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Updated: May 3, 2026

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Clinical Requirements for Transparent Machine Learning Model Information: A Mixed Methods Study Protocol.

Louis Agha-Mir-Salim1, Nicolas Frey1, Lina Mosch1

  • 1Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
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Physicians need transparent information from machine learning models for better diagnostic tools. This study identifies requirements for transparent AI in emergency departments to ensure responsible healthcare use.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Machine learning (ML) models lack transparency, creating risks in clinical applications.
  • Effective use of AI in healthcare requires understanding model behavior.

Purpose of the Study:

  • To identify physician requirements for transparent ML information in diagnostic decision support systems.
  • To develop and test a prototype system addressing clinical needs and regulatory standards.
  • To enhance the responsible implementation of artificial intelligence in healthcare settings.

Main Methods:

  • Mixed methods approach combining qualitative semi-structured interviews with physicians.
  • Iterative prototype development and user testing.
Keywords:
Machine learningclinical decision supporttransparency

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  • Focus on diagnostic decision support in the emergency department.

    • Main Results:

    • Qualitative insights into physician needs for ML model transparency were gathered.
    • A prototype system was developed based on identified requirements.
    • The study aims to align clinical needs with regulatory compliance for AI in healthcare.

    Conclusions:

    • Addressing ML transparency is crucial for safe and effective AI adoption in emergency medicine.
    • User-centered design is key to developing trustworthy clinical decision support tools.
    • This research contributes to the responsible use of artificial intelligence in patient care.