Generative adversarial networks and synthetic patient data: current challenges and future perspectives
Open accessAbstract
Artificial intelligence (AI) has been heralded as one of the key technological innovations of the 21st century. Within healthcare, much attention has been placed upon the ability of deductive AI systems to analyse large datasets to find patterns that would be unfeasible to program. Generative AI, including generative adversarial networks, are a newer type of machine learning that functions to create fake data after learning the properties of real data. Artificially generated patient data has the potential to revolutionise clinical research and protect patient privacy. Using novel techniques, it is increasingly possible to fully anonymise datasets to the point where no datapoint is traceable to any real individual. This can be used to expand and balance datasets as well as to replace the use of real patient data in certain contexts. This paper focuses upon three key uses of synthetic data: clinical research, data privacy and medical education. We also highlight ethical and practical concerns that require consideration.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Generative AI aims to address various medical conditions, from chronic diseases like diabetes to acute conditions like stroke.
“Generative AI aims to address various medical conditions, from chronic diseases like diabetes to acute conditions like stroke [6, 38, 60].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [6]
The Introduction mentions diabetes as a condition for which AI predictive models have been developed, but identifies that statement as text generated by GPT-3, not a finding about generative AI applications. The Clinical research and Conclusion sections discuss generative AI’s potential to improve healthcare broadly; they do not identify stroke or establish the claimed chronic-to-acute range. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGenerative AI models, such as GANs, can be used for tasks like synthesising medical images or generating patient data.
“Generative AI models, such as GANs, can be used for tasks like synthesising medical images or generating patient data [6, 37).”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Addressing bias in big data and AI for health care: A call for open science
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903 · see citing works
The Clinician and Dataset Shift in Artificial Intelligence
Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S., & Saria, S. (2021) · New England Journal of Medicine · doi:10.1056/nejmc2104626 · Cited by 855 · see citing works
Elreedy, D., & Atiya, A. F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.070 · Cited by 763 · see citing works
Synthetic data in machine learning for medicine and healthcare
Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00751-8 · Cited by 728 · see citing works
Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
Marcel, S., & Millán, J. D. R. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1012 · Cited by 538 · see citing works
AI-generated characters for supporting personalized learning and well-being
Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00417-9 · Cited by 358 · see citing works
Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00003-6 · Cited by 240 · cited 1× · see citing works
“10 Similarly, GANs may also be used in the construction of synthetic digital twins, whereby an artificial construction of a system is created that preserves all the patterns of the true system.”
BackgroundJeong, J., Tariq, A., Adejumo, T., Trivedi, H., Gichoya, J. W., & Banerjee, I. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00556-w · Cited by 167 · see citing works
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis
Wang, C., Yang, G., Papanastasiou, G., Tsaftaris, S. A., Newby, D. E., Gray, C. D., Macnaught, G., & MacGillivray, T. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.015 · Cited by 117 · see citing works
Generating Synthetic Data in Finance: Opportunities, Challenges and PitfallsPreprint
Assefa, S. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3634235 · Cited by 63 · see citing works
Xing, X., Ser, J. D., Wu, Y., Li, Y., Xia, J., Xu, L., Firmin, D. N., Gatehouse, P. D., & Yang, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3158897 · Cited by 36 · see citing works
Synthetic patient data in health care: a widening legal loophole
Arora, A., & Arora, A. (2022) · The Lancet · doi:10.1016/s0140-6736(22)00232-x · Cited by 35 · see citing works
Self-citationArtificial intelligence: a new frontier for anaesthesiology training
Arora, A. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.06.049 · Cited by 15 · see citing works
Self-citationWhat's lurking in your electrocardiogram?
Topol, E. J. (2021) · The Lancet · doi:10.1016/s0140-6736(21)00452-9 · Cited by 15 · see citing works
Disrupting clinical education: Using artificial intelligence to create training material
Arora, A. (2020) · The Clinical Teacher · doi:10.1111/tct.13177 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
Reddy, S. (2024) · Implementation Science · doi:10.1186/s13012-024-01357-9 · Cited by 554 · see citing works
“Generative AI aims to address various medical conditions, from chronic diseases like diabetes to acute conditions like stroke [6, 38, 60].”
BackgroundMethodZushin, P.-J. H., Mukherjee, S., & Wu, J. C. (2023) · Journal of Clinical Investigation · doi:10.1172/jci175824 · Cited by 347 · see citing works
The value of standards for health datasets in artificial intelligence-based applications
Arora, A., Alderman, J. E., Palmer, J., Ganapathi, S., Laws, E., McCradden, M. D., Oakden‐Rayner, L., Pfohl, S. R., Ghassemi, M., McKay, F., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02608-w · Cited by 312 · see citing works
Self-citationLa nueva realidad de la educación ante los avances de la inteligencia artificial generativa
García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023) · RIED-Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37716 · Cited by 238 · see citing works
Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization
Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023) · Information and Organization · doi:10.1016/j.infoandorg.2023.100478 · Cited by 173 · see citing works
Self-citationThe promise of large language models in health care
Arora, A., & Arora, A. (2023) · The Lancet · doi:10.1016/s0140-6736(23)00216-7 · Cited by 153 · see citing works
Self-citationGenerative artificial intelligence in medicine
Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03983-2 · Cited by 119 · see citing works
Artificial intelligence for visually impaired
Wang, J., Wang, S., & Zhang, Y. (2023) · Displays · doi:10.1016/j.displa.2023.102391 · Cited by 118 · see citing works
Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113 · see citing works
Paladugu, P., Ong, J., Nelson, N. G., Kamran, S. A., Waisberg, E., Zaman, N., Kumar, R., DIAS, R. D., Lee, A. G., & Tavakkoli, A. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03304-z · Cited by 98 · see citing works
Multi-omics approaches to decipher the interactions of nanoparticles and biological systems
Wang, Y., Xiao, Z., Wang, Z., Lee, D., Ma, Y., Wilhelm, S., Wang, H., Kim, B. Y. S., & Jiang, W. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00264-4 · Cited by 74 · see citing works
A systematic literature review on the effectiveness of deepfake detection techniques
Stroebel, L., Llewellyn, M., Hartley, T., Ip, T. S., & Ahmed, M. (2023) · Journal of Cyber Security Technology · doi:10.1080/23742917.2023.2192888 · Cited by 66 · see citing works
A scoping review on generative AI and large language models in mitigating medication related harm
Ong, J. C. L., Chen, M. H., Ng, N., Elangovan, K., Tan, N. Y. T., Jin, L., Xie, Q., Ting, D. S. W., Rodriguez-Monguio, R., Bates, D. W., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01565-7 · Cited by 64 · see citing works
Alajaji, S. A., Khoury, Z. H., Elgharib, M. A., Saeed, M. M. A., Ahmed, A. R. H., Khan, M. B., Tavares, T., Jessri, M., Puché, A. C., Hoorfar, H., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100369 · Cited by 66 · see citing works
Medical Ethics of Large Language Models in Medicine
Ong, J. C. L., Chang, Y., Wasswa, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi‐Velez, F., Lu, W., Savulescu, J., et al. (2024) · NEJM AI · doi:10.1056/aira2400038 · Cited by 59 · see citing works
Lim, B., Seth, I., Kah, S., Sofiadellis, F., Ross, R. J., Rozen, W. M., & Cuomo, R. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12206524 · Cited by 53 · see citing works
Baig, M. M., Hobson, C., GholamHosseini, H., Ullah, E., & Afifi, S. M. (2024) · Applied Sciences · doi:10.3390/app142310899 · Cited by 60 · see citing works
Waldock, W. J., Zhang, J., Guni, A., Nabeel, A., Darzi, A. W., & Ashrafian, H. (2024) · Journal of Medical Internet Research · doi:10.2196/56532 · Cited by 51 · see citing works
Generative AI in Undergraduate Medical Education: A Rapid Review
Hale, J., Alexander, S., Wright, S. T., & Gilliland, K. (2024) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205241266697 · Cited by 46 · see citing works
The Rise of Generative Artificial Intelligence in Healthcare
Kuzlu, M., Xiao, Z., Sarp, S., Çatak, F. Ö., Gurler, N., & Güler, Ö. (2023) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco58584.2023.10155107 · Cited by 45 · see citing works
Generative artificial intelligence: synthetic datasets in dentistry
Umer, F., & Adnan, N. (2024) · BDJ Open · doi:10.1038/s41405-024-00198-4 · Cited by 43 · see citing works
How can quantum computing be applied in clinical trial design and optimization?
