From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning...

Area of Science:

• Artificial Intelligence in Medicine
• Pediatric Machine Learning Research

Background:

• Machine learning (ML) applications are expanding across medical fields, yet direct translation to pediatrics is hindered by unique clinical, technical, and ethical challenges.
• Pediatric ML research faces complexities from diverse developmental stages, family-centered care models, data heterogeneity, and a scarcity of high-quality databases.

Purpose of the Study:

• To address the unique nuances of applying ML in pediatrics.
• To provide a comprehensive outline of special considerations for ML in pediatrics, from project ideation to clinical implementation.
• To serve as a guideline for ML scientists and clinicians in the pediatric setting.

Main Methods:

• Development of a common pipeline for structuring ML projects in pediatrics.
• Establishment of a framework for translating ML models into clinical practice for pediatric populations.
• Identification and discussion of ethical and legal considerations specific to pediatric ML.

Main Results:

• A structured approach is proposed to navigate the complexities of pediatric ML.
• The review highlights the need for tailored pipelines and frameworks to overcome data and developmental challenges.
• Ethical and legal considerations are emphasized throughout the ML project lifecycle in pediatrics.

Conclusions:

• Implementing ML in pediatrics demands specific strategies that account for developmental diversity and family-centered care.
• The proposed pipeline and framework aim to facilitate the successful development and clinical integration of ML solutions for children.
• This work provides essential guidance for researchers and clinicians to advance pediatric ML responsibly and effectively.