Artificial Intelligence (AI) technology is profoundly transforming the diagnostic paradigm of medical imaging. Centered on deep learning and large language models (LLMs), AI technologies have been widely applied in multi-modal imaging tasks, including lesion detection, image segmentation, computer-aided diagnosis, and automated report generation, demonstrating significant value in improving diagnostic efficiency, accuracy, and standardization. However, as these technologies penetrate into core clinical decision-making processes, emerging challenges have become increasingly apparent, including algorithmic “black box” opacity, data privacy breaches, algorithmic bias, ambiguous liability attribution, and the erosion of humanistic care. This paper systematically reviews the technical foundations, clinical practices, and workflow impacts of AI in medical imaging, provides an in-depth analysis of the ethical dilemmas faced by these technologies, and proposes normative pathways and development strategies based on existing research. The aim is to provide reference for the safe, compliant, and sustainable clinical application of medical imaging AI.

Xinxin Pan, Qinshao Wei. Current Status and Ethical Considerations of Artificial Intelligence in Medical Imaging. Academic Journal of Medicine & Health Sciences (2026), Vol. 7, Issue 2: 27-32. https://doi.org/10.25236/AJMHS.2026.070205.

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