EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
arXiv:2605.30637v1 Announce Type: new Abstract: Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence. LLMs are increasingly used to support these decisions due to strong language capabilities, broad biomedical knowledge, and efficiency, yet the reliability of LLMs on real-world clinical decision tasks remains insufficiently understood. To evaluate CDM models, especially LLM-based models, an ideal and practical medical decision benchmark should be constructe
The proliferation of LLMs in various domains, including healthcare, necessitates robust and reliable evaluation benchmarks to ensure their safe and effective deployment.
A reliable benchmark for clinical decision-making with LLMs is crucial for differentiating effective and safe AI tools from unreliable ones, impacting patient care and regulatory frameworks.
The introduction of EHRBench provides a more standardized and rigorous method for evaluating LLMs in clinical contexts, potentially accelerating their adoption while improving safety and efficacy.
- · AI developers in healthcare
- · Healthcare providers adopting AI
- · Patients through improved care
- · Unreliable AI models
- · Healthcare systems slow to adopt AI
This benchmark helps validate specific LLM applications for clinical decision making.
Validated LLMs could lead to widespread integration into electronic health records and clinical workflows, transforming medical practice.
The enhanced decision support capabilities could eventually lead to improved patient outcomes and more efficient healthcare resource allocation.
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Read at arXiv cs.AI