Machine learning in the development and application of patient-reported outcome measures (PROMs) for surgical patients: a systematic review
AI and ML have the potential to improve PROM utilization in surgical care by enhancing efficiency and personalization while maintaining data quality. Clinicians can use AI-driven PROMs to reduce patient burden and integrate ML models for accurate post-surgical outcome prediction, thereby optimizing patient-centered care.
Authors
- Lina Alshabanah
- Tariq Alanezi (ORCID: https://orcid.org/0009-0001-6215-0042)
- Kin‐Kit Li (ORCID: https://orcid.org/0000-0002-0635-0103)
- Nawaf K Alkhayal
- Muhammad Mamdani
- Mohamad A. Hussain
- Mohammed Al-Omran
- Husam Alrumaih
- Leen Al-Omran
- Meena Verma
Institutions
- Brigham and Women's Hospital (US)
- St. Michael's Hospital (CA)
- Alfaisal University (SA)
- Harvard University (US)
- Royal College of Surgeons in Ireland (IE)
- University of Toronto (CA)
- King Faisal Specialist Hospital & Research Centre (SA)
- St Michael's Hospital (GB)
- Institute for Clinical Evaluative Sciences (CA)
- Artificial Intelligence in Medicine (Canada) (CA)
- Unity Health Toronto
- Ontario Medical Association (CA)
Publication Details
- Journal
- Journal of Patient-Reported Outcomes
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1186/s41687-026-00992-8
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 17.70