{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:39:07Z","timestamp":1790699947085,"version":"4.1.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Biomed Eng"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision\u2013language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This review focuses on the state of the art rather than re-explaining well-established models. We summarize learning paradigms, contrast classical machine learning (ML) and deep learning (DL) families, and emphasize advances most relevant to clinical translation: medical foundation models, multimodal VLMs, hybrid CNN\u2013transformer architectures, diffusion-based augmentation, self-supervised pretraining, federated learning, and efficient deployment. We also discuss modality-specific issues across X-ray, CT, MRI, PET\/SPECT, ultrasound, OCT, endoscopy, microscopy, and optical\/molecular\/infrared imaging because model choice depends strongly on image structure, annotation cost, and workflow. Finally, we outline persistent clinical challenges, data diversity and bias, rare-condition detection, annotation noise, explainability, calibration, and equitable performance, and the methods that mitigate them. The aim is to provide biomedical engineers and clinicians with a compact, clinically grounded reference for selecting and validating AI-based classifiers for real medical workflows.<\/jats:p>","DOI":"10.1007\/s10439-026-04241-3","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:55:53Z","timestamp":1781632553000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging"],"prefix":"10.1007","author":[{"given":"Nafiseh","family":"Ghaffar Nia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rayyan","family":"Manwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1437-8456","authenticated-orcid":false,"given":"Kamran","family":"Avanaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"issue":"12","key":"4241_CR1","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.3390\/bioengineering10121435","volume":"10","author":"L Pinto-Coelho","year":"2023","unstructured":"Pinto-Coelho, L. How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications. Bioengineering. 10(12):1435, 2023.","journal-title":"Bioengineering"},{"issue":"4","key":"4241_CR2","doi-asserted-by":"publisher","first-page":"267","DOI":"10.51537\/chaos.1326790","volume":"5","author":"N Rasool","year":"2023","unstructured":"Rasool, N., and J. I. Bhat. Unveiling the complexity of medical imaging through deep learning approaches. Chaos Theory Appl. 5(4):267\u2013280, 2023.","journal-title":"Chaos Theory Appl."},{"issue":"1","key":"4241_CR3","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41746-023-00811-0","volume":"6","author":"S-C Huang","year":"2023","unstructured":"Huang, S.-C., A. Pareek, M. Jensen, M. P. Lungren, S. Yeung, and A. S. Chaudhari. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. NPJ Digit. Med. 6(1):74, 2023.","journal-title":"NPJ Digit. Med."},{"key":"4241_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101504","volume":"47","author":"ME Rayed","year":"2024","unstructured":"Rayed, M. E., S. S. Islam, S. I. Niha, J. R. Jim, M. M. Kabir, and M. F. Mridha. Deep learning for medical image segmentation: state-of-the-art advancements and challenges. Inform. Med. Unlocked.47:101504, 2024.","journal-title":"Inform. Med. Unlocked"},{"issue":"5","key":"4241_CR5","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3390\/info15050246","volume":"15","author":"KS Shakya","year":"2024","unstructured":"Shakya, K. S., A. Alavi, J. Porteous, P. K. A. Laddi, and M. Jaiswal. A critical analysis of deep semi-supervised learning approaches for enhanced medical image classification. Information. 15(5):246, 2024.","journal-title":"Information"},{"issue":"3","key":"4241_CR6","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1002\/mp.17547","volume":"52","author":"J Sun","year":"2025","unstructured":"Sun, J., T. Wang, M. Wang, X. Li, and Y. Xu. Semi-supervised medical image segmentation network based on mutual learning. Med. Phys. 52(3):1589\u20131600, 2025.","journal-title":"Med. Phys."