Preliminary appraisal of machine learning based prediction models

Area of Science:

• Medical Informatics
• Clinical Prediction Models
• Machine Learning in Healthcare

Background:

• Healthcare research heavily focuses on developing prediction models.
• Limited integration of these models into routine clinical practice persists.
• Barriers include insufficient reporting, poor generalizability, and lack of user-friendly software, particularly for machine learning (ML) and artificial intelligence (AI) models.

Purpose of the Study:

• To propose a framework for appraising ML-based prediction models.
• To facilitate the responsible adoption of ML models in clinical settings.
• To address challenges hindering the implementation of healthcare prediction models.

Main Methods:

• Development of five key questions for preliminary appraisal.
• Focus on models published in or submitted to medical journals.
• Guidance for evaluating ML models for clinical use.

Main Results:

• A set of five critical questions is presented.
• These questions aim to aid in the preliminary assessment of ML models.
• The framework supports evaluating model reporting, generalizability, and usability.

Conclusions:

• Responsible adoption of ML prediction models requires careful appraisal.
• The proposed questions can guide clinicians and researchers in evaluating ML models.
• Addressing implementation barriers is crucial for leveraging ML in healthcare.