Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation

• URL: http://arxiv.org/abs/2409.01941v1
• Date: Tue, 3 Sep 2024 14:38:29 GMT
• Title: Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation
• Authors: Jack Krolik, Herprit Mahal, Feroz Ahmad, Gaurav Trivedi, Bahador Saket,
• Abstract summary: This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q&A) systems.
• Score: 2.7379431425414693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q\&A) systems, a crucial form of Natural Language Processing. Traditionally, human evaluation has been indispensable for assessing the quality of these responses. However, manual evaluation by medical professionals is time-consuming and costly. Our study examines whether LLMs can reliably replicate human evaluations by using questions derived from patient data, thereby saving valuable time for medical experts. While the findings suggest promising results, further research is needed to address more specific or complex questions that were beyond the scope of this initial investigation.

Related papers

• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering [5.065947993017158]
  Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. We examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews.
  arXiv  Detail & Related papers  (2024-06-09T16:33:28Z)
• MedLM: Exploring Language Models for Medical Question Answering Systems [2.84801080855027]
  Large Language Models (LLMs) with their advanced generative capabilities have shown promise in various NLP tasks. This study aims to compare the performance of general and medical-specific distilled LMs for medical Q&A. The findings will provide valuable insights into the suitability of different LMs for specific applications in the medical domain.
  arXiv  Detail & Related papers  (2024-01-21T03:37:47Z)
• A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis [87.25494411021066]
  GPT-4V's multimodal capability for medical image analysis is evaluated. It is found that GPT-4V excels in understanding medical images and generates high-quality radiology reports. It is found that its performance for medical visual grounding needs to be substantially improved.
  arXiv  Detail & Related papers  (2023-10-31T11:39:09Z)
• Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors [27.13497855061732]
  This paper evaluates quality estimation feedback in vivo with a human study simulating decision-making in high-stakes medical settings. We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses.
  arXiv  Detail & Related papers  (2023-10-25T18:44:14Z)
• 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)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation [58.54483567073125]
  We propose a protocol that aims to increase objectivity by grounding evaluations in Consultation Checklists. We observed good levels of inter-annotator agreement in a first evaluation study using the protocol.
  arXiv  Detail & Related papers  (2022-11-17T10:54:28Z)
• Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation [56.25869366777579]
  In recent years, machine learning models have rapidly become better at generating clinical consultation notes. We present an extensive human evaluation study where 5 clinicians listen to 57 mock consultations, write their own notes, post-edit a number of automatically generated notes, and extract all the errors. We find that a simple, character-based Levenshtein distance metric performs on par if not better than common model-based metrics like BertScore.
  arXiv  Detail & Related papers  (2022-04-01T14:04:16Z)
• Question-Driven Summarization of Answers to Consumer Health Questions [17.732729654047983]
  We present the MEDIQA Answer Summarization dataset. This dataset is the first summarization collection containing question-driven summaries of answers to consumer health questions. We include results of baseline and state-of-the-art deep learning summarization models.
  arXiv  Detail & Related papers  (2020-05-18T20:36:11Z)
• Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment [64.52563354823711]
  Rehabilitation assessment is critical to determine an adequate intervention for a patient. Current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. We developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-27T17:04:07Z)

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.