Challenges in translational machine learning.

Area of Science:

• Biomedical Informatics
• Clinical Decision Support
• Machine Learning

Background:

• Machine learning (ML) algorithms are increasingly integrated into clinical decision support systems.
• Bridging the gap between ML development and clinical adoption requires a specialized approach.

Purpose of the Study:

• To define and review the field of "translational machine learning" (translational ML).
• To provide guidance for clinicians and bioinformaticians in developing and implementing translational ML pipelines.
• To highlight challenges and best practices in applying ML within clinical settings.

Main Methods:

• Review of key steps in translational ML pipelines, from model building to clinical implementation.
• Discussion of experimental setup, computational analysis, interpretability, and reproducibility.
• Emphasis on collaborative efforts and data sharing.

Main Results:

• Joint efforts and strong communication between data scientists and clinicians are crucial for successful translational ML.
• Collaboration improves the interpretability and trustworthiness of ML models.
• Multi-centric cohorts and data sharing facilitate the development of generalizable and reproducible ML models.

Conclusions:

• Translational ML aims to streamline the adoption of ML in clinics, improving decision support.
• Addressing challenges in interpretability, reproducibility, and generalization is key.
• Collaboration and data sharing are essential for advancing translational ML and its clinical impact.