From Data to Decisions: How AI Is Revolutionizing Clinical Prediction Models in Plastic Surgery

The impact of clinical prediction models within artificial intelligence (AI) and machine learning is significant. With its ability to analyze vast amounts of data and identify complex patterns, machine learning has the potential to improve and implement evidence-based plastic, reconstructive, and hand surgery. In addition, it is capable of predicting the diagnosis, prognosis, and outcomes of individual patients. This modeling aids daily clinical decision-making, most commonly at the moment, as decision support. The purpose of this article is to provide a practice guideline to plastic surgeons implementing AI in clinical decision-making or setting up AI research to develop clinical prediction models using the 7-step approach and the ABCD validation steps of Steyerberg and Vergouwe. The authors also describe 2 important protocols that are in the development stage for AI research: (1) the transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis checklist, and (2) the Prediction Model Risk of Bias Assessment Tool checklist to access potential biases.