},{"key":"4241_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102263","volume":"106","author":"Y Weng","year":"2024","unstructured":"Weng, Y., Y. Zhang, W. Wang, and T. Dening. Semi-supervised information fusion for medical image analysis: recent progress and future perspectives. Inform. Fusion.106:102263, 2024.","journal-title":"Inform. Fusion"},{"issue":"1","key":"4241_CR8","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1038\/s41746-025-02035-w","volume":"8","author":"T Xu","year":"2025","unstructured":"Xu, T., et al. A generalizable 3D framework and model for self-supervised learning in medical imaging. NPJ Digit. Med. 8(1):639, 2025.","journal-title":"NPJ Digit. Med."},{"issue":"4","key":"4241_CR9","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/computation13040088","volume":"13","author":"A Gupta","year":"2025","unstructured":"Gupta, A., I. Osman, M. S. Shehata, W. J. Braun, and R. E. Feldman. Medmae: a self-supervised backbone for medical imaging tasks. Computation. 13(4):88, 2025.","journal-title":"Computation"},{"issue":"1","key":"4241_CR10","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1038\/s41467-025-56321-y","volume":"16","author":"CI Bercea","year":"2025","unstructured":"Bercea, C. I., B. Wiestler, D. Rueckert, and J. A. Schnabel. Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging. Nat. Commun. 16(1):1624, 2025.","journal-title":"Nat. Commun."},{"key":"4241_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2025.102560","volume":"123","author":"X Huang","year":"2025","unstructured":"Huang, X., et al. Tailored self-supervised pretraining improves brain MRI diagnostic models. Comput. Med. Imaging Graph.123:102560, 2025.","journal-title":"Comput. Med. Imaging Graph."},{"key":"4241_CR12","doi-asserted-by":"publisher","first-page":"11465","DOI":"10.1038\/s41467-025-66267-w","volume":"16","author":"RS Murthy","year":"2025","unstructured":"Murthy, R. S., S. V. Stassen, D. M. Siu, M. C. Lo, G. G. Yip, and K. K. Tsia. Generalizable morphological profiling of cells by interpretable unsupervised learning. Nat. Commun. 16:11465, 2025.","journal-title":"Nat. Commun."},{"key":"4241_CR13","doi-asserted-by":"publisher","first-page":"103375","DOI":"10.1016\/j.artmed.2026.103375","volume":"175","author":"S Kumari","year":"2026","unstructured":"Kumari, S., and P. Singh. Learning with less: a survey of deep learning in medical imaging under varying supervision levels. Artif. Intell. Med. 175:103375, 2026.","journal-title":"Artif. Intell. Med."},{"key":"4241_CR14","doi-asserted-by":"crossref","unstructured":"Bannur, S. et al., Learning to exploit temporal structure for biomedical vision-language processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 15016\u201315027.","DOI":"10.1109\/CVPR52729.2023.01442"},{"issue":"1","key":"4241_CR15","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/s41467-025-66620-z","volume":"17","author":"Z Gao","year":"2025","unstructured":"Gao, Z., et al. A lung CT vision foundation model facilitating disease diagnosis and medical imaging. Nat. Commun. 17(1):35, 2025.","journal-title":"Nat. Commun."},{"issue":"1","key":"4241_CR16","doi-asserted-by":"publisher","first-page":"4542","DOI":"10.1038\/s41467-023-40260-7","volume":"14","author":"X Zhang","year":"2023","unstructured":"Zhang, X., C. Wu, Y. Zhang, W. Xie, and Y. Wang. Knowledge-enhanced visual-language pre-training on chest radiology images. Nat. Commun. 14(1):4542, 2023.","journal-title":"Nat. Commun."},{"issue":"15","key":"4241_CR17","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.3390\/cancers16152668","volume":"16","author":"V Trojani","year":"2024","unstructured":"Trojani, V., M. C. Bassi, L. Verzellesi, and M. Bertolini. Impact of preprocessing parameters in medical imaging-based radiomic studies: a systematic review. Cancers. 