Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers
Year: 2025
Type: article
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.
Authors: A. S. Panayides, H. Chen, N. D. Filipovic, T. Geroski, J. Hou, K. Lekadir, K. Marias, G. K. Matsopoulos, G. Papanastasiou, P. Sarder, G. Tourassi, S. A. Tsaftaris, H. Fu, E. Kyriacou, C. P. Loizou, M. Zervakis, J.H. Saltz, F. E. Shamout, K. C. L. Wong, J. Yao, A. Amini, D.I. Fotiadis, C.S. Pattichis, M.S. Pattichis
Institutions: Hong Kong University of Science and Technology, University of Kragujevac, Institució Catalana de Recerca i Estudis Avançats, Universitat de Barcelona, Hellenic Mediterranean University, Foundation for Research and Technology Hellas, National Technical University of Athens, Academy of Athens, University of Florida Health, University of Florida, Oak Ridge National Laboratory, University of Edinburgh, Agency for Science, Technology and Research, Institute of High Performance Computing, Cyprus University of Technology, Technical University of Crete, Stony Brook University, New York University Abu Dhabi, IBM Research - Almaden, Tencent (China), University of Louisville, University of Ioannina, University of Cyprus, University of New Mexico
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
Funders: U.S. Department of Energy, UT-Battelle
OpenAlex ID: W7117727641