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Peer-reviewedCC BY-NC-NDMedical review (2021)

The application value of artificial intelligence in medical imaging: foundations, progress, and challenges.

  • Hongzan Sun1
  • Yushan Zhang1
  • Yu Shi1
  • Bing Ji2
  • Chen Li3
  • Jiajia Yang4
  • Marcin Grzegorzek5
  • Qiyong Guo1
5 affiliations
  1. 1Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
  2. 2School of Control Science and Engineering, Shandong University, Shandong, Jinan, China.
  3. 3College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.
  4. 4Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan.
  5. 5Institute of Medical Informatics, University of Luebeck, Luebeck, Germany.
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Abstract

Artificial intelligence (AI) is rapidly transforming medical imaging, reshaping the field as it does everyday life. As imaging data grow in complexity and clinical workloads continue to rise, AI has become increasingly essential for improving diagnostic efficiency and precision. However, despite these technical advances, the clinical adoption of AI in medical imaging remains fragmented, with limited generalizability, insufficient interpretability, and unclear clinical responsibility. In this review, we critically examine the evolution of AI in medical imaging, with a particular focus on its functional roles across the imaging workflow and its actual contributions to contemporary clinical needs. We systematically analyze current application paradigms, identify methodological and translational bottlenecks, and discuss emerging strategies to improve model robustness, interpretability, and clinical integration. In addition, we identify key barriers to the practical implementation of AI in medical imaging, including challenges related to cross-domain generalizability, data privacy issues and regulatory compliance. By examining these opportunities and challenges, this review aims to guide future research directions and foster deeper interdisciplinary collaboration among AI, clinical medicine, and biomedical engineering.

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Cite

BibTeXRIS
BibTeX
@article{sun2026application,
  title = {{The application value of artificial intelligence in medical imaging: foundations, progress, and challenges.}},
  author = {Hongzan Sun and Yushan Zhang and Yu Shi and Bing Ji and Chen Li and Jiajia Yang and Marcin Grzegorzek and Qiyong Guo},
  journal = {Medical review (2021)},
  year = {2026},
  month = oct,
  doi = {10.1515/mr-2025-0083},
  url = {https://europepmc.org/article/MED/42923799},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - The application value of artificial intelligence in medical imaging: foundations, progress, and challenges.
AU  - Hongzan Sun
AU  - Yushan Zhang
AU  - Yu Shi
AU  - Bing Ji
AU  - Chen Li
AU  - Jiajia Yang
AU  - Marcin Grzegorzek
AU  - Qiyong Guo
PY  - 2026
DA  - 2026/10/05
JO  - Medical review (2021)
DO  - 10.1515/mr-2025-0083
UR  - https://europepmc.org/article/MED/42923799
AB  - Artificial intelligence (AI) is rapidly transforming medical imaging, reshaping the field as it does everyday life. As imaging data grow in complexity and clinical workloads continue to rise, AI has become increasingly essential for improving diagnostic efficiency and precision. However, despite these technical advances, the clinical adoption of AI in medical imaging remains fragmented, with limited generalizability, insufficient interpretability, and unclear clinical responsibility. In this review, we critically examine the evolution of AI in medical imaging, with a particular focus on its functional roles across the imaging workflow and its actual contributions to contemporary clinical needs. We systematically analyze current application paradigms, identify methodological and translational bottlenecks, and discuss emerging strategies to improve model robustness, interpretability, and clinical integration. In addition, we identify key barriers to the practical implementation of AI in medical imaging, including challenges related to cross-domain generalizability, data privacy issues and regulatory compliance. By examining these opportunities and challenges, this review aims to guide future research directions and foster deeper interdisciplinary collaboration among AI, clinical medicine, and biomedical engineering.
SN  - 2097-0773
SN  - 2749-9642
ER  -