16(15):2668, 2024.","journal-title":"Cancers"},{"issue":"1","key":"4241_CR18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s12880-024-01253-0","volume":"24","author":"B VanBerlo","year":"2024","unstructured":"VanBerlo, B., J. Hoey, and A. Wong. A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound. BMC Med. Imaging. 24(1):79, 2024.","journal-title":"BMC Med. Imaging"},{"key":"4241_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103444","volume":"101","author":"S Brussee","year":"2025","unstructured":"Brussee, S., G. Buzzanca, A. M. Schrader, and J. Kers. Graph neural networks in histopathology: emerging trends and future directions. Med. Image Anal.101:103444, 2025.","journal-title":"Med. Image Anal."},{"issue":"1","key":"4241_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3390\/brainsci15010017","volume":"15","author":"H Mohammadi","year":"2024","unstructured":"Mohammadi, H., and W. Karwowski. Graph neural networks in brain connectivity studies: methods, challenges, and future directions. Brain Sci. 15(1):17, 2024.","journal-title":"Brain Sci."},{"key":"4241_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108635","volume":"177","author":"Y Li","year":"2024","unstructured":"Li, Y., et al. A review of deep learning-based information fusion techniques for multimodal medical image classification. Comput. Biol. and Med.177:108635, 2024.","journal-title":"Comput. Biol. and Med."},{"issue":"9","key":"4241_CR22","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3390\/info15090517","volume":"15","author":"ID Mienye","year":"2024","unstructured":"Mienye, I. D., T. G. Swart, and G. Obaido. Recurrent neural networks: a comprehensive review of architectures, variants, and applications. Information. 15(9):517, 2024.","journal-title":"Information"},{"issue":"5","key":"4241_CR23","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/s42452-025-06644-8","volume":"7","author":"G Mushtaq","year":"2025","unstructured":"Mushtaq, G., and K. Veningston. A comprehensive review on fetal health surveillance using ultrasound imagery. Discov. Appl. Sci. 7(5):478, 2025.","journal-title":"Discov. Appl. Sci."},{"issue":"1","key":"4241_CR24","doi-asserted-by":"publisher","first-page":"6862","DOI":"10.1038\/s41467-025-62123-z","volume":"16","author":"J Engelmann","year":"2025","unstructured":"Engelmann, J., and M. O. Bernabeu. Training a high-performance retinal foundation model with half-the-data and 400 times less compute. Nat. Commun. 16(1):6862, 2025.","journal-title":"Nat. Commun."},{"issue":"12","key":"4241_CR25","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.3390\/medicina59122114","volume":"59","author":"M Zeppieri","year":"2023","unstructured":"Zeppieri, M., et al. Optical coherence tomography (OCT): a brief look at the uses and technological evolution of ophthalmology. Medicina. 59(12):2114, 2023.","journal-title":"Medicina"},{"issue":"1","key":"4241_CR26","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1038\/s41746-025-01772-2","volume":"8","author":"D Shi","year":"2025","unstructured":"Shi, D., et al. A multimodal visual\u2013language foundation model for computational ophthalmology. NPJ Digit. Med. 8(1):381, 2025.","journal-title":"NPJ Digit. Med."},{"issue":"11","key":"4241_CR27","doi-asserted-by":"publisher","first-page":"275","DOI":"10.3390\/jimaging10110275","volume":"10","author":"Z Nie","year":"2024","unstructured":"Nie, Z., M. Xu, Z. Wang, X. Lu, and W. Song. A review of application of deep learning in endoscopic image processing. J. Imaging. 10(11):275, 2024.","journal-title":"J. Imaging"},{"key":"4241_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102949","volume":"156","author":"E Pachetti","year":"2024","unstructured":"Pachetti, E., and S. Colantonio. A systematic review of few-shot learning in medical imaging. Artif. Intell. Med.156:102949, 2024.","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"4241_CR29","doi-asserted-by":"publisher","first-page":"14069","DOI":"10.1038\/s41598-023-39738-7","volume":"13","author":"A Decoux","year":"2023","unstructured":"Decoux, A., et al. Comparative performances of machine learning algorithms in radiomics and impacting factors. Sci. Rep. 13(1):14069, 2023.","journal-title":"Sci. Rep."