Articlenpj health systems2025
Human evaluation of large language models in healthcare: gaps, challenges, and the need for standardization.
Article in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Validating LLM judges for automated oversight of patient communication.medRxiv : the preprint server for health sciences · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- "MELMA" in otolaryngology: Medical evaluation of large language model answers. Clinician-rated scoring (MELMA-Q) and web-based auditing (MELMA-W) novel tools for AI assessment.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Assessing pediatricians' readiness for artificial intelligence: a cross-sectional study in Istanbul, Türki̇ye.BMC pediatrics · 2026Article
- Comparative performance of recent and prior large language models and pediatric residents on pediatric in-training examination questions.Scientific reports · 2026Article
- Creating customized chatbots with ChatGPT to promote physical activity: a mini review.Frontiers in digital health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Publications related to experimentation with Large Language Models (LLMs) in healthcare are rapidly increasing. While human evaluation remains the gold standard for evaluating LLMs, there is still a lack of standardization in its implementation. In this review article, we systematically examine studies involving LLMs in healthcare that have conducted human evaluations. We analyze the metrics used, assess their variability across studies. We also propose a standardized framework along with an interactive open web application HumanELY, to facilitate human evaluation. We believe that use of HumanELY will provide an opportunity for consistent, comprehensive, reliable, reproducible, and measurable human evaluations of LLM in healthcare. HumanELY is publicly available at https://www.brainxai.com/humanely .
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.