{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T14:05:23Z","timestamp":1774361123269,"version":"3.50.1"},"reference-count":104,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National University of Science and Technology POLITEHNICA Bucharest"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In recent years, the integration of advanced methods in medical imaging has become a major topic of interest due to its potential to enhance diagnostic accuracy, improve clinical efficiency, and increase specialists\u2019 confidence in Artificial Intelligence (AI)-based decision-making. This paper explores the synthesis of Explainable AI (XAI) and Generative AI (GAI) in medical imaging, highlighting the advantages and challenges of these emerging technologies. The objective of this paper is to explore how the combined use of XAI and GAI contributes both to interpretability and to diagnostic accuracy. This research represents a systematic literature review conducted in accordance with PRISMA 2020, based on searches carried out in the PubMed, Scopus, IEEE Xplore, MDPI and ScienceDirect databases. Thus, a comprehensive overview of the integration of XAI and GAI in medical imaging is presented, based on recent studies and validated clinical applications. The advantages of combining transparency and data amplification in diagnostic models are highlighted, demonstrating their complementary roles in improving diagnosis using medical imaging. Ongoing challenges in clinical adoption are also emphasised, including interpretability and the need for validated assessment metrics. Beyond technological benefits, the paper also underlines the importance of ethical and legal considerations in the use of XAI and GAI in medical imaging. Based on the detailed analysis of the investigated studies, the paper also proposes a visual and architectural system concept intended for medical imaging, oriented towards research into the development of a unified system capable of detecting multiple types of pathologies. This research provides a detailed perspective on how XAI and GAI can revolutionise medical imaging by optimising data interpretation, enhancing human-AI collaboration, and increasing patient safety.<\/jats:p>","DOI":"10.3390\/a19040244","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:58:59Z","timestamp":1774357139000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Smart Medical Image Processing System Based on Explainable and Generative Artificial Intelligence: A Comprehensive Review"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0868-8195","authenticated-orcid":false,"given":"Cosmin George","family":"Nicol\u0103escu","sequence":"first","affiliation":[{"name":"Doctoral School of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 061071 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florentina Magda","family":"Enescu","sequence":"additional","affiliation":[{"name":"Pite\u0219ti University Centre, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pite\u0219ti, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3218-9218","authenticated-orcid":false,"given":"Alin Gheorghi\u021b\u0103","family":"Maz\u0103re","sequence":"additional","affiliation":[{"name":"Pite\u0219ti University Centre, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pite\u0219ti, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9311-7598","authenticated-orcid":false,"given":"Nicu","family":"Bizon","sequence":"additional","affiliation":[{"name":"Doctoral School of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 061071 Bucharest, Romania"},{"name":"Pite\u0219ti University Centre, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pite\u0219ti, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4396-8081","authenticated-orcid":false,"given":"Cristian","family":"Toma","sequence":"additional","affiliation":[{"name":"Center of Innovation and e-Health, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"ref_1","unstructured":"Prentzas, N., Kakas, A., and Pattichis, C.S. (2023). Explainable AI applications in the Medical Domain: A systematic review. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"35728","DOI":"10.1109\/ACCESS.2024.3370848","article-title":"Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges","volume":"12","author":"Islam","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s10278-014-9689-9","article-title":"A Web Simulation of Medical Image Reconstruction and Processing as an Educational Tool","volume":"28","author":"Papamichail","year":"2015","journal-title":"J. Digit. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bercovich, E., and Javitt, M.C. (2018). Medical Imaging: From Roentgen to the Digital Revolution, and Beyond. Rambam Maimonides Med. J., 9.","DOI":"10.5041\/RMMJ.10355"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Singh, J., Goyal, S., Kaushal, R.K., Kumar, N., and Sehra, S.S. (2024). Applied Data Science and Smart Systems, CRC Press. [1st ed.].","DOI":"10.1201\/9781003471059"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100146","DOI":"10.1016\/j.cmpbup.2024.100146","article-title":"AI in diagnostic imaging: Revolutionising accuracy and efficiency","volume":"5","author":"Khalifa","year":"2024","journal-title":"Comput. Methods Programs Biomed. Update"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111884","DOI":"10.1016\/j.ejrad.2024.111884","article-title":"Current status and future directions of explainable artificial intelligence in medical imaging","volume":"183","author":"Saw","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.21037\/qims-23-1600","article-title":"Application of convolutional neural networks in medical images: A bibliometric analysis","volume":"14","author":"Jia","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/978-3-031-58181-6_11","article-title":"Is Grad-CAM Explainable in Medical Images?","volume":"Volume 2009","author":"Suara","year":"2024","journal-title":"Computer Vision and Image Processing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2371","DOI":"10.52783\/jes.3220","article-title":"Diagnosis of Medical Images Using Convolutional Neural Networks","volume":"20","author":"Desai","year":"2024","journal-title":"J. Electr. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9620","DOI":"10.21037\/qims-24-723","article-title":"A literature review of artificial intelligence (AI) for medical image segmentation: From AI and explainable AI to trustworthy AI","volume":"14","author":"Teng","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.procs.2020.06.005","article-title":"Super-Resolution using GANs for Medical Imaging","volume":"173","author":"Gupta","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","unstructured":"Du, W., and Tian, H. (2022). Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"821","DOI":"10.21037\/atm-20-6325","article-title":"Narrative review of generative adversarial networks in medical and molecular imaging","volume":"9","author":"Koshino","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4506214","DOI":"10.1109\/TIM.2023.3296838","article-title":"3DSRNet: 3-D Spine Reconstruction Network Using 2-D Orthogonal X-Ray Images Based on Deep Learning","volume":"72","author":"Gao","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7395","DOI":"10.1109\/TNNLS.2024.3386610","article-title":"A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans","volume":"36","author":"Yao","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1109\/TPAMI.2023.3346330","article-title":"A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation","volume":"46","author":"Fidon","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8385","DOI":"10.1109\/JBHI.2025.3578625","article-title":"A Multi-Resolution Hybrid CNN-Transformer Network With Scale-Guided Attention for Medical Image Segmentation","volume":"29","author":"Zhu","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"55448","DOI":"10.1109\/ACCESS.2024.3390245","article-title":"HLSNC-GAN: Medical Image Synthesis Using Hinge Loss and Switchable Normalization in CycleGAN","volume":"12","author":"Heng","year":"2024","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1109\/JBHI.2023.3336721","article-title":"Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT","volume":"29","author":"Liu","year":"2025","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/RBME.2023.3269776","article-title":"Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review","volume":"17","author":"Wang","year":"2024","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113623","DOI":"10.1109\/ACCESS.2023.3313977","article-title":"Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification","volume":"11","author":"Mahmood","year":"2023","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/RBME.2025.3528946","article-title":"Review of Artificial Intelligence in Lung Nodule Risk Assessment","volume":"19","author":"Wei","year":"2025","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"58229","DOI":"10.1109\/ACCESS.2025.3555543","article-title":"State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis","volume":"13","author":"Amirian","year":"2025","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101214","DOI":"10.1016\/j.imr.2025.101214","article-title":"A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff","volume":"14","author":"Gu","year":"2025","journal-title":"Integr. Med. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101146","DOI":"10.1016\/j.measen.2024.101146","article-title":"Application of artificial intelligence digital holography technology based on medical sensors in the development of medical image fusion","volume":"33","author":"Zhong","year":"2024","journal-title":"Meas. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100218","DOI":"10.1016\/j.slast.2024.100218","article-title":"Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening","volume":"29","author":"Guo","year":"2024","journal-title":"SLAS Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100751","DOI":"10.1016\/j.eij.2025.100751","article-title":"Enhancing cancer detection in medical imaging through federated learning and explainable artificial intelligence: A hybrid approach for optimized diagnostics","volume":"31","author":"Karthiga","year":"2025","journal-title":"Egypt. Inform. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"116982","DOI":"10.1016\/j.bios.2024.116982","article-title":"Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo","volume":"271","author":"Song","year":"2025","journal-title":"Biosens. Bioelectron."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Khiat, S., Mahmoudi, S.A., Stassin, S., Boukerroui, L., Sena\u00ef, B., and Mahmoudi, S. (2025). An Efficient Explainability of Deep Models on Medical Images. Algorithms, 18.","DOI":"10.3390\/a18040210"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Adeniran, O.T., Ojeme, B., Ajibola, T.E., Peter, O.O.E., Ajala, A.O., Rahman, M.M., and Khalifa, F. (2025). Explainable MRI-based ensemble learnable architecture for Alzheimer\u2019s disease detection. Algorithms, 18.","DOI":"10.3390\/a18030163"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alam, M.M., and Latifi, S. (2025). Early Detection of Alzheimer\u2019s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging. Algorithms, 18.","DOI":"10.20944\/preprints202506.1854.v1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhabinets, M., Tyler, B., Lukac, M., Nagayama, S., Moln\u00e1r, F., and Kameyama, M. (2025). Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability. Algorithms, 18.","DOI":"10.3390\/a18060310"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"\u0160i\u0161i\u0107, N., and Rogelj, P. (2025). Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review. Algorithms, 18.","DOI":"10.3390\/a18100636"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Raghavan, K., Balasubramanian, S., and Veezhinathan, K. (2024). Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images. Comput. Intell., 40.","DOI":"10.1111\/coin.12660"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.csbj.2024.08.005","article-title":"Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis","volume":"24","author":"Muhammad","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Webber, G., and Reader, A.J. (2024). Diffusion models for medical image reconstruction. BJR Artif. Intell., 1.","DOI":"10.1093\/bjrai\/ubae013"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1146\/annurev-bioeng-102723-013922","article-title":"Physics-Inspired Generative Models in Medical Imaging","volume":"27","author":"Hein","year":"2025","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Usama, Z., Alavi, A., and Chan, J. (2025). A Review on the Applications of GANs for 3D Medical Image Analysis. Appl. Sci., 15.","DOI":"10.3390\/app152011219"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"Kuijf","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1038\/s41598-023-29521-z","article-title":"Unsupervised anomaly detection with generative adversarial networks in mammography","volume":"13","author":"Park","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101695","DOI":"10.1016\/j.imu.2025.101695","article-title":"D-A GAN: A novel Dual-Attention GAN for efficient and explainable medical anomaly detection","volume":"58","author":"Ounasser","year":"2025","journal-title":"Inform. Med. Unlocked"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Biswas, S., Mostafiz, R., Uddin, M.S., and Uddin, M.S. (2025). FLPneXAINet: Federated deep learning and explainable AI for improved pneumonia prediction utilizing GAN-augmented chest X-ray data. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0324957"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mertes, S., Huber, T., Weitz, K., Heimerl, A., and Andr\u00e9, E. (2022). GANterfactual\u2014Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning. Front. Artif. Intell., 5.","DOI":"10.3389\/frai.2022.825565"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jiang, X., Hu, Z., Wang, S., and Zhang, Y. (2023). Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers, 15.","DOI":"10.3390\/cancers15143608"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hafeez, Y., Memon, K., AL-Quraishi, M.S., Yahya, N., Elferik, S., and Ali, S.S.A. (2025). Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It. Diagnostics, 15.","DOI":"10.3390\/diagnostics15020168"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Cukur, T., Greenspan, H., and Yang, G. (2023). Editorial: Generative adversarial networks in cardiovascular research. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1307812"},{"key":"ref_48","unstructured":"Xie, R., Chen, J., Jiang, L., Xiao, R., Pan, Y., and Cai, Y. (2023). Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Haddaway, N.R., Page, M.J., Pritchard, C.C., and McGuinness, L.A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst. Rev., 18.","DOI":"10.1002\/cl2.1230"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"110787","DOI":"10.1016\/j.ejrad.2023.110787","article-title":"Explainable AI in medical imaging: An overview for clinical practitioners\u2014Saliency-based XAI approaches","volume":"162","author":"Borys","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sheu, R.-K., and Pardeshi, M.S. (2022). A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System. Sensors, 22.","DOI":"10.3390\/s22208068"},{"key":"ref_52","unstructured":"Nagisetty, V., Graves, L., Scott, J., and Ganesh, V. (2022). xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems. arXiv."},{"key":"ref_53","unstructured":"Dagnaw, G.H., Zhu, Y., Maqsood, M.H., Yang, W., Dong, X., Yin, X., and Liew, A.W.-C. (2025). Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Skandarani, Y., Jodoin, P.-M., and Lalande, A. (2023). GANs for Medical Image Synthesis: An Empirical Study. J. Imaging, 9.","DOI":"10.3390\/jimaging9030069"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bhati, D., Neha, F., and Amiruzzaman, M. (2024). A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. J. Imaging, 10.","DOI":"10.20944\/preprints202408.0765.v1"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Peng, J., Xu, J., and Bouridane, A. (2023). Survey of Explainable AI Techniques in Healthcare. Sensors, 23.","DOI":"10.3390\/s23020634"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Imoize, A.L., Hemanth, J., Do, D.-T., and Sur, S.N. (2022). XAI methods for precision medicine in medical decision support systems. Explainable Artificial Intelligence in Medical Decision Support Systems, IET.","DOI":"10.1049\/PBHE050E"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"26419","DOI":"10.1109\/ACCESS.2024.3367606","article-title":"Application of Example-Based Explainable Artificial Intelligence (XAI) for Analysis and Interpretation of Medical Imaging: A Systematic Review","volume":"12","author":"Fontes","year":"2024","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","article-title":"GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification","volume":"321","author":"Diamant","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2330524","DOI":"10.1080\/21681163.2024.2330524","article-title":"Application of generative adversarial networks in image, face reconstruction and medical imaging: Challenges and the current progress","volume":"12","author":"Sabnam","year":"2024","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Cagas, W., Ko, C., Hsiao, B., Grandhi, S., Bhattacharya, R., Zhu, K., and Lam, M. (2024). Medical Imaging Complexity and its Effects on GAN Performance. arXiv.","DOI":"10.1007\/978-981-96-2641-0_14"},{"key":"ref_62","unstructured":"Pinaya, W.H.L., Graham, M.S., Kerfoot, E., Tudosiu, P.-D., Dafflon, J., Fernandez, V., Sanchez, P., Wolleb, J., da Costa, P.F., and Patel, A. (2023). Generative AI for Medical Imaging: Extending the MONAI Framework. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Dravid, A., Schiffers, F., Gong, B., and Katsaggelos, A.K. (2022). medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space. arXiv.","DOI":"10.1109\/CVPRW56347.2022.00331"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"105075","DOI":"10.1016\/j.ebiom.2024.105075","article-title":"Using generative AI to investigate medical imagery models and datasets","volume":"102","author":"Lang","year":"2024","journal-title":"eBioMedicine"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Enescu, F.M., Cosmin-George, N., and Bizon, N. (2023). Smart System using Blockchain for Medical Diagnosis. Proceedings of the 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Bucharest, Romania, IEEE.","DOI":"10.1109\/ECAI58194.2023.10194219"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Nicol\u0103escu, C.-G., Enescu, F.M., and Bizon, N. (2024). Designing of Medical Diagnostic System based on Blockchain and Artificial Intelligence Technologies. Proceedings of the 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, IEEE.","DOI":"10.1109\/ECAI61503.2024.10607584"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ahmed, A.M.A., and Ali, L.A.M. (2021). Explainable medical image segmentation via generative adversarial networks and layer-wise relevance propagation. arXiv.","DOI":"10.5617\/nmi.9126"},{"key":"ref_68","unstructured":"Niu, Y., Gu, L., Zhao, Y., and Lu, F. (2021). Explainable Diabetic Retinopathy Detection and Retinal Image Generation. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Xiong, X., Sun, Y., Liu, X., Ke, W., Lam, C.-T., Chen, J., Jiang, M., Wang, M., Xie, H., and Tong, T. (2023). Distance Guided Generative Adversarial Network for Explainable Binary Classifications. arXiv.","DOI":"10.1016\/j.compmedimag.2024.102444"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Xiong, X., Sun, Y., Liu, X., Lam, C.-T., Tong, T., Chen, H., Gao, Q., Ke, W., and Tan, T. (2023). A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classifications. arXiv.","DOI":"10.1109\/ICASSP48485.2024.10448260"},{"key":"ref_71","unstructured":"Vigneshwaran, V., Ohara, E., Wilms, M., and Forkert, N.D. (2024). MACAW: A Causal Generative Model for Medical Imaging. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Awudong, B., Li, Q., Liang, Z., Tian, L., and Yan, J. (2024). Attentional adversarial training for few-shot medical image segmentation without annotations. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0298227"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Yu, X., Li, G., Lou, W., Liu, S., Wan, X., Chen, Y., and Li, H. (2023). Diffusion-based Data Augmentation for Nuclei Image Segmentation. arXiv.","DOI":"10.1007\/978-3-031-43993-3_57"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Akrout, M., Gyepesi, B., Holl\u00f3, P., Po\u00f3r, A., Kincs\u0151, B., Solis, S., Cirone, K., Kawahara, J., Slade, D., and Abid, L. (2023). Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images. arXiv.","DOI":"10.1007\/978-3-031-53767-7_10"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., and Fletcher, P.T. (2023). NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models. arXiv.","DOI":"10.1007\/978-3-031-43987-2_76"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Yellapragada, S., Graikos, A., Prasanna, P., Kurc, T., Saltz, J., and Samaras, D. (2023). PathLDM: Text conditioned Latent Diffusion Model for Histopathology. arXiv.","DOI":"10.1109\/WACV57701.2024.00510"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"100040","DOI":"10.1016\/j.ibmed.2021.100040","article-title":"Medical image editing in the latent space of Generative Adversarial Networks","volume":"5","author":"Rosado","year":"2021","journal-title":"Intell. Based Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.59275\/j.melba.2024-4862","article-title":"Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder","volume":"2","author":"Atad","year":"2024","journal-title":"J. Mach. Learn. Biomed. Imaging"},{"key":"ref_79","unstructured":"Favero, G.M., Saremi, P., Kaczmarek, E., Nichyporuk, B., and Arbel, T. (2025). Conditional diffusion models are medical image classifiers that provide explainability and uncertainty for free. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/s43856-025-00998-1","article-title":"A diffusion model for universal medical image enhancement","volume":"5","author":"Fei","year":"2025","journal-title":"Commun. Med."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Sch\u00f6n, J., Selvan, R., and Petersen, J. (2022). Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods. arXiv.","DOI":"10.1007\/978-3-031-18576-2_3"},{"key":"ref_82","first-page":"85","article-title":"Explainable Deep Learning Methods in Medical Image Classification: A Survey","volume":"56","author":"Neves","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Hettikankanamage, N., Shafiabady, N., Chatteur, F., Wu, R.M.X., Ud Din, F., and Zhou, J. (2025). eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing. Sensors, 25.","DOI":"10.3390\/s25216649"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"52","DOI":"10.53623\/amms.v1i1.692","article-title":"Explainable Artificial Intelligence (XAI) in Medical Imaging: Techniques, Applications, Challenges, and Future Directions","volume":"1","author":"Purwono","year":"2025","journal-title":"Adv. Mech. Mechatron. Syst."},{"key":"ref_85","unstructured":"Hou, J., Liu, S., Bie, Y., Wang, H., Tan, A., Luo, L., and Chen, H. (2024). Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"959","DOI":"10.3348\/kjr.2024.0392","article-title":"Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates","volume":"25","author":"Jung","year":"2024","journal-title":"Korean J. Radiol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Ihongbe, I.E., Fouad, S., Mahmoud, T.F., Rajasekaran, A., and Bhatia, B. (2024). Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0308758"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1016\/j.icte.2024.09.008","article-title":"Explainable AI (XAI) in image segmentation in medicine, industry, and beyond: A survey","volume":"10","author":"Tsai","year":"2024","journal-title":"ICT Express"},{"key":"ref_89","first-page":"40","article-title":"Explainable Artificial Intelligence-Based Diseases Diagnosis From Unstructured Clinical Data and Decision Making Using Blockchain Technologies","volume":"9","author":"Sumathi","year":"2025","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jiang, T., Li, R., Yuan, L., Grzegorzek, M., Li, C., and Li, X. (2025). A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1551894"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"12098","DOI":"10.1038\/s41598-023-39278-0","article-title":"A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis","volume":"13","author":"Niehues","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"McNaughton, J., Fernandez, J., Holdsworth, S., Chong, B., Shim, V., and Wang, A. (2023). Machine Learning for Medical Image Translation: A Systematic Review. Bioengineering, 10.","DOI":"10.3390\/bioengineering10091078"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Abdulqader, M.M., and Abdulazeez, A.M. (2025). A Comparative Study of Generative Adversarial Networks in Medical Image Processing. Eng, 6.","DOI":"10.3390\/eng6110291"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Akbar, M.U., Wang, W., and Eklund, A. (2024). Beware of diffusion models for synthesizing medical images\u2014A comparison with GANs in terms of memorizing brain MRI and chest X-ray images. arXiv.","DOI":"10.2139\/ssrn.4611613"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Metsch, J.M., and Hauschild, A.C. (2024). BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data. bioRxiv.","DOI":"10.1101\/2024.12.20.629677"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Bizon, N., and Appasani, B. (2026). Medical diagnosis system based on explainable artificial intelligence and blockchain. Explainable Artificial Intelligence for Trustworthy Decisions in Smart Applications, Springer.","DOI":"10.1007\/978-3-031-97007-8"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Bhatt, C.M., and Peddoju, S.K. (2017). Cloud Computing Systems and Applications in Healthcare, IGI Global.","DOI":"10.4018\/978-1-5225-1002-4"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Enescu, F.M., Nicolaescu, C.-G., Ionescu, V.M., Bizon, N., Marica, M.C., and R\u0103boac\u0103, M.-S. (2024). Blockchain in Personal Document Archiving Services. Proceedings of the 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, IEEE.","DOI":"10.1109\/ECAI61503.2024.10607454"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Enescu, F.M., Nicol\u0103escu, C.-G., Bizon, N., Ionescu, V.M., Marica, C.M., and Bo\u0219tinaru, R.-N. (2024). Architecture Model for the Verification and Identification of Inappropriate Products by the Consumer\u2014Supported by IoT, Blockchain and AI. Proceedings of the 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, IEEE.","DOI":"10.1109\/ECAI61503.2024.10607462"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Kundisch, A., H\u00f6nning, A., Mutze, S., Kreissl, L., Spohn, F., Lemcke, J., Sitz, M., Sparenberg, P., and Goelz, L. (2021). Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0260560"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Nicol\u0103escu, C.-G., Enescu, F.M., Bizon, N., \u0162ugulea, A.-M., and Ionescu, V.M. (2023). Intelligent System for Monitoring and Controlling the Energy Consumed. Proceedings of the 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Bucharest, Romania, IEEE.","DOI":"10.1109\/ECAI58194.2023.10194055"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3709716","article-title":"QURE: AI-Assisted and Automatically Verified UDF Inlining","volume":"3","author":"Siddiqui","year":"2025","journal-title":"Proc. ACM Manag. Data"},{"key":"ref_103","first-page":"181","article-title":"A Novel Deep Learning Method for Pneumonia Recognition Based on X-Ray Images","volume":"87","author":"Zhou","year":"2025","journal-title":"UPB Sci. Bull. Ser. C"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Enescu, M.-C., Nicol\u0103escu, C., and Stirbu, C. (2023). Intelligent System of Analysis of the Level of Gain of Development Hegemony for Great Powers. Proceedings of the 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Bucharest, Romania, IEEE.","DOI":"10.1109\/ECAI58194.2023.10194204"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:15:09Z","timestamp":1774358109000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":104,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["a19040244"],"URL":"https:\/\/doi.org\/10.3390\/a19040244","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]}}}