AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows
arXiv:2606.17474v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for use in clinical consultation tasks, yet most medical evaluations remain static, single-turn, or narrowly outcome-based, limiting their ability to reflect the sequential, uncertain, and interactive nature of real-world care. Here, we propose AIPatient Arena, an EHRs-grounded evaluation framework for assessing the clinical utility of LLMs across eight dimensions of clinical competence. The framework integrates EHR data into patient-specific knowledge graphs, enabling multi-turn physician-
The rapid advancement of large language models (LLMs) is pushing their application into sensitive domains like healthcare, necessitating robust and comprehensive evaluation frameworks.
This framework addresses a critical gap in LLM evaluation by focusing on real-world clinical consultation workflows, moving beyond static tests to assess practical utility and patient safety.
The development of an EHR-grounded evaluation framework allows for more realistic and multi-dimensional assessment of LLMs in clinical settings, potentially accelerating their trusted integration into healthcare.
- · AI developers in healthcare
- · Healthcare providers
- · Patients
- · Medical AI research institutions
- · LLM developers ignoring clinical validation
- · Traditional medical software companies slow to adapt AI
Refined LLMs with stronger clinical competence due to rigorous evaluation.
Increased trust and adoption of AI assistants in medical diagnosis and treatment planning.
Transformation of medical education and clinical practice with AI becoming an integral part of the healthcare workflow and decision-making.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL