Making machine learning matter to clinicians: model actionability in medical decision-making

Area of Science:

• * Biomedical informatics and artificial intelligence in healthcare.
• * Clinical decision support systems and predictive modeling.

Background:

• * Machine learning (ML) offers transformative potential for patient care but faces challenges in translating in silico performance to clinical utility.
• * Current evaluation methods for ML models often overlook crucial aspects of real-world applicability during early development.
• * The concept of 'actionability' is vital for assessing a model's practical value at the point of care but remains undervalued.

Purpose of the Study:

• * To introduce and define a novel metric for evaluating the actionability of machine learning models in clinical settings.
• * To propose a framework for assessing ML model usefulness early in the development lifecycle, prior to complex analyses.
• * To contribute to the development of pragmatic tools for identifying the potential clinical impacts of ML applications.

Main Methods:

• * Conceptual development of a new metric focused on the actionability of ML models.
• * Positioning the proposed metric as a preliminary evaluation step before calibration and decision curve analysis.
• * Emphasis on practical utility and clinical impact assessment.

Main Results:

• * A proposed metric for actionability is introduced as a key consideration for ML model evaluation.
• * This metric aims to provide an early assessment of a model's potential clinical usefulness.
• * The metric is intended to complement existing performance evaluation techniques.

Conclusions:

• * Actionability is an essential, yet often overlooked, metric for evaluating the clinical utility of machine learning models.
• * The proposed metric offers a pragmatic approach to assess potential clinical impact early in model development.
• * Integrating actionability assessment can enhance the development of clinically relevant and impactful ML tools for healthcare.