Key result
Machine learning algorithms outperform traditional risk scores for predicting postoperative outcomes across surgical specialties.
Why the study?
Machine learning algorithms build upon traditional risk scoring tools to provide dynamic and tailored patient information across surgical specialties.
Do machine learning algorithms improve diagnosis and prediction of postoperative outcomes compared to traditional risk scoring tools in surgical patients?
Do machine learning algorithms improve diagnosis and prediction of postoperative outcomes compared to traditional risk scoring tools in surgical patients?
Machine learning algorithms show potential superiority over traditional risk scoring tools for diagnosis and predicting postoperative outcomes in surgical patients.
Hypothesis-generating for machine learning in surgical risk prediction; prospective validation needed before clinical adoption.
Background: Traditional risk scoring tools have assisted to guide surgical practice for decades. Machine learning algorithms build upon this concept to allow dynamic and tailored patient information. These algorithms have been employed across most surgical specialties with multiple aims, including cost of care assessment, risk stratification, and prediction of procedural survival. Methods: Paper selection was based on three main criteria: relevance, recency, and novelty. Relevant studies were identified through a comprehensive search of major databases, including PubMed and Scopus. Results: Machine learning algorithms pose significant advantages compared to traditional risk scoring tools. Across cardiac and noncardiac specialties, multiple studies have identified machine learning algorithms as superior to control or traditional scoring tools at diagnosis. Conclusion: In this focused analysis, we have identified the potential of machine learning to aid in diagnosis, management, and prediction of postoperative outcomes. Surgeons must continue to integrate machine learning into their practice with the aim of improving both patient and surgeon‐based outcomes.
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Lombardo et al. (2025) conducted a review in Cardiac and noncardiac surgeries. Machine learning algorithms vs. Traditional risk scoring tools was evaluated on Diagnosis, management, and prediction of postoperative outcomes. Machine learning algorithms were identified as superior to traditional risk scoring tools for diagnosis and prediction of postoperative outcomes across cardiac and noncardiac surgical specialties.
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