期刊
AMERICAN SURGEON
卷 89, 期 1, 页码 31-35出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/00031348221109478
关键词
artificial intelligence; machine learning; deep learning; surgery; patient-reported outcomes
类别
Patient-reported outcomes (PROs) help providers understand differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. Artificial intelligence (AI) and machine learning (ML) techniques improve patient outcomes by accurately predicting PROs, enabling patient-centered shared decision-making.
Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.
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