},{"issue":"10","key":"4241_CR30","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10462-024-10884-2","volume":"57","author":"M Salmi","year":"2024","unstructured":"Salmi, M., D. Atif, D. Oliva, A. Abraham, and S. Ventura. Handling imbalanced medical datasets: review of a decade of research. Artif. Intell. Rev. 57(10):273, 2024.","journal-title":"Artif. Intell. Rev."},{"issue":"15","key":"4241_CR31","doi-asserted-by":"publisher","first-page":"3845","DOI":"10.3390\/cancers15153845","volume":"15","author":"M Tabassum","year":"2023","unstructured":"Tabassum, M., A. A. Suman, E. Suero Molina, E. Pan, A. Di Ieva, and S. Liu. Radiomics and machine learning in brain tumors and their habitat: a systematic review. Cancers. 15(15):3845, 2023.","journal-title":"Cancers"},{"key":"4241_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An introduction to statistical learning: with applications in R","author":"G James","year":"2013","unstructured":"James, G., D. Witten, T. Hastie, and R. Tibshirani. An introduction to statistical learning: with applications in R. Springer, 2013."},{"issue":"1","key":"4241_CR33","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s40537-024-00973-y","volume":"11","author":"RK Halder","year":"2024","unstructured":"Halder, R. K., M. N. Uddin, M. A. Uddin, S. Aryal, and A. Khraisat. Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications. J. Big Data. 11(1):113, 2024.","journal-title":"J. Big Data"},{"key":"4241_CR34","doi-asserted-by":"publisher","first-page":"39811","DOI":"10.1109\/ACCESS.2024.3375755","volume":"12","author":"BM Kanber","year":"2024","unstructured":"Kanber, B. M., A. Al Smadi, N. F. Noaman, B. Liu, S. Gou, and M. K. Alsmadi. Lightgbm: a leading force in breast cancer diagnosis through machine learning and image processing. IEEE Access. 12:39811\u201339832, 2024.","journal-title":"IEEE Access"},{"issue":"6","key":"4241_CR35","doi-asserted-by":"publisher","first-page":"987","DOI":"10.32604\/iasc.2024.059429","volume":"39","author":"E Halabaku","year":"2024","unstructured":"Halabaku, E., and E. Byty\u00e7i. Overfitting in machine learning: a comparative analysis of decision trees and random forests. Intell. Autom. Soft Comput. 39(6):987\u20131006, 2024.","journal-title":"Intell. Autom. Soft Comput."},{"key":"4241_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109507","volume":"185","author":"C Chen","year":"2025","unstructured":"Chen, C., N. A. M. Isa, and X. Liu. A review of convolutional neural network based methods for medical image classification. Comput. Biol. Med.185:109507, 2025.","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"4241_CR37","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10462-024-10992-z","volume":"58","author":"Y Heng","year":"2024","unstructured":"Heng, Y., et al. Survey: application and analysis of generative adversarial networks in medical images. Artif. Intell. Rev. 58(2):39, 2024.","journal-title":"Artif. Intell. Rev."},{"key":"4241_CR38","volume":"87","author":"R Kumar","year":"2026","unstructured":"Kumar, R., Y.-W. Kim, and Y.-C. Byun. Multi-tier data augmentation and balancing framework integrating diffusion, tomek link, and SMOTE (DiToS) for robust image-based fault detection in solar panels. Sustain. Energy Technol. Assess.87:104916, 2026.","journal-title":"Sustain. Energy Technol. Assess."},{"issue":"2","key":"4241_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.3390\/diagnostics16020211","volume":"16","author":"M Azad","year":"2026","unstructured":"Azad, M., et al. A Systematic review of diffusion models for medical image-based diagnosis: methods, taxonomies, clinical integration, explainability, and future directions. Diagnostics. 