ArticleJournal of patient-reported outcomes2025
A new generation of patient-reported outcome measures with large language models.
Article in Journal of patient-reported outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Leveraging large language models in patient-reported outcome measure development: practical opportunities, cautions, and a human-in-the-loop roadmap.Journal of patient-reported outcomes · 2026Article
- Patient-reported outcome measures in lumbar disc herniation: a scoping review.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Use of patient-reported outcome measures in everyday clinical practice in ophthalmology: results of a European multicountry survey.The British journal of ophthalmology · 2026Article
- Evaluating the effectiveness of oral phlebotonics for the conservative management of hemorrhoidal disease: insights from the VIVI2022/01/VIVALDI study.Frontiers in surgery · 2026Article
- Using Large Language Models for In Silico Development and Simulation of a Patient-Reported Outcome Questionnaire for Cataract Surgery with Various Intraocular Lenses: A Pre-Validation Study.Journal of clinical medicine · 2025Article
- Artificial intelligence and the future of patient-centered outcomes.Journal of patient-reported outcomes · 2025Article
- Streamlining wearable data integration for EHDS: a case study on advancing healthcare interoperability using Garmin devices and FHIR.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient-reported outcome measures (PROMs) are cornerstones of patient-centered clinical medicine and reflect patients' abilities, difficulties, perceptions and behaviors. The highly structured questionnaire format of PROMs currently limits their real-world validity and acceptability to patients, which becomes increasingly relevant with the high clinical interest in PROM data. In this short commentary, we aim to demonstrate the potential use of large language models (LLMs) in the context of PROM data collection and interpretation. MAIN BODY: The popularization of LLMs enables the development of a new generation of PROMs generated and administered through digital technology that interact with patients and score their responses in real time based on artificial intelligence. LLM-PROMs will need to be developed with multi-stakeholder input and careful validation against established PROMs. LLM-PROMs could complement traditional PROMs particularly in real-world clinical applications.
conclusionLLM-PROMs could allow quantifying patient-relevant dimensions based on less structured contents and foster the use of patient-reported data in digital, clinical applications of PROMs.
Indexed as
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.