Doğa, H., Bose, A., Sahin, M. E., Bettencourt‐Silva, J. H., Pham, A., Kim, E., Andress, A., Saxena, S., Parida, L., Robertus, J. L., et al. (2024) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2024.08.005 · Cited by 41 · see citing works
The AI revolution in glaucoma: Bridging challenges with opportunities
Li, F., Wang, D., Yang, Z., Zhang, Y., Jiang, J., Liu, X., Kong, K., Zhou, F., Tham, C. C. Y., Medeiros, F. A., et al. (2024) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2024.101291 · Cited by 40 · see citing works
Nedbal, C., Naik, N., Castellani, D., Gauhar, V., Geraghty, R. M., & Somani, B. K. (2023) · Current Opinion in Urology · doi:10.1097/mou.0000000000001151 · Cited by 38 · see citing works
The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine
Katwaroo, A. R., Adesh, V. S., Lowtan, A., & Umakanthan, S. (2023) · Postgraduate Medical Journal · doi:10.1093/postmj/qgad135 · Cited by 40 · see citing works
Villegas-Ch, W. E., Govea, J., & Revelo-Tapia, S. (2023) · Sustainability · doi:10.3390/su151914512 · Cited by 35 · see citing works
Ethical Application of Generative Artificial Intelligence in Medicine
Hasan, S. S., Fury, M. S., Woo, J. J., Kunze, K. N., & Ramkumar, P. N. (2024) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2024.12.011 · Cited by 40 · see citing works
Sosna, J., Joskowicz, L., & Saban, M. (2025) · Radiology · doi:10.1148/radiol.240982 · Cited by 31 · see citing works
Kang, H. Y. J., Batbaatar, E., Choi, D., Choi, K. S., Ko, M., & Ryu, K. S. (2023) · JMIR Medical Informatics · doi:10.2196/47859 · Cited by 31 · see citing works
Ng, C. K. C. (2023) · Children · doi:10.3390/children10081372 · Cited by 29 · see citing works
Unleashing Creativity in the Metaverse: Generative AI and Multimodal Content
Saddik, A. E., Ahmad, J., Khan, M., Abouzahir, S., & Gueaieb, W. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3713075 · Cited by 32 · see citing works
Generative Adversarial Networks: Security, Privacy, and Ethical Considerations
Shafik, W. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76710-4_5 · Cited by 28 · see citing works
Cascella, M., Scarpati, G., Bignami, E. G., Cuomo, A., Vittori, A., Gennaro, P. D., Crispo, A., & Coluccia, S. (2023) · Journal of Anesthesia Analgesia and Critical Care · doi:10.1186/s44158-023-00104-8 · Cited by 27 · see citing works
Generative AI Models in Time-Varying Biomedical Data: Scoping Review
He, R., Sarwal, V., Qiu, X., Zhuang, Y., Zhang, L., Liu, Y., & Chiang, J. N. (2024) · Journal of Medical Internet Research · doi:10.2196/59792 · Cited by 32 · see citing works
Trotsyuk, A. A., Waeiss, Q., Bhatia, R. T., Aponte, B. J., Heffernan, I. M. L., Madgavkar, D., Felder, R. M., Lehmann, L. S., Palmer, M. J., Greely, H. T., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00926-3 · Cited by 27 · see citing works
Embracing Generative Artificial Intelligence in Clinical Research and Beyond
Foote, H. P., Hong, C., Anwar, M. M., Borentain, M., Bugin, K., Dreyer, N. A., Fessel, J. P., Goyal, N., Hanger, M., Hernandez, A. F., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101593 · Cited by 30 · see citing works
Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey
Tung, T., Hasnaeen, S. M. N., & Zhao, X. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1692517 · Cited by 31 · see citing works
A Justifiable Investment in AI for Healthcare: Aligning Ambition with Reality
