Reviewnpj health systems2025
Stakeholder-centric participation in large language models enhanced health systems.
Review 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 5 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
5 citing papers in PubMed.
- Validating LLM judges for automated oversight of patient communication.medRxiv : the preprint server for health sciences · 2026Article
- Patients' and Providers' Attitudes Toward Artificial Intelligence and Electronic Health Record Use in Deep Phenotyping and Rare-Disease Screening: An Empty Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Emotion-Adaptive Large Language Model-Driven Clinical Decision Support: User Evaluation of the Empathic Clinical Decision Support System Framework for Trust and Explainability.JMIR human factors · 2026Article
- Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Implementing artificial intelligence (AI)-supported communication tools in healthcare: System-level perspectives.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Large language models (LLMs) are transforming healthcare by advancing clinical decision support, patient care, and administrative efficiency. However, effectively and sustainably integrating LLMs into healthcare systems requires addressing participatory gaps that may hinder alignment with stakeholders' practical and ethical needs. This paper explores how participatory methods can be applied throughout the development lifecycle of LLM-enhanced health systems (LLMHS), arguing that: (1) participatory approaches are critical for engaging stakeholders in LLMHS development, and (2) LLM techniques can create novel participatory opportunities that reinforce stakeholder engagement while driving technical innovation in LLMHS. This dual perspective highlights the potential of LLMHS to align technical sophistication with real-world healthcare demands, paving the way for next-generation health systems.
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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.