ArticleNPJ digital medicine2025
Evaluating clinical AI summaries with large language models as judges.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Validating LLM judges for automated oversight of patient communication.medRxiv : the preprint server for health sciences · 2026Article
- What Platform Scores Miss: Multidimensional Evaluation of AI Teaching Agents in Medical Education.JMIR medical education · 2026Article
- Bridging primary care with artificial intelligence: toward a global 1.5-Tier Healthcare System.npj health systems · 2026Review
- Context-informed personalized segmentation from radiology reports: A fully automated framework for long-term liver tumor follow-up.Medical physics · 2026Article
- Clinical AI Generators and Reviewers must be Tested Together.Journal of medical systems · 2026Article
- A Supervised Fine-Tuned Large Language Model for Lifestyle Management in Patients With Prostate Cancer: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- PAUSE-Agents: A Clinician-in-the-Loop Multi-Agent AI Pipeline for ICU-to-Ward Handoff Briefs.medRxiv : the preprint server for health sciences · 2026Article
- LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- Article
- When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare.NPJ digital medicine · 2026Article
- Expert Evaluation and Consensus on GPT-4o Summaries of Clinical Letters: Validation and Results of the Framework and Implementation of AI Tools Project.JMIR medical informatics · 2026Article
- Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study.JMIR formative research · 2026Article
- Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.Journal of medical Internet research · 2026Article
- Comparative Analysis of Japanese Clinical Note Styles Between Physicians and Large Language Models Using Identical Psychiatric Cases: Quantitative Text Analysis.JMIR formative research · 2026Article
- EchoAtlas: A Conversational, Multi-View Vision-Language Foundation Model for Echocardiography Interpretation and Clinical Reasoning.medRxiv : the preprint server for health sciences · 2026Article
- Personalised health plan development using agentic AI in Singapore's national preventive care programme: a pilot study.NPJ digital medicine · 2026Article
- An LLM-assisted framework for accelerated and verifiable clinical hypothesis testing from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Unmet Needs and Service Priorities for ADHD in Australia: AI-Assisted Analysis of Senate Inquiry Submissions.International journal of environmental research and public health · 2026Article
- Artificial Authority: The Promise and Perils of LLM Judges in Healthcare.Bioengineering (Basel, Switzerland) · 2026Review
- Scaling Biomedical Knowledge Graph Retrieval for Interpretable Reasoning: Applications to Clinical Diagnosis Prediction.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Electronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires reliable evaluation. Human review is the gold standard but is costly and slow. To address this, we introduce and validate an automated LLM-based method to assess real-world EHR multi-document summaries. Benchmarking against the validated Provider Documentation Summarization Quality Instrument (PDSQI), our LLM-as-a-Judge framework demonstrated strong inter-rater reliability with human evaluators. GPT-o3-mini achieved an intraclass correlation coefficient of 0.818 (95% CI 0.772-0.854), a median score difference of 0 from humans, and completed evaluations in 22 seconds. Overall, reasoning models excelled in inter-rater reliability, particularly for evaluations requiring advanced reasoning and domain expertise, outperforming non-reasoning, task-trained, and multi-agent approaches. By automating high-quality evaluations, a medical LLM-as-a-Judge provides a scalable, efficient way to identify accurate, safe AI-generated clinical summaries.
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.