Karpathakis, K., Morley, J., & Floridi, L. (2024) · Minds and Machines · doi:10.1007/s11023-024-09692-y · Cited by 24 · see citing works
Wei, S., Sasi, C., Piepenbrock, J., Huynen, M. A., & Hoen, P. A. C. ’. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110873 · Cited by 22 · see citing works
Schoenhof, R., Schoenhof, R., Blumenstock, G., Lethaus, B., & Hoefert, S. (2024) · Journal of Dentistry · doi:10.1016/j.jdent.2024.105042 · Cited by 21 · see citing works
Stephanian, B., Karki, S., Debnath, K., Saltychev, M., Rossi-Meyer, M. K., Kandathil, C. K., & Most, S. P. (2024) · Facial Plastic Surgery & Aesthetic Medicine · doi:10.1089/fpsam.2024.0204 · Cited by 23 · see citing works
The perils and promises of generative artificial intelligence in neurointerventional surgery
Ray, T. R., Kellogg, R. T., Fargen, K. M., Hui, F. K., & Vargas, J. (2023) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2023-020353 · Cited by 21 · see citing works
The visual communication using generative artificial intelligence in the context of new media
Liu, W., & Kim, H.-G. (2025) · Scientific Reports · doi:10.1038/s41598-025-96869-9 · Cited by 20 · see citing works
Expanding horizons and navigating challenges for enhanced clinical workflows: ChatGPT in urology
Talyshinskii, A., Naik, N., Hameed, B. M. Z., Zhanbyrbekuly, U., Khairli, G., Гулиев, Б. Г., Juilebø-Jones, P., Tzelves, L. I., & Somani, B. K. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2023.1257191 · Cited by 20 · see citing works
Which curriculum components do medical students find most helpful for evaluating AI outputs?
Waldock, W. J., Lam, G., Baptista, A., Walls, R., & Sam, A. H. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06735-5 · Cited by 22 · see citing works
Can generative artificial intelligence pass the orthopaedic board examination?
Isleem, U. N., Zaidat, B., Ren, R., Geng, E. A., Burapachaisri, A., Tang, J. E., Kim, J. S., & Cho, S. K. (2024) · Journal of Orthopaedics · doi:10.1016/j.jor.2023.10.026 · Cited by 19 · see citing works
Artificial Intelligence in Planning for Spine Surgery
Ali, I. S., Bakaes, Y., MacLeod, J., Lee, T. Y., Cho, S., & Hsu, W. K. (2025) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-025-09992-5 · Cited by 18 · see citing works
Arora, A., & Arora, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283094 · Cited by 18 · see citing works
Self-citationIn Vitro Skin Models as Non-Animal Methods for Dermal Drug Development and Safety Assessment
Gagosian, V. S. C., Coronel, R., Buss, B. C., dos Santos, M. L. F., Liste, I., Anta, B., & Foti, L. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17101342 · Cited by 17 · see citing works
Ethics of Artificial Intelligence in Academia
Khan, Z. R. (2024) · Springer International Handbooks of Education · doi:10.1007/978-3-031-54144-5_188 · Cited by 19 · see citing works
Shahin, M. H., Desai, P. V., Terranova, N., Guan, Y., Helikar, T., Lobentanzer, S., Liu, Q., Lu, J. J.-Q., Madhavan, S., Mo, G., et al. (2025) · Clinical and Translational Science · doi:10.1111/cts.70203 · Cited by 15 · see citing works
Das, U. (2025) · ChemistrySelect · doi:10.1002/slct.202502448 · Cited by 15 · see citing works
Kannan, M., Umamaheswari, D., Manimekala, B., Mary, P. S., Savitha, P. M., & Rozario, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-15019-3 · Cited by 14 · see citing works
Burns, B., Nemelka, B., & Arora, A. (2024) · Future Healthcare Journal · doi:10.1016/j.fhj.2024.100166 · Cited by 14 · see citing works
