TITLE:
Research Progress and Trends of AI‑Empowered Medical Imaging Teaching in the Context of New Medical Science
AUTHORS:
Qun Yang, Nan Chen, Yun He
KEYWORDS:
Artificial Intelligence, Medical Imaging Teaching, Teaching Reform, Human-Machine Collaboration, Smart Classroom
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.8,
August
26,
2026
ABSTRACT: The rapid advancement of artificial intelligence (AI) is driving a profound paradigm shift in medical imaging education. As a discipline bridging theoretical knowledge, practical skills, and clinical application, medical imaging faces multiple challenges in the intelligent era, including insufficient teaching resources and difficulties in cultivating clinical reasoning. This paper, using a narrative review approach, systematically examines the theoretical underpinnings of AI‑empowered medical imaging teaching—Outcome‑Based Education (OBE), constructivism, Tyler’s Objective Model, and human‑machine collaborative teaching theory—and synthesises the integration of AI across three dimensions: pedagogical reconstruction, personalised evaluation, and image‑reading training. It further analyses key constraints such as insufficient algorithmic interpretability, content reliability risks, faculty competency gaps, and ethical deficiencies. On this basis, it proposes development pathways, including building human‑machine collaborative teaching ecosystems, strengthening AI ethics education, establishing evidence‑based evaluation systems, and deepening industry‑education integration. The review finds that AI‑driven teaching models hold promise for bridging theoretical instruction and clinical practice, and for supporting the cultivation of versatile medical imaging talents in the intelligent era. However, the current evidence is mostly derived from single‑centre, small‑sample studies lacking effect sizes and long‑term follow‑up, and many proposed strategies remain to be validated.