User Interface Design for AI-Based Medical Imaging: A Scoping Review

While the graphical user interface (GUI) is fundamental to computer-aided diagnosis (CAD) systems, a significant void exists in the technical literature regarding its design and integration into clinical workflows. Bridging this gap is essential, as a well-designed interface is the key driver of usability, interpretability, and the ultimate adoption of medical imaging tools. This scoping review aims to identify and analyze the primary approaches and emerging trends in GUI design for CAD systems developed for medical image analysis, with a specific focus on classification and segmentation tasks. We analyze not only the interaction patterns but also the specific clinical applications, datasets, AI algorithms, and evaluation protocols reported in the literature. A literature search was conducted across five major digital databases (ACM Digital Library, IEEE Xplore, PubMed, ScienceDirect, and SpringerLink) for scientific publications between 2020 and 2024. The search string ("medical image" AND ("computer-aided diagnosis" OR "CADx" OR "CAD") AND "user interface") was used. The search terms were strategically selected to specifically target visual diagnostic AI systems that integrate a clinician-facing interface. Initial screenings, followed by the application of predefined inclusion and exclusion criteria, were performed. Data was charted using eight research questions. From an initial pool of 1147 articles identified across two search phases, a total of 46 studies met the final inclusion criteria and composed the review set. The data extraction process revealed a growing trend towards the integration of interactive machine learning features, visualization of model uncertainty, and tools for explainable AI (XAI) directly within the user interface, moving beyond simple image display and result presentation. The design of GUIs for modern CAD systems is evolving from static displays to interactive and collaborative platforms. Many of them not only present the AI's prediction but also provide clinicians with tools to understand, question, and refine the automated analysis. A clear gap remains in the standardization of usability and human-computer interaction metrics for evaluating these systems, suggesting a critical direction for future research.