Artificial intelligence is rapidly transforming clinical imaging by enabling earlier, more accurate, and scalable diagnostic processes across radiology, pathology, and ultrasound-based workflows. However, despite significant advances in model performance, challenges persist regarding methodological reproducibility, clinical interpretability, and the standardization of AI implementation. This Methods Collection aims to bridge that gap by presenting visual, step-by-step protocols for AI applications that are robust, explainable, and ready for real-world integration.

The collection will showcase methods for image preprocessing, annotation strategies, segmentation, classification, and multimodal fusion, with particular emphasis on reproducible pipelines and transparent model evaluation. It will also highlight approaches to explainability, such as saliency maps, attention mechanisms, and uncertainty quantification, that make AI outputs interpretable and clinically actionable. By providing detailed demonstrations using real or synthetic imaging datasets, this collection will help researchers, clinicians, and engineers confidently adopt, validate, and adapt AI tools.

Through the visual format of JoVE, contributors will be able to communicate not only the “what” but the “how” behind state-of-the-art AI techniques in medical imaging. This approach will foster methodological rigor, facilitate training of new researchers, and accelerate safe clinical translation.