https://doi.org/10.4081/itjm.2024.1782
Generative artificial intelligence in healthcare: current status and future directions
Published: 28 August 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Generative artificial intelligence (GAI) is rapidly transforming the healthcare landscape, offering innovative solutions in areas such as medical imaging, drug discovery, and clinical decision support. This comprehensive review examines the current role of GAI in healthcare, its potential benefits, drawbacks, challenges, and future research directions. By synthesizing recent literature and expert perspectives, this review provides a critical analysis of GAI’s impact on healthcare delivery, patient outcomes, and ethical considerations. While GAI shows promise in enhancing diagnostic accuracy, accelerating drug development, and improving healthcare efficiency, it also faces significant challenges related to data privacy, regulatory compliance, and ethical implementation. This review aims to inform healthcare professionals, researchers, and policymakers about the current state and future potential of GAI in healthcare, emphasizing the need for responsible development and deployment of these technologies.
Kuzlu M, Xiao Z, Sarp S, et al. The rise of generative artificial intelligence in healthcare. 12th Mediterranean Conference on Embedded Computing (MECO). 2023;1-4.
DOI: https://doi.org/10.1109/MECO58584.2023.10155107
Jadon A, Kumar S. Leveraging generative AI models for synthetic data generation in healthcare: balancing research and privacy. 2023 International Conference on Smart Applications, Communications and Networking 2023;arXiv:2305.05247.
DOI: https://doi.org/10.1109/SmartNets58706.2023.10215825
Shokrollahi Y, Yarmohammadtoosky S, Nikahd MM, et al. A comprehensive review of generative AI in healthcare. 2023 Available from: https://arxiv.org/abs/2310.00795.
Arora A, Arora A. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc J 2022;9:190-3.
DOI: https://doi.org/10.7861/fhj.2022-0013
Burlina P, Joshi N, Paul W, et al. Addressing artificial intelligence bias in retinal disease diagnostics. Transl Vis Sci Technol 2021;10:13.
DOI: https://doi.org/10.1167/tvst.10.2.13
Schubert M, Lasotta M, Sahm F, et al. Evaluating the multimodal capabilities of generative ai in complex clinical diagnostics. medRxiv Available from: https://www.medrxiv.org/content/10.1101/2023.11.01.23297938v1
Mansouri H, Barigou F, Nahili A, et al. Enhancing medical image fusion and diagnostic accuracy using vision transformers: a novel approach leveraging generative adversarial networks. All Sciences Abstracts 2023;1:8.
DOI: https://doi.org/10.59287/as-abstracts.929
Hirosawa T, Harada Y, Yokose M, et al. Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 chatbot for clinical vignettes with common chief complaints: a pilot study. Int J Environ Res Public Health 2023;20:3378.
DOI: https://doi.org/10.3390/ijerph20043378
Sharma B, Gao Y, Miller T, et al. Multi-task training with in-domain language models for diagnostic reasoning. Proceedings of the 5th Clinical Natural Language Processing Workshop. Toronto, Canada: Association for Computational Linguistics; 2023. pp 78-85.
DOI: https://doi.org/10.18653/v1/2023.clinicalnlp-1.10
Ding Y, Ma L, Ma J, et al. A generative adversarial network-based intelligent fault diagnosis method for rotating machinery under small sample size conditions. IEEE Access. 2019;7:149736-49.
DOI: https://doi.org/10.1109/ACCESS.2019.2947194
Musalamadugu T, Kannan H. Generative AI for medical imaging analysis and applications. Future Med AI 2023;1:FMAI5.
DOI: https://doi.org/10.2217/fmai-2023-0004
Chen H, Engkvist O. Drug design augmented by artificial intelligence become a reality?. Trends Pharmacol Sci 2019;40:592-604.
DOI: https://doi.org/10.1016/j.tips.2019.09.004
Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 2019;37:1038-40.
DOI: https://doi.org/10.1038/s41587-019-0224-x
Tong X, Liu X, Tan X, et al. Models for de novo drug design. J Med Chem 2021;64:6305-21.
DOI: https://doi.org/10.1021/acs.jmedchem.1c00927
Lin E, Lin CH, Lane H. Relevant applications of generative adversarial networks in drug design and discovery: Molecular de novo design, dimensionality reduction, and de novo peptide and protein design. Molecules 2020;25:3250.
DOI: https://doi.org/10.3390/molecules25143250
Zhavoronkov A, Aspuru-Guzik A. Reply to ‘Assessing the impact of generative AI on medicinal chemistry’. Nat Biotechnol 2020;38:146.
DOI: https://doi.org/10.1038/s41587-020-0417-3
Kadurin A, Nikolenko S, Khrabrov K, et al. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol Pharm 2017;14:3098-104.
DOI: https://doi.org/10.1021/acs.molpharmaceut.7b00346
Walters W, Murcko M. Assessing the impact of generative AI on medicinal chemistry. Nat Biotechnol 2020;38:143-5.
DOI: https://doi.org/10.1038/s41587-020-0418-2
Zhang P, Kamel Boulos MN. Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet 2023;15:286.
DOI: https://doi.org/10.3390/fi15090286
Yu P, Xu H, Hu X, Deng C. Leveraging generative AI and large language models: a comprehensive roadmap for healthcare integration. Healthcare 2023;11:2776.
DOI: https://doi.org/10.3390/healthcare11202776
Oniani D, Hilsman J, Peng Y, et al. From military to healthcare: adopting and expanding ethical principles for generative artificial intelligence. Available from: https://arxiv.org/abs/2308.02448.
Shah C. Generative AI and the future of information access. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, October 21-25, Birmingham, United Kingdom. doi:10.1145/3583780.3615317.
DOI: https://doi.org/10.1145/3583780.3615317
How to Cite
Ouanes, K. (2024). Generative artificial intelligence in healthcare: current status and future directions. Italian Journal of Medicine, 18(3). https://doi.org/10.4081/itjm.2024.1782
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