Self-citationJung, H., Kim, Y., Seo, J., Choi, H., Kim, M., Han, J., Kee, G., Ko, S., Kim, B., Choi, B., et al. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00203-x · Cited by 13 · see citing works
Bolpagni, M., Pardini, S., & Gabrielli, S. (2024) · Internet Interventions · doi:10.1016/j.invent.2024.100775 · Cited by 13 · see citing works
Paul, A., & Mallidi, S. (2024) · Photoacoustics · doi:10.1016/j.pacs.2024.100674 · Cited by 11 · see citing works
MF-CGAN: Multifeature Conditional GAN for Synthetic Data Generation in Internet of Medical Things
Pandey, C., Tiwari, V., Francis, S. A. J., Pal, V., & Roy, D. S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3554207 · Cited by 11 · see citing works
Arora, A., Bojko, L., Kumar, S., Lillington, J., Panesar, S. S., & Petrungaro, B. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105164 · Cited by 10 · see citing works
Self-citationAI-Enabled Health Systems: Transforming Personalized Medicine And Wellness
Pulimamidi, G. P. B. D. R. (2023) · Journal of Propulsion Technology · doi:10.52783/tjjpt.v44.i3.2467 · Cited by 10 · see citing works
Synthetic data: the future of open-access health-care datasets?
Arora, A. (2023) · The Lancet · doi:10.1016/s0140-6736(23)00324-0 · Cited by 9 · see citing works
Self-citationUncover This Tech Term: Generative Adversarial Networks
Ahmed, H. S. (2024) · Korean Journal of Radiology · doi:10.3348/kjr.2023.1306 · Cited by 8 · see citing works
Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence
Masalkhi, M., Sporn, K., Kumar, R., Ong, J., Nguyen, T. A., Waisberg, E., Zaman, N., Lee, A. G., & Tavakkoli, A. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01519-1 · Cited by 10 · see citing works
The Role of Generative AI in Empowering Generation Z in Higher Education
Zábojník, R., & Hromada, V. (2024) · Media & Marketing Identity · doi:10.34135/mmidentity-2024-75 · Cited by 11 · see citing works
Mobile medical systems for equitable healthcare
Chan, J., Goel, M., Gollakota, S., & Nandakumar, R. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00330-5 · Cited by 7 · see citing works
Nirojini, P. S., Kanaga, K., Devika, S. V., & Pradeep, P. (2024) · Scholars Academic Journal of Pharmacy · doi:10.36347/sajp.2024.v13i02.003 · Cited by 8 · see citing works
Application of text-to-image translation algorithms in medicine: A systematic review
Mai, M., Mirza, F. N., & DiMarco, C. (2024) · JAAD reviews. · doi:10.1016/j.jdrv.2024.09.003 · Cited by 7 · see citing works
Helen, D., & Suresh, N. V. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3731-8.ch004 · Cited by 7 · see citing works
Fieldhouse, J. K., Ge, J., Randhawa, N., Wolking, D. J., Genovese, B. N., Mazet, J. A. K., & Desai, A. N. (2025) · Public Health · doi:10.1016/j.puhe.2025.105961 · Cited by 7 · see citing works
Emerging Technologies for Exploring the Cellular Mechanisms in Vascular Diseases
Sahu, D., Ganguly, T., Mann, A., Gupta, Y., Nynatten, L. R. V., & Fraser, D. D. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms27010164 · Cited by 6 · see citing works
McDonnell, K. J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12144830 · Cited by 7 · see citing works
Ethical Challenges of Using Synthetic Data
Chauhan, P., Bongo, L. A., & Pedersen, E. (2023) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v1i1.27490 · Cited by 6 · see citing works
Can LLMs improve existing scenario of healthcare?
