A new evaluation framework called MEDIC has been developed to assess the safety and utility of Large Language Models (LLMs) in clinical settings. This framework moves beyond traditional licensing exam benchmarks to evaluate functional capabilities across five dimensions, including deterministic execution and a novel Cross-Examination Framework (CEF) for quantifying information fidelity and hallucination rates. The evaluation revealed a significant gap between LLMs' knowledge retrieval abilities and their performance on operational tasks like clinical calculations or SQL generation. Additionally, models optimized for high refusal rates did not consistently demonstrate active safety by reliably detecting errors in clinical documentation, indicating a need for a portfolio approach to deploying LLMs in healthcare. AI
IMPACT This framework could guide the development and deployment of safer and more effective LLMs in healthcare by highlighting critical performance gaps.
RANK_REASON This is a research paper detailing a new evaluation framework for LLMs in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- Cross-Examination Framework
- DagsHub
- Gotit.pub
- Hugging Face
- Large Language Models
- LLMs
- Praveenkumar Kanithi
- ScienceCast
- SQL
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