ArticleNPJ digital medicine2025
Application of artificial intelligence to measure and predict patient values and preferences: a scoping review.
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 1 paper.
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
1 citing paper in PubMed.
- Risks and Benefits of Artificial Intelligence as an Adjunct in Colorectal Multidisciplinary Decision Making.ANZ journal of surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Patients' voices are often difficult to capture directly in healthcare decisions. This scoping review examines how artificial intelligence (AI) has been applied to measure and predict patient values and preferences, aiming to evaluate its potential to generate reliable, patient-centered evidence, identify opportunities and challenges, and explore AI tools in literature reviews. Analyzing 67 studies, we summarize how AI processes diverse data sources such as social media, clinical records, and patient surveys to extract population- and individual-based patient values and preferences. Researchers have applied AI for efficient data preprocessing, extraction, analysis, integration, and modeling. Despite promising validation results (e.g., >80% accuracy in data preprocessing), key challenges remain, such as data quality issues, lack of real-world validation, and ethical concerns. This review underscores the potential of AI in patient values and preferences research and calls for greater transparency and real-world implementation to better align healthcare delivery with patient needs.
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.