Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers

Over the past five years, artificial intelligence (AI) has introduced new models and methods for addressing the challenges associated with the broader adoption of AI models and systems in medicine. This paper reviews recent advances in AI for medical image and video analysis, outlines emerging paradigms, highlights pathways for successful clinical translation, and provides recommendations for future work. Hybrid Convolutional Neural Network (CNN) Transformer architectures now deliver state-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration. Foundation and generative AI models enable the use of transfer learning to smaller datasets with limited ground truth. Federated learning supports privacy-preserving collaboration across institutions. Explainable and trustworthy AI approaches have become essential to foster clinician trust, ensure regulatory compliance, and facilitate ethical deployment. Together, these developments pave the way for integrating AI into radiology, pathology, and wider healthcare workflows.

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Publication Details

Journal
IEEE Journal of Biomedical and Health Informatics
Published
2025-12-31
DOI
https://doi.org/10.1109/jbhi.2025.3649496
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.36
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article

Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers

M.S. Pattichis, Amir A. Amini, Georgia D. Tourassi, Tijana Geroski et al.
1 citations
IEEE Journal of Biomedical and Health Informatics
COVID-19 diagnosis using AI
1.36
article

Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers

M.S. Pattichis, Amir A. Amini, Georgia D. Tourassi, Tijana Geroski, Efthyvoulos Kyriacou, Pinaki Sarder, D. I. Fotiadis, J. H. Saltz, Jiawen Yao, Karim Lekadir, Huazhu Fu, G. K. Matsopoulos, K. C. L. Wong, F. E. Shamout, N. D. Filipovic, K. Marias, J. Hou, C.S. Pattichis, A. S. Panayides, S. A. Tsaftaris, H. Chen, M. Zervakis, C. P. Loizou, G. Papanastasiou
article en
1 citations

Abstract

Over the past five years, artificial intelligence (AI) has introduced new models and methods for addressing the challenges associated with the broader adoption of AI models and systems in medicine. This paper reviews recent advances in AI for medical image and video analysis, outlines emerging paradigms, highlights pathways for successful clinical translation, and provides recommendations for future work. Hybrid Convolutional Neural Network (CNN) Transformer architectures now deliver state-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration. Foundation and generative AI models enable the use of transfer learning to smaller datasets with limited ground truth. Federated learning supports privacy-preserving collaboration across institutions. Explainable and trustworthy AI approaches have become essential to foster clinician trust, ensure regulatory compliance, and facilitate ethical deployment. Together, these developments pave the way for integrating AI into radiology, pathology, and wider healthcare workflows.

IEEE Journal of Biomedical and Health InformaticsVol. 30(2)
Abu Dhabi University (AE), Institució Catalana de Recerca i Estudis Avançats (ES), New York University Abu Dhabi (AE), Oak Ridge National Laboratory (US), University of Kragujevac (RS), University of Louisville (US), Cyprus University of Technology (CY), University of New Mexico (US), National Technical University of Athens (GR), University of Ioannina (GR), Hong Kong University of Science and Technology (HK), Academy of Athens (GR), Tencent (China) (CN), Mediterranean University (ME), University of Florida Health (US), Institute of High Performance Computing (SG), University of Cyprus (CY), IBM Research - Almaden (US), Athena Research and Innovation Center In Information Communication & Knowledge Technologies (GR), Technical University of Crete (GR), Stony Brook University (US), Foundation for Research and Technology Hellas (GR), University of Edinburgh (GB)
Partnerships for the goals
Openalex Percentile: Top 13%
COVID-19 diagnosis using AI
1.36
DOIOpen Access PDFOpenAlex
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