From Data to Decisions: How Artificial Intelligence Is Revolutionizing Clinical Prediction Models in Plastic Surgery
Kevin Kooi1,2,3, Estefania Talavera4, Liliane Freundt1
1From the Division of Plastic and Reconstructive Surgery, Massachusetts General Hospital.
Plastic and Reconstructive Surgery
|January 9, 2024
Summary
Artificial intelligence (AI) and machine learning significantly impact clinical prediction models in plastic surgery. These tools enhance evidence-based practice and aid daily decision-making for improved patient outcomes.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Surgery
Background:
- Machine learning (ML) offers significant potential for plastic, reconstructive, and hand surgery.
- ML can analyze large datasets to identify complex patterns, improving evidence-based practices.
- AI models can predict patient diagnosis, prognosis, and outcomes, aiding clinical decision-making.
Purpose of the Study:
- To provide a practice guideline for plastic surgeons on implementing AI in clinical decision-making.
- To guide the development of AI-driven clinical prediction models.
- To introduce the 7-step approach and ABCD validation steps for AI research.
Main Methods:
- Utilizing the 7-step approach for AI implementation.
- Applying the ABCD validation steps by Steyerberg and Vergouwe.
- Describing two developmental protocols for AI research.
Main Results:
- AI and ML models can enhance plastic surgery by analyzing complex data.
- Clinical prediction models aid in diagnosis, prognosis, and outcome prediction.
- AI serves as a valuable decision support tool in daily practice.
Conclusions:
- AI and ML are crucial for advancing clinical prediction models in surgery.
- Guidelines and validation steps are essential for effective AI implementation.
- New protocols enhance transparency and bias assessment in AI research.