16(2):211, 2026.","journal-title":"Diagnostics"},{"key":"4241_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108384","volume":"256","author":"Z Zhu","year":"2024","unstructured":"Zhu, Z., Y. Liu, C.-A. Yuan, X. Qin, and F. Yang. A diffusion model multi-scale feature fusion network for imbalanced medical image classification research. Comput. Methods Programs Biomed.256:108384, 2024.","journal-title":"Comput. Methods Programs Biomed."},{"key":"4241_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari, O. N., H. Ahmadabadi, H. Kashiani, S. B. Shokouhi, and A. Ayatollahi. MedViT: a robust vision transformer for generalized medical image classification. Comput. Biol. Med.157:106791, 2023.","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"4241_CR42","doi-asserted-by":"publisher","first-page":"3248","DOI":"10.1007\/s10278-024-01322-4","volume":"38","author":"JW Kim","year":"2025","unstructured":"Kim, J. W., A. U. Khan, and I. Banerjee. Systematic review of hybrid vision transformer architectures for radiological image analysis. J. Imaging Inform. Med. 38(5):3248\u20133262, 2025.","journal-title":"J. Imaging Inform. Med."},{"issue":"1","key":"4241_CR43","doi-asserted-by":"publisher","first-page":"41936","DOI":"10.1038\/s41598-025-25857-w","volume":"15","author":"M Ferdous","year":"2025","unstructured":"Ferdous, M., S. Mahmud, and M. E. Z. Shimul. MedNet: a lightweight attention-augmented CNN for medical image classification. Sci. Rep. 15(1):41936, 2025.","journal-title":"Sci. Rep."},{"issue":"1","key":"4241_CR44","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10916-024-02105-8","volume":"48","author":"S Takahashi","year":"2024","unstructured":"Takahashi, S., et al. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J. Med. Syst. 48(1):84, 2024.","journal-title":"J. Med. Syst."},{"issue":"1","key":"4241_CR45","doi-asserted-by":"publisher","first-page":"29300","DOI":"10.1038\/s41598-024-78566-1","volume":"14","author":"S Yin","year":"2024","unstructured":"Yin, S., et al. Brain CT image classification based on mask RCNN and attention mechanism. Sci. Rep. 14(1):29300, 2024.","journal-title":"Sci. Rep."},{"issue":"3","key":"4241_CR46","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/s42256-024-00807-9","volume":"6","author":"S Pai","year":"2024","unstructured":"Pai, S., et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6(3):354\u2013367, 2024.","journal-title":"Nat. Mach. Intell."},{"issue":"3","key":"4241_CR47","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","volume":"30","author":"MY Lu","year":"2024","unstructured":"Lu, M. Y., et al. A visual-language foundation model for computational pathology. Nat. Med. 30(3):863\u2013874, 2024.","journal-title":"Nat. Med."},{"issue":"8015","key":"4241_CR48","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","volume":"630","author":"H Xu","year":"2024","unstructured":"Xu, H., et al. A whole-slide foundation model for digital pathology from real-world data. Nature. 630(8015):181\u2013188, 2024.","journal-title":"Nature"},{"key":"4241_CR49","doi-asserted-by":"publisher","first-page":"3749","DOI":"10.1038\/s41591-025-03982-3","volume":"31","author":"T Ding","year":"2025","unstructured":"Ding, T., et al. A multimodal whole-slide foundation model for pathology. Nat. Med. 31:3749\u20133761, 2025.","journal-title":"Nat. Med."},{"issue":"1","key":"4241_CR50","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/s41467-025-56822-w","volume":"16","author":"C Niu","year":"2025","unstructured":"Niu, C., et al. Medical multimodal multitask foundation model for lung cancer screening. Nat. Commun. 16(1):1523, 2025.","journal-title":"Nat. Commun."},{"issue":"8","key":"4241_CR51","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1038\/s41591-025-03747-y","volume":"31","author":"S Yan","year":"2025","unstructured":"Yan, S., et al. A multimodal vision foundation model for clinical dermatology. Nat. Med. 31(8):2691\u20132702, 2025.","journal-title":"Nat. Med."