The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications
Open accessAbstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.
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1 retracted
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RetractedImageNet classification with deep convolutional neural networks
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Squeeze-and-Excitation Networks
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Cited by
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
Minimal residual disease as a target for liquid biopsy in patients with solid tumours
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Harnessing the Heart’s Magnetic Field for Advanced Diagnostic Techniques
Elfouly, T., & Alouani, A. (2024) · Sensors · doi:10.3390/s24186017 · Cited by 19
Evaluating facial dermis aging in healthy Caucasian females with LC-OCT and deep learning
Assi, A., Fischman, S., Lopez, C., Pedrazzani, M., Grignon, G., Missodey, R., Korichi, R., Cauchard, J.-H., Ralambondrainy, S., & Bonnier, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-74370-z · Cited by 13
Artificial Intelligence in Coronary Artery Calcium Scoring
Aromiwura, A. A., & Kalra, D. K. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13123453 · Cited by 11
Metadata
- Authors
- Pinto-Coelho, L.
- Venue
- Bioengineering
- Publisher
- MDPI AG
- Published
- 2023-12-18
- Volume
- 10
- Issue
- 12
- Pages
- 1435
- ISSN
- 2306-5354 (ISSN-L)
- OpenAlex
- W4389885061
- Cited by
- 676
- References
- 100
- FWCI
- 23.39
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
