ESR Essentials: common performance metrics in AI—practice recommendations by the European Society of Medical Imaging Informatics - Takeaways - MDSpire

ESR Essentials: common performance metrics in AI—practice recommendations by the European Society of Medical Imaging Informatics

  • By

  • Michail E. Klontzas

  • Kevin B. W. Groot Lipman

  • Tugba Akinci D’ Antonoli

  • Anna Andreychenko

  • Renato Cuocolo

  • Matthias Dietzel

  • Salvatore Gitto

  • Henkjan Huisman

  • João Santinha

  • Federica Vernuccio

  • Jacob J. Visser

  • Merel Huisman

  • August 3, 2025

  • 0 min

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  • 1

    AI tools must be locally validated with independent datasets to ensure performance aligns with claimed metrics and reflects institutional practices.

  • 2

    Task-specific performance metrics, including segmentation, test-based, and outcome-based metrics, are essential for evaluating AI models in clinical settings.

  • 3

    Performance assessment should consider the clinical context, engaging radiologists to define relevant metrics and evaluate across vulnerable subgroups.

  • 4

    Comprehensive evaluation of AI performance is critical for safe integration into radiology, accounting for variability in imaging modalities and patient demographics.

  • 5

    Calibration and uncertainty quantification metrics are vital for understanding AI model behavior, yet their real-world implementation in imaging remains limited.

Original Source(s)

ESR Essentials: common performance metrics in AI—practice recommendations by the European Society of Medical Imaging Informatics

  • by Michail E. Klontzas, Kevin B. W. Groot Lipman, Tugba Akinci D’ Antonoli, Anna Andreychenko, Renato Cuocolo, Matthias Dietzel, Salvatore Gitto, Henkjan Huisman, João Santinha, Federica Vernuccio, Jacob J. Visser, Merel Huisman

  • August 3, 2025

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