Faculty, Staff and Student Publications
Publication Date
9-28-2024
Journal
npj Digital Medicine
Abstract
With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we propose QUEST, a comprehensive and practical framework for human evaluation of LLMs covering three phases of workflow: Planning, Implementation and Adjudication, and Scoring and Review. QUEST is designed with five proposed evaluation principles: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence.
Keywords
Health care, Medical research
DOI
10.1038/s41746-024-01258-7
PMID
39333376
PMCID
PMC11437138
PubMedCentral® Posted Date
9-28-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Biomedical Informatics Commons, Data Science Commons, Health Services Research Commons
