Author(s)

Zhuoyun Jiang ¹, Chenyu Ma ¹, Yuanshou Zhu ¹, Zhigang Zhu ¹

Affiliation(s)

¹ School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Zhigang Zhu

ABSTRACT

With the rapid development of artificial intelligence technology, medical imaging is gradually shifting from traditional image reading to AI-centered assisted diagnosis, quantitative analysis, and intelligent workflows. Medical Image Processing, as a fundamental course supporting clinical applications of AI, plays an important role in interdisciplinary medical-engineering education. This course is typically offered to both medical and engineering students, whose differences in knowledge structure, thinking patterns, and language systems often lead to learning gaps and reduced teaching efficiency. This paper proposes a bidirectional knowledge-complementary teaching strategy for medical-engineering integration using generative artificial intelligence as a teaching support tool. It adopts "medical context – engineering context – unified expression" as the core bridge. Without replacing the teacher's dominant role, generative AI is involved in concept explanation, terminology alignment, and learning feedback, helping engineering students understand clinical contexts and diagnostic logic, and helping medical students understand AI algorithms and evaluation systems. By analyzing teaching challenges, designing classroom activities, and summarizing implementation experience, this paper examines the strategy's value in reducing interdisciplinary barriers, promoting a shared language, and improving classroom interaction. It also discusses potential risks and governance approaches of generative AI in education. Practice shows that, with proper regulation and evaluation mechanisms, generative AI can serve as an effective interdisciplinary bridge in Medical Image Processing courses, providing a transferable model for medical-engineering teaching reform.

KEYWORDS

Generative AI; Medical image processing; Clinical auxiliary diagnosis; Medical-Engineering integration; Knowledge complementarity

CITE THIS PAPER

Zhuoyun Jiang, Chenyu Ma, Yuanshou Zhu, Zhigang Zhu. Research on Teaching Strategies for Medical Image Processing Courses Enabled by Generative AI. Curriculum and Teaching Methodology (2026). Vol. 9, No. 3, 100-107. DOI: http://dx.doi.org/10.23977/curtm.2026.090313.

REFERENCES

[1] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88. 
[2]  Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56. 
[3]  Mesko, B., & Győrffy, Z. (2019). The rise of the empowered physician in the digital health era. Journal of medical Internet research, 21(3), e12490.
[4]  Preiksaitis, C., & Rose, C. (2023). Opportunities, challenges, and future directions of generative artificial intelligence in medical education: scoping review. JMIR medical education, 9, e48785.
[5]  Ahsan, Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187.
[6]  Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., ... & Ting, D. S. W. (2025). Generative artificial intelligence in medicine. Nature Medicine, 1-13.
[7]  Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. radiographics, 37(2), 505-515.
[8]  Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC medical imaging, 15(1), 29.

Sponsors, Associates, and Links

---

• Advances in Educational Technology and Psychology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education