Development of digital learning tools for the anatomy curriculum

Overview

This research strand operates at the intersection of medical education, and digital technology. We ask: How can modern histology education meet the diagnostic demands of the AI era? By integrating whole slide imaging, AI-assisted pattern recognition, and biostatistics, we aim to rethink how morphological reasoning and diagnostic competence are taught preparing the next generation of physicians to work alongside intelligent systems, not behind them.

Key publications: [Darici et al., 2021; Brügge et al., 2024]

Digital innovation in medical training

• Analyze cognitive load: How do different digital interfaces affect the cognitive load associated with learning human anatomy?
• Scalable AI solutions: How do low-cost, AI-supported tools in anatomy education look like?
• Curricular implementation: What are the long-term educational outcomes of digital curricula compared to traditional instruction?

Educational Impact

The findings from this research strand directly inform the design of evidence-based, digital-first teaching materials. The goal is to produce scalable training tools that can be integrated into existing medical curricula, ensuring that students reach clinical competency faster and more effectively.