ArticleFrontiers in artificial intelligence2024
Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.
Article in Frontiers in artificial intelligence, 2024. 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.
- AI agents: a game changer in ophthalmology?Eye (London, England) · 2026Article
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- Artificial Intelligence and its Current Role in Clinical Outcome Prediction, Musculoskeletal Imaging, and Economic and Ethical Considerations within Orthopedics and Sports Medicine.Current reviews in musculoskeletal medicine · 2026Review
- From inferring preferences to enabling choice: potentials of digital tools to improve substitute decision-making.Frontiers in digital health · 2026Article
- Application of artificial intelligence to measure and predict patient values and preferences: a scoping review.NPJ digital medicine · 2025Article
- Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review.Interactive journal of medical research · 2025Review
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
Background: The algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones. Methods: We performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs following a medical event alongside qualitative studies exploring a theoretical PPP. Results: Sixty-eight studies used machine learning to evaluate PROMs; an additional 20 studies focused on a theoretical PPP. For PROMs, orthopedic surgeries ( Conclusion: The primary objective of a PPP is to estimate patient-reported quality of life following an intervention. Use of machine learning to predict PROMs for
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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.