Why the study?
Despite advances in AI and machine learning models to predict perioperative pulmonary and respiratory complications, these models are rarely implemented in routine clinical practice due to a translation gap.
Population
59 unique publications with 63 phenotype-specific study-listings on PPRCs
Comparison
AI and machine learning models for predicting PPRCs vs current clinical practice
Design
Narrative review synthesizing evidence and proposing a translational framework
Key result
Artificial intelligence models for predicting perioperative pulmonary complications lacked robust validation, with only 15.3% (9 of 59) of publications reporting true external validation.
Authors
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AI models for PPRC prediction rarely reach clinical practice; leaves open the need for implementation and prospective validation studies.
Near-term clinical translation of AI models for perioperative respiratory complications is more likely through institution-specific development and local adaptation rather than direct widespread deployment.
Lee et al. (2026) conducted a review in Perioperative pulmonary and respiratory complications (n=59). Artificial intelligence and machine learning models was evaluated on Outcome ascertainment quality and external validation. Artificial intelligence models for predicting perioperative pulmonary complications lacked robust validation, with only 15.3% (9 of 59) of publications reporting true external validation.
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