Key result
Artificial intelligence algorithms for preoperative risk stratification and intraoperative event prediction may outperform conventional tools, though their real-world clinical impact remains unclear.
Why the study?
Emerging AI algorithms model and predict perioperative problems with high accuracy, but their limited scope, lack of transparency, and real-world impact on clinical practice and patient outcomes remain unclear.
Emerging AI applications in perioperative risk stratification show potential to outperform conventional tools, though limited scope and lack of transparency remain key challenges for widespread adoption.
AI may enhance perioperative risk prediction beyond conventional tools; leaves open need for prospective validation before adoption.
The rapidly developing field of artificial intelligence (AI) may soon equip clinicians with algorithms that model and predict perioperative problems with extreme accuracy. Here, we outline emerging AI applications in preoperative risk stratification and intraoperative event prediction, where algorithm performance has been shown to outstrip commonly used conventional risk prediction tools. While offering an enticing view of a novel perioperative practice with superhuman foresight, AI's limited scope and lack of transparency remain key challenges for widespread adoption. As yet it is unclear whether machine learning alone can influence human clinical practice to exert real-world effects on patient outcomes.
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Harris et al. (2024) conducted a review in Perioperative problems. Artificial intelligence (AI) vs. Conventional risk prediction tools was evaluated. Artificial intelligence algorithms for preoperative risk stratification and intraoperative event prediction may outperform conventional tools, though their real-world clinical impact remains unclear.
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