The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.
TL;DR
It is suggested that the reliability of health care LLM reliability is difficult to evaluate adequately using a single universal standard, and future evaluations of health care LLM reliability need to be guided by standardized evaluation frameworks that reflect domain-specific contexts.
Abstract
Background
Integration of large language models (LLMs) into health care has accelerated rapidly, yet reliability concerns pose potential risks to patient safety. Although human evaluation has been widely used as an important approach for assessing LLM reliability, a systematic understanding of how such evaluations have been operationalized across studies remains limited.
Objective
This study aimed to characterize the current landscape of human evaluation frameworks for LLM reliability in health care and to identify similarities and differences between the clinical and public health domains.
Methods
In line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, PubMed, Web of Science, the Cochrane Library, CINAHL, and Google Scholar were searched for studies published from January 2016 to July 2025. Eligible studies were English-language original research conducted in health care settings that assessed the reliability of LLM-generated responses through human evaluation. Key exclusion criteria were studies without human evaluation and studies focused primarily on LLM model selection, performance optimization, or technical development. Extracted data were analyzed across 3 dimensions: what was evaluated, who evaluated, and how evaluation was conducted. Reported methodological limitations were also categorized and compared between the clinical and public health domains.
Results
Of the 4347 records identified, 71 studies were included in the final analysis (clinical, n=26; public health, n=45). Six reliability indicators were used: accuracy, relevance, completeness, clarity, safety, and consistency. The clinical domain more frequently assessed guideline concordance, internal consistency, and structural coherence, whereas the public health domain more frequently assessed understandability, harm potential, and repeat response consistency. Single-specialty clinicians were the most common evaluators in both domains, although mixed evaluator panels were observed only in the public health domain. Evaluator panels generally consisted of 5 or fewer members. Five-point Likert scales and researcher-defined rubrics were commonly used evaluation approaches in both domains. Key methodological limitations included evaluator subjectivity, nonstandardized indicators, and limited evaluation scope and settings.
Conclusions
To our knowledge, this is the first review to systematically examine how human evaluations of LLM reliability have been conducted across health care. The focus of reliability evaluation differed across domains, with clinical evaluations giving relatively greater attention to clinical validity and logical rigor, whereas public health evaluations gave relatively greater attention to understandability, practical use, and safe use. These differences suggest that the reliability of health care LLMs is difficult to evaluate adequately using a single universal standard. In addition, the methodological limitations identified in this review indicate that current human evaluation approaches are insufficiently standardized. Therefore, future evaluations of health care LLM reliability need to be guided by standardized evaluation frameworks that reflect domain-specific contexts and encompass indicator definitions, judgment criteria, evaluator guidance, and evaluation procedures.