Artificial intelligence is increasingly shaping radiology, supporting detection, diagnosis and workflow efficiency. Its safe use, however, depends on robust evaluation of performance. Metrics must reflect clinical objectives, patient safety and real-world settings rather than theoretical accuracy alone. The European Society of Medical Imaging Informatics has outlined practice recommendations, emphasising the selection of task-specific measures, validation with independent datasets and awareness of pitfalls that could undermine clinical trust. By aligning AI performance assessment with clinical reality, radiologists can integrate these tools more effectively and safeguard patient outcomes.
Task-Specific Evaluation Across Levels
AI models in radiology operate across a spectrum, from pixels to patient outcomes, and each level requires tailored assessment. At the technical level, segmentation metrics such as Dice similarity coefficient or intersection over union quantify overlap between predicted and reference structures. These are particularly relevant for tasks like tumour contouring or organ delineation. Boundary-specific measures, such as the normalised surface distance, help capture structural detail, especially in small or irregular lesions. For detection, bounding box localisation is assessed through overlap thresholds, while mean average precision summarises results across classes.
Classification tasks rely on both test-based and outcome-based measures. Sensitivity and specificity provide prevalence-independent indicators of diagnostic ability, while precision and negative predictive value reflect clinical consequences in real-world populations. Balanced accuracy, F1-score and the Matthews correlation coefficient offer alternatives to accuracy, especially in low-prevalence or class-imbalanced settings where accuracy can be misleading. Multi-threshold measures, including the area under the receiver operating characteristic curve and the precision-recall curve, are used to capture performance across thresholds. For more complex scenarios with multiple classes, metrics like macro and micro F1-scores, Cohen’s Kappa and MCC address performance across varying class distributions.
Clinical Relevance and Pitfalls
Evaluating AI performance requires more than mathematical assessment. Clinical context and workflow integration are essential for ensuring meaningful use. Metrics must align with the intended task, prevalence in the target population and subgroup characteristics. For instance, screening programmes may prioritise high sensitivity to minimise missed cases, while invasive diagnostic pathways may demand higher specificity to avoid unnecessary interventions. Adjusting thresholds accordingly is vital, but must be grounded in calibration and uncertainty quantification to prevent overconfidence.
Several pitfalls are common. Overreliance on a single metric, such as accuracy, can mask weaknesses in imbalanced datasets. In low-prevalence settings, even highly specific tools may generate large numbers of false positives, burdening workflows and potentially leading to overtreatment. In segmentation, metrics may overlook small but clinically important structures, while some measures may fail to capture shape differences. Insufficient reporting remains another limitation, hindering reproducibility and transparency. Mitigation strategies include reporting multiple complementary metrics, tailoring evaluation to anatomical structures, involving clinicians in defining relevant outcomes and following established reporting guidelines such as CLAIM and CLEAR.
Beyond Technical Metrics: Trials and Image Quality
With the rise of generative AI, image quality assessment has become increasingly important. Common metrics include structural similarity index measure, peak signal-to-noise ratio and root mean square error, though these do not always reflect diagnostic quality. Human evaluation therefore remains indispensable, ensuring that synthetic images contribute to safe interpretation.
Clinical trials offer a further dimension for evaluation, addressing patient-centred outcomes such as recall rates, interval cancer detection, hospitalisation or treatment waiting times. These measures complement diagnostic metrics by linking AI directly to healthcare delivery and efficiency. Although such trials remain limited due to the relative novelty of AI in imaging, their number is expected to increase, reflecting a broader shift towards measuring impact at patient and institutional levels.
Robust evaluation of AI in radiology requires a multifaceted approach, integrating technical, diagnostic and clinical outcomes. Selecting task-specific metrics, validating locally and avoiding common pitfalls are essential steps towards safe implementation. As AI-generated images and clinical trials become more prominent, the scope of assessment must extend beyond algorithmic accuracy to encompass diagnostic quality and patient-centred outcomes. By adopting standardised reporting and involving clinicians in evaluation, radiologists can ensure that AI delivers on its promise of improving diagnosis, workflow and patient safety.
Source: European Radiology
Image Credit: iStock