},{"key":"4241_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2025.108870","volume":"268","author":"Y Sun","year":"2025","unstructured":"Sun, Y., et al. Visual-language foundation models in medical imaging: a systematic review and meta-analysis of diagnostic and analytical applications. Comput. Methods Programs Biomed.268:108870, 2025.","journal-title":"Comput. Methods Programs Biomed."},{"issue":"7972","key":"4241_CR53","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal, K., et al. Large language models encode clinical knowledge. Nature. 620(7972):172\u2013180, 2023.","journal-title":"Nature"},{"key":"4241_CR54","doi-asserted-by":"crossref","first-page":"28541","DOI":"10.52202\/075280-1240","volume":"36","author":"C Li","year":"2023","unstructured":"Li, C., et al. Llava-med: training a large language-and-vision assistant for biomedicine in one day. Adv. Neural Inf. Process. Syst. 36:28541\u201328564, 2023.","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"4241_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2024.100099","volume":"2","author":"Y Liu","year":"2024","unstructured":"Liu, Y., et al. A systematic evaluation of GPT-4V\u2019s multimodal capability for chest X-ray image analysis. Meta-Radiol.2(4):100099, 2024.","journal-title":"Meta-Radiol."},{"issue":"2","key":"4241_CR56","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1038\/s41591-024-03302-1","volume":"31","author":"R Tanno","year":"2025","unstructured":"Tanno, R., et al. Collaboration between clinicians and vision\u2013language models in radiology report generation. Nat. Med. 31(2):599\u2013608, 2025.","journal-title":"Nat. Med."},{"issue":"13","key":"4241_CR57","doi-asserted-by":"publisher","first-page":"eadq0305","DOI":"10.1126\/sciadv.adq0305","volume":"11","author":"Y Yang","year":"2025","unstructured":"Yang, Y., et al. Demographic bias of expert-level vision-language foundation models in medical imaging. Sci. Adv. 11(13):eadq0305, 2025.","journal-title":"Sci. Adv."},{"key":"4241_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103201","volume":"95","author":"H Wang","year":"2024","unstructured":"Wang, H., Q. Jin, S. Li, S. Liu, M. Wang, and Z. Song. A comprehensive survey on deep active learning in medical image analysis. Med. Image Anal.95:103201, 2024.","journal-title":"Med. Image Anal."},{"key":"4241_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105012","volume":"146","author":"A Davila","year":"2024","unstructured":"Davila, A., J. Colan, and Y. Hasegawa. Comparison of fine-tuning strategies for transfer learning in medical image classification. Image Vis. Comput.146:105012, 2024.","journal-title":"Image Vis. Comput."},{"key":"4241_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122807","volume":"242","author":"Z Zhao","year":"2024","unstructured":"Zhao, Z., L. Alzubaidi, J. Zhang, Y. Duan, and Y. Gu. A comparison review of transfer learning and self-supervised learning: definitions, applications, advantages and limitations. Expert Syst. Appl.242:122807, 2024.","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"4241_CR61","doi-asserted-by":"publisher","first-page":"22576","DOI":"10.1038\/s41598-023-49956-8","volume":"13","author":"S Tayebi Arasteh","year":"2023","unstructured":"Tayebi Arasteh, S., C. Kuhl, M.-J. Saehn, P. Isfort, D. Truhn, and S. Nebelung. Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning. Sci. Rep. 13(1):22576, 2023.","journal-title":"Sci. Rep."},{"issue":"1150","key":"4241_CR62","doi-asserted-by":"publisher","first-page":"20220890","DOI":"10.1259\/bjr.20220890","volume":"96","author":"MHU Rehman","year":"2023","unstructured":"Rehman, M. H. U., W. Hugo Lopez Pinaya, P. Nachev, J. T. Teo, S. Ourselin, and M. J. Cardoso. Federated learning for medical imaging radiology. Br. J. Radiol. 96(1150):20220890, 2023.","journal-title":"Br. J. Radiol."},{"issue":"1","key":"4241_CR63","doi-asserted-by":"publisher","first-page":"12482","DOI":"10.1038\/s41598-025-97565-4","volume":"15","author":"R Haripriya","year":"2025","unstructured":"Haripriya, R., N. Khare, and M. Pandey. Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings. Sci. Rep. 15(1):12482, 2025.","journal-title":"Sci. Rep."},{"issue":"6","key":"4241_CR64","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1007\/s10278-024-01173-z","volume":"37","author":"P Kulkarni","year":"2024","unstructured":"Kulkarni, P., A. Kanhere, E. L. Siegel, P. H. Yi, and V. S. Parekh. ISLE: an intelligent streaming framework for high-throughput AI inference in medical imaging. J. Imaging Inform. Med. 37(6):3250\u20133263, 2024.","journal-title":"J. Imaging Inform. Med."},{"issue":"1","key":"4241_CR65","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41746-024-01248-9","volume":"7","author":"K Wenderott","year":"2024","unstructured":"Wenderott, K., J. Krups, F. Zaruchas, and M. Weigl. Effects of artificial intelligence implementation on efficiency in medical imaging\u2014a systematic literature review and meta-analysis. NPJ Digit. Med. 7(1):265, 2024.","journal-title":"NPJ Digit. Med."},{"issue":"11","key":"4241_CR66","doi-asserted-by":"publisher","first-page":"9591","DOI":"10.1007\/s11042-024-19192-x","volume":"84","author":"L Mohanty","year":"2025","unstructured":"Mohanty, L., A. Kumar, V. Mehta, M. Agarwal, and J. S. Suri. Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind. Multimed. Tools Appl. 84(11):9591\u20139665, 2025.","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"4241_CR67","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1038\/s41746-025-01597-z","volume":"8","author":"RS Gupta","year":"2025","unstructured":"Gupta, R. S., C. E. Wood, T. Engstrom, J. D. Pole, and S. Shrapnel. A systematic review of quantum machine learning for digital health. NPJ Digit. Med. 8(1):237, 2025.","journal-title":"NPJ Digit. Med."},{"issue":"1","key":"4241_CR68","doi-asserted-by":"publisher","DOI":"10.1002\/der2.70018","volume":"6","author":"DREK Kakish","year":"2025","unstructured":"Kakish, D. R. E. K., et al. Transforming dermatopathology with AI: addressing bias, enhancing interpretability, and shaping future diagnostics. Dermatol. Rev.6(1):e70018, 2025.","journal-title":"Dermatol. Rev."},{"issue":"6","key":"4241_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3637487","volume":"57","author":"MI Hossain","year":"2025","unstructured":"Hossain, M. I., G. Zamzmi, P. R. Mouton, M. S. Salekin, Y. Sun, and D. Goldgof. Explainable AI for medical data: current methods, limitations, and future directions. ACM Comput. Surv. 57(6):1\u201346, 2025.","journal-title":"ACM Comput. Surv."},{"issue":"8","key":"4241_CR70","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1158\/0008-5472.CAN-18-2791","volume":"79","author":"Z Turani","year":"2019","unstructured":"Turani, Z., et al. Optical radiomic signatures derived from optical coherence tomography images improve identification of Melanoma. Cancer Res. 79(8):2021\u20132030, 2019.","journal-title":"Cancer Res."},{"issue":"10","key":"4241_CR71","doi-asserted-by":"publisher","DOI":"10.1002\/jbio.202000212","volume":"13","author":"R Manwar","year":"2020","unstructured":"Manwar, R., X. Li, S. Mahmoodkalayeh, E. Asano, D. Zhu, and K. Avanaki. Deep learning protocol for improved photoacoustic brain imaging. J. Biophotonics.13(10):e202000212, 2020.","journal-title":"J. Biophotonics"}],"container-title":["Annals of Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-026-04241-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-026-04241-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-026-04241-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:15:57Z","timestamp":1782929757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10439-026-04241-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":71,"alternative-id":["4241"],"URL":"https:\/\/doi.org\/10.1007\/s10439-026-04241-3","relation":{},"ISSN":["0090-6964","1573-9686"],"issn-type":[{"value":"0090-6964","type":"print"},{"value":"1573-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"10 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}