Ray, P. P. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.08.006 · Cited by 6 · see citing works
Artificial Intelligence in Plastic Surgery: Current Status, Limitations, and Future Directions
Copeland‐Halperin, L. R. (2025) · Aesthetic Surgery Journal · doi:10.1093/asj/sjaf239 · Cited by 5 · see citing works
Application of Generative AI in Patient Engagement
ZareiNejad, M., & Tavana, P. (2025) · Springer eBooks · doi:10.1007/978-3-031-82963-5_5 · Cited by 4 · see citing works
Li, F., Udoidiok, I., & Zhang, J. (2025) · IEEE Access · doi:10.1109/access.2025.3564985 · Cited by 5 · see citing works
Webster‐Clark, M., Filion, K. B., & Platt, R. W. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae015 · Cited by 5 · see citing works
Generative artificial intelligence in healthcare: current status and future directions
Ouanes, K. (2024) · Italian Journal of Medicine · doi:10.4081/itjm.2024.1782 · Cited by 5 · see citing works
Liu, S., Kandakji, L., Stupnicki, A., Sumodhee, D., Leucci, M. T., Hau, S., Balal, S., Okonkwo, A. C. O., Moghul, I., Kanda, S. P., et al. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.6.12 · Cited by 5 · see citing works
Yang, C., Sheng, H., Kolbinson, K. T., Shaterian, H., Ashe, P. C., Gao, P., Zhang, W., Quilichini, T. D., & Xiang, D. (2025) · Plant Methods · doi:10.1186/s13007-025-01451-z · Cited by 4 · see citing works
Rahman, M. I., Hossain, M. R., Sayem, S. M., & Emdad, F. B. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100342 · Cited by 6 · see citing works
Frid, S. A., Bassegoda, O., Mahamud, M. A. C., Sanjuan, G., de la Hoz, M. Á. A., Celi, L. A., Cano, I., Anmella, G., Cuñat, T., Arellano, A. L., et al. (2025) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2025-101640 · Cited by 4 · see citing works
Development of Fully Synthetic Medical Database Shuffling Method
Nasimov, R., Nasimova, N., Muminov, B. B., Usmanxodjayeva, A., Sobirova, G., & Abdusalomov, A. B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60997-8_6 · Cited by 4 · see citing works
Paladugu, P., Kumar, R., Yelamanchi, J., Waisberg, E., Ong, J., Masalkhi, M., Gowda, C., Lee, R. S., Amiri, D., Jagadeesan, R., et al. (2025) · Neuroinformatics · doi:10.1007/s12021-025-09729-2 · Cited by 3 · see citing works
Combating Antimicrobial Resistance: Role of Key Stakeholders with Focus on the Pharmaceutical Sector
Sharma, S., Satyaseela, M. P., Burri, R., Palakodeti, R., & Pamarthy, D. (2025) · Pharmaceutical Medicine · doi:10.1007/s40290-025-00572-z · Cited by 3 · see citing works
Ung, C. Y., Correia, C., Zhang, Z. M., Caya, C., Zhu, S., Billadeau, D. D., & Li, H. (2025) · Cancers · doi:10.3390/cancers17213543 · Cited by 3 · see citing works
LLM-Powered Synthetic Data Modeling for Self-Emulsifying Drug Delivery Systems
Niloy, S. I., Khan, M. R. K., Mostafa, N., Ashour, E. A., & Khalil, K. (2025) · International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) · doi:10.1109/aibthings66987.2025.11296223 · Cited by 3 · see citing works
Nyamawe, A. S., & Shao, D. D. (2026) · Digital Health · doi:10.1177/20552076261418897 · Cited by 4 · see citing works
