Summary of Medic: Towards a Comprehensive Framework For Evaluating Llms in Clinical Applications, by Praveen K Kanithi et al.
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications
by Praveen K Kanithi, Clément Christophe, Marco AF Pimentel, Tathagata Raha, Nada Saadi, Hamza Javed, Svetlana Maslenkova, Nasir Hayat, Ronnie Rajan, Shadab Khan
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid development of Large Language Models (LLMs) has led to a need for comprehensive evaluation frameworks beyond traditional benchmarks. The MEDIC framework assesses LLMs across five critical dimensions of clinical competence, including medical reasoning, ethics and bias, data and language understanding, in-context learning, and clinical safety. MEDIC features a novel cross-examination framework that quantifies LLM performance across areas like coverage and hallucination detection without requiring reference outputs. The authors apply MEDIC to evaluate LLMs on various tasks such as medical question-answering, safety, summarization, note generation, and others. Results show performance disparities between model sizes, baseline vs medically finetuned models, highlighting the importance of evaluating LLMs comprehensively for specific clinical applications. The paper’s findings have implications on model selection for healthcare settings, ensuring that the most promising models are identified and adapted for diverse applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how to evaluate Large Language Models (LLMs) in a way that’s relevant to real-world use in healthcare. Right now, people are just looking at LLMs’ performance on specific tests, but this isn’t enough because the models change so quickly. The researchers created a new evaluation framework called MEDIC, which looks at five important areas where LLMs need to perform well: being able to reason medically, avoiding biased or unfair decisions, understanding medical data and language, learning from real-world situations, and keeping patients safe. They tested their framework on various tasks like answering medical questions, making sure information is accurate, summarizing text, generating notes, and more. The results show that different models perform better in different areas, which is important for choosing the right model for a specific healthcare application. |
Keywords
» Artificial intelligence » Hallucination » Language understanding » Question answering » Summarization
