark for interactive doctor-patient-EHR assistance. Built from
The rapid advancement of large language models (LLMs) over the past several years has brought them to a point where their practical application in complex, high-stakes fields like medicine is becoming feasible, necessitating robust evaluation benchmarks.
This benchmark signifies a critical step towards safely and effectively integrating AI into healthcare, moving beyond isolated capabilities to evaluate LLMs in realistic, interactive clinical workflows, which directly impacts patient care and physician efficiency.
The evaluation of medical LLMs will shift from assessing individual functions to comprehensively testing their ability to coordinate multiple complex tasks in dynamic, interactive healthcare scenarios, thereby accelerating appropriate deployment.
- · Healthcare AI developers
- · Patients (through improved care)
- · Hospitals and clinics
- · AI ethics and safety researchers
- · AI models lacking robust integration capabilities
- · Developers focused solely on single-task clinical AI
- · Systems unable to adapt to interactive environments
Physicians will gain new assistant tools that can navigate complex patient interactions and EHR systems more effectively.
This improved assistance could lead to reduced physician burnout and enhanced diagnostic accuracy, lowering healthcare costs and improving patient outcomes.
The success of such benchmarks might accelerate the development of similar interactive, multi-modal AI assistants across other professional domains, leading to widespread white-collar automation.
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Read at arXiv cs.AI