Computational Intelligence Methods for Biomarkers Discovery in Autoimmune Diseases: Case Studies
Bano, N., Sajid, I., Faizi, S. A. A., Mutshembele, A., Barh, D., & Raza, K. (2024) · Studies in computational intelligence · doi:10.1007/978-981-99-9029-0_15 · Cited by 3 · see citing works
POTENTIAL CONTRIBUTION OF ChatGPT® TO LEARNING ABOUT SEPTIC SHOCK IN INTENSIVE CARE
Sasso, G. T. M. D., Lanzoni, G. M. D. M., Alvarez, A. G., Barra, D. C. C., & Barbosa, S. D. F. F. (2024) · Texto & Contexto - Enfermagem · doi:10.1590/1980-265x-tce-2023-0184en · Cited by 3 · see citing works
Generative AI Applications for Enhancing Medical Training
Sial, Q., Shah, I. A., & Jhanjhi, N. Z. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-6577-9.ch009 · Cited by 3 · see citing works
Ethics of Artificial Intelligence in Academia
Khan, Z. R. (2023) · Springer eBooks · doi:10.1007/978-981-287-079-7_188-1 · Cited by 5 · see citing works
Harnessing generative AI: Transformative applications in medical imaging and beyond
Goyal, S., & Kaushal, L. (2024) · Future Health · doi:10.25259/fh_12_2024 · Cited by 3 · see citing works
Sasso, G. T. M. D., Lanzoni, G. M. D. M., Alvarez, A. G., Barra, D. C. C., & Barbosa, S. D. F. F. (2024) · Texto & Contexto - Enfermagem · doi:10.1590/1980-265x-tce-2023-0184pt · Cited by 3 · see citing works
Kulkarni, B. C., Sai, B. S., Kolagad, V., Patil, N., & Bhat, P. (2025) · SN Computer Science · doi:10.1007/s42979-025-03709-8 · Cited by 3 · see citing works
Generative AI for Simulating Rare Disease Scenarios in Training Robots
Bhattad, A., Gulhane, M., Gavhale, K. R., Revatkar, A., Patil, D., & Singh, U. P. (2024) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3745812.3745858 · Cited by 2 · see citing works
Zhao, Z. (2025) · Intelligent Decision Technologies · doi:10.1177/18724981251394990 · Cited by 2 · see citing works
Duwe, G., Moench, K., Kauth, V., Angeloni, M., Eckhoff, J., Görtz, M., Hoefert, S., Kocar, T. D., Kollitsch, L., Mehralivand, S., et al. (2026) · Die Urologie · doi:10.1007/s00120-026-02778-8 · Cited by 2 · see citing works
Mohammad‐Rahimi, H., Jain, S., Naveed, K., Hosseinpour, S., Kirkevang, L., Nosrat, A., & Pauwels, R. (2026) · International Endodontic Journal · doi:10.1111/iej.70146 · Cited by 2 · see citing works
- Authors
- Arora, A., & Arora, A.
- Affiliations
- University of Cambridge (GB)
- Venue
- Future Healthcare Journal
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 9
- Issue
- 2
- Pages
- 190-193
- ISSN
- 2514-6645 (ISSN-L)
- PMC
- PMC9345230
- OpenAlex
- W4288455572
- Semantic Scholar
- 5c56849181d2871dedb95f3bda443dc11d29a399
- Cited by
- 161
- References
- 18
- Citations by year
- 2026: 20 · 2025: 58 · 2024: 51 · 2023: 31 · 2022: 1
- FWCI
- 10.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Healthcare Journal (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- generative adversarial networks, synthetic patient data, synthetic data generation, clinical research, data privacy, data anonymization, data balancing, medical education, ethical considerations, artificial intelligence in healthcare
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
