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.
| Original language | English |
|---|---|
| Journal | IEEE Journal of Biomedical and Health Informatics |
| DOIs | |
| State | Accepted/In press - 2026 |
Funding
Matsopoulos, G. Papanastasiou, P. Sarder, G. Tourassi, S. A. Tsaftaris, acted as Section leaders (in ascending surname order). A. Amini, H. Fu, E. Kyriacou, M. Zervakis, J. H. Saltz, F. E. Shamout, K. C. L. Wong, J. Yao acted as Section contributors (in ascending surname order). A.S. Panayides, D.I. Fotiadis, C. S. Pattichis, and M. S. Pattichis acted as manuscript’s editors. A. S. Panayides is with the CYENS Centre of Excellence, Nicosia, Cyprus (e-mail [email protected]) H. Chen and J. Hou are with the Dep. of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China (e-mail: [email protected], [email protected]) N. D. Filipovic and T. Geroski are with the University of Kragujevac, Serbia (e-mail: [email protected], [email protected]) K. Lekadir is with the Institució Catalana de Recerca i Estudis Avançats (ICREA) and the Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Spain (e-mail: [email protected]) K. Marias is with the Foundation for Research and Technology-FORTH, and Dept. of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece (email: [email protected]) G. K. Matsopoulos is with the School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece (e-mail: [email protected]) G. Papanastasiou is with the Mathematics Research Centre, Academy of Athens, and Archimedes Unit, Athena Research Centre, Athens, Greece (e-mail: [email protected]) P. Sarder is with the CMIL, Medicine - Quantitative Health, University of Florida at Gainesville, Florida (e-mail: [email protected]) G. Tourassi is with the Computing and Computational Sciences Directorate Oak Ridge National Laboratory, USA (e-mail: [email protected]). G. Tourassi acknowledges that "This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will
Keywords
- Artificial intelligence (AI)
- clinical workflow integration
- computational pathology
- convolutional neural networks (CNNs)
- ethics
- explainable AI
- federated learning
- foundation models
- generative AI
- medical imaging
- medical video analysis
- medical video analysis
- multimodal fusion
- regulatory compliance
- transformers
- trustworthy AI