Med-CoDE: Medical Critique based Disagreement Evaluation Framework

• URL: http://arxiv.org/abs/2504.15330v1
• Date: Mon, 21 Apr 2025 16:51:11 GMT
• Title: Med-CoDE: Medical Critique based Disagreement Evaluation Framework
• Authors: Mohit Gupta, Akiko Aizawa, Rajiv Ratn Shah,
• Abstract summary: The reliability and accuracy of large language models (LLMs) in medical contexts remain critical concerns.<n>Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance.<n>We propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges.
• Score: 72.42301910238861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of large language models (LLMs) has significantly influenced numerous fields, including healthcare, by enhancing the capabilities of automated systems to process and generate human-like text. However, despite their advancements, the reliability and accuracy of LLMs in medical contexts remain critical concerns. Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance, leading to potential risks in clinical settings. In this work, we propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges. The framework leverages a critique-based approach to quantitatively measure the degree of disagreement between model-generated responses and established medical ground truths. This framework captures both accuracy and reliability in medical settings. The proposed evaluation framework aims to fill the existing gap in LLM assessment by offering a systematic method to evaluate the quality and trustworthiness of medical LLMs. Through extensive experiments and case studies, we illustrate the practicality of our framework in providing a comprehensive and reliable evaluation of medical LLMs.

Related papers

• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• A Mixed-Methods Evaluation of LLM-Based Chatbots for Menopause [7.156867036177255]
  The integration of Large Language Models (LLMs) into healthcare settings has gained significant attention.<n>We examine the performance of publicly available LLM-based chatbots for menopause-related queries.<n>Our findings highlight the promise and limitations of traditional evaluation metrics for sensitive health topics.
  arXiv  Detail & Related papers  (2025-02-05T19:56:52Z)
• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios [50.032101237019205]
  CliMedBench is a comprehensive benchmark with 14 expert-guided core clinical scenarios. The reliability of this benchmark has been confirmed in several ways.
  arXiv  Detail & Related papers  (2024-10-04T15:15:36Z)
• MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications [2.838746648891565]
  We introduce MEDIC, a framework assessing Large Language Models (LLMs) across five critical dimensions of clinical competence. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks. Results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths.
  arXiv  Detail & Related papers  (2024-09-11T14:44:51Z)
• Evaluating large language models in medical applications: a survey [1.5923327069574245]
  Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains. evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information.
  arXiv  Detail & Related papers  (2024-05-13T05:08:33Z)
• Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm [15.627870862369784]
  Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis. We propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services.
  arXiv  Detail & Related papers  (2024-03-25T06:17:54Z)
• An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models [22.409334091186995]
  Large language models (LLMs) often suffer from hallucinations, leading to overly confident but incorrect judgments. This paper introduces an automated evaluation framework that assesses the practical capabilities of LLMs as virtual doctors during multi-turn consultations.
  arXiv  Detail & Related papers  (2023-09-05T09:24:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.