Designing AI tools for Pathology: A mixed-method study on user interface design for breast cancer lymph node metastases detection

Abstract

Background

Histopathological assessment of lymph nodes is essential in breast cancer care. Artificial intelligence (AI) has shown promise in supporting this process, yet successful clinical adoption requires attention to human-AI interaction and user interface design. This study explored the user experience of an AI-based clinical decision support tool assisting pathologists in diagnosing breast cancer metastases.

Methods

We conducted a within-subjects mixed-methods study in which ten pathologists evaluated two AI-based clinical decision support user interface prototypes for breast cancer metastasis detection in sentinel nodes. ANNO displayed threshold based on-image annotations with minimal automation, whereas RAVS presented four likelihood regions of interest (ROI) per slide with automated measurement and case summaries. Quantitative measures included review time, diagnostic outcomes, and System Usability Scale (SUS) scores. Qualitative data were gathered via semi-structured interviews and analysed thematically.

Results

ANNO achieved higher usability scores (mean SUS 90.3) and was associated with a sense of control and resembled current diagnostic workflow, whereas RAVS (mean SUS 80.0) was preferred by participants valuing rapid navigation and automated measurements. Interview findings highlighted trade-offs: ANNO preserved diagnostic overview and autonomy, while RAVS improved navigational efficiency but reduced whole-slide context. ROI presentation strategy seemed to influence preference. Trust in AI was generally cautious and linked to needs for transparency and long-term clinical exposure.

Conclusion

Both interfaces were perceived as potentially clinically valuable but differed in their usability and workflow integration. Our exploratory findings indicates that for future development of AI-based decision aiding tools for this diagnostic task, a combination of high human control with targeted automation is advisable, combined with visual clarity, and an effective ROI presentation strategy.