Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations
Robust assessment of artificial intelligence (AI) models in medical imaging is paramount for reliable clinical integration. This international collaborative review paper provides an overview of key evaluation metrics across diverse tasks, including classification, regression, survival analysis, dete...
| Autores: | , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/227851 |
| Acceso en línea: | https://hdl.handle.net/2445/227851 |
| Access Level: | acceso abierto |
| Palabra clave: | Intel·ligència artificial en medicina Diagnòstic per la imatge Aprenentatge automàtic Algorismes computacionals Medical artificial intelligence Diagnostic imaging Machine learning Computer algorithms |
| Sumario: | Robust assessment of artificial intelligence (AI) models in medical imaging is paramount for reliable clinical integration. This international collaborative review paper provides an overview of key evaluation metrics across diverse tasks, including classification, regression, survival analysis, detection, and segmentation, as well as specialized metrics for calibration, foundation models, large language models, and synthetic images. Challenges of comparing models statistically and translating metric scores to clinical practice are also discussed. For each section, the paper outlines fundamental metrics, identifies common pitfalls and misapplications, and offers recommendations for more robust evaluations. Key recommendations often involve utilizing multiple, complementary metrics tailored to the specific task and dataset properties, transparent reporting of methodology, and critically, considering the clinical utility and real-world implications of model performance. Ultimately, effective evaluation requires a comprehensive, context-aware approach that goes beyond statistical metrics to ensure. |
|---|