Making Machine Learning Clinically Useful in Thrombosis and Hemostasis: A Roadmap for Diagnostic Translation

Area of Science:

• Medical Informatics
• Clinical Pathology
• Artificial Intelligence in Medicine

Background:

• Artificial intelligence (AI), especially machine learning (ML), holds significant promise for transforming healthcare, including the fields of thrombosis and hemostasis.
• Clinical decisions in these areas often involve integrating multiple data points, making them suitable for AI-driven decision support.
• Despite growing interest and research, the practical application of ML tools in routine clinical decision-making remains limited, with many models being early-stage prototypes.

Purpose of the Study:

• To propose a framework for translating machine learning (ML) tools into clinically useful diagnostic instruments in medicine.
• To provide a roadmap for assessing the clinical utility, transportability, and safe deployment of ML models.
• To guide clinicians and laboratory specialists in distinguishing between promising ML prototypes and clinically ready tools.

Main Methods:

• Framing ML tools as diagnostic instruments to define and assess clinical usefulness.
• Developing a roadmap for diagnostic translation, covering intended use, data, validation, implementation, and governance.
• Utilizing figures to illustrate the translation roadmap and map ML to laboratory processes.
• Employing a checklist for structured evaluation of published ML studies.

Main Results:

• The review proposes treating ML tools as diagnostic instruments to clarify their clinical utility at the bedside.
• A comprehensive roadmap is outlined for the translation of ML tools from development to clinical implementation.
• Guidance is provided for evaluating the readiness of ML tools for sustainable use in clinical practice.

Conclusions:

• The diagnostic instrument framework offers a practical approach to achieving and assessing the clinical usefulness of ML tools.
• A structured roadmap is essential for the successful translation and implementation of ML in clinical settings, particularly in thrombosis and hemostasis.
• Clinicians and laboratory specialists need clear criteria to differentiate between early-stage ML prototypes and deployable clinical decision support tools.