ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.Frontiers in digital health · 2026Pooled it
- Validating LLM judges for automated oversight of patient communication.medRxiv : the preprint server for health sciences · 2026Article
- Key aspects of fine-tuning and applying LLM-as-a-judge for clinical data summaries in the radiological workflow.Frontiers in artificial intelligence · 2026Article
- Evaluating clinical AI summaries with large language models as judges.NPJ digital medicine · 2025Article
- Toward digital twins in the intensive care unit: a medication management case study.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Development and validation of the provider documentation summarization quality instrument for large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge.medRxiv : the preprint server for health sciences · 2025Article
- Current and future state of evaluation of large language models for medical summarization tasks.npj health systems · 2025Article
- On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.Journal of biomedical informatics · 2024Article
Corrections and comments
- Update of
Authors and funding
9 authors.
Funding
Abstract
In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automated evaluation metrics in NLG in healthcare. To have a robust and well-validated baseline with which to examine the alignment of these metrics, we created a comprehensive human evaluation framework. Employing ChatGPT-3.5-turbo generative output, we correlated human judgments with each metric. None of the metrics demonstrated high alignment; however, the SapBERT score-a Unified Medical Language System (UMLS)- showed the best results. This underscores the importance of incorporating domain-specific knowledge into evaluation efforts. Our work reveals the deficiency in quality evaluations for generated text and introduces our comprehensive human evaluation framework as a baseline. Future efforts should prioritize integrating medical knowledge databases to enhance the alignment of automated metrics, particularly focusing on refining the SapBERT score for improved assessments.
Indexed as
Identifiers
40417585PMC12099413What 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.