Do no harm: a roadmap for responsible machine learning for health care.

Area of Science:

• Health Informatics
• Machine Learning in Medicine
• Clinical Translation

Background:

• Growing interest in machine learning (ML) applications in healthcare.
• Limited progress in deploying ML interventions into actual patient care.
• Need for structured approaches to bridge the gap between ML development and clinical practice.

Purpose of the Study:

• To present a comprehensive framework for accelerating the translation of ML-based interventions in healthcare.
• To provide context and guidelines for successful clinical deployment of ML tools.
• To facilitate the systematic integration of ML into routine patient care.

Main Methods:

• Development of a multi-stage framework for ML intervention translation.
• Identification of key stakeholder roles and engagement strategies.
• Emphasis on a systematic process from problem formulation to widespread deployment.

Main Results:

• A proposed framework designed to overcome barriers to ML deployment in healthcare.
• Guidelines for managing the entire lifecycle of ML-based health interventions.
• Highlighting the necessity of interdisciplinary collaboration for successful implementation.

Conclusions:

• Successful translation of ML in healthcare demands a structured, systematic approach.
• Engaged stakeholders and a clear process are crucial for moving ML from research to patient care.
• The presented framework offers a roadmap for accelerating clinical adoption of ML technologies.