Enhancing Diagnostic and Postoperative Outcome Predictions Through Machine Learning: A Focused Analysis on Noncardiac and Cardiac Surgeries

Abstract

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.