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Cited by 268Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsArtificial Intelligence in Healthcare

Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations

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Sai, Siva, Gaur, Aanchal, Sai, Revant, Chamola, Vinay, Guizani, Mohsen, Rodrigues, Joel J. P. C.·2024·IEEE Access
DOI 10.1109/access.2024.3367715
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Abstract

Generative artificial intelligence (GAI) can be broadly described as an artificial intelligence system capable of generating images, text, and other media types with human prompts. GAI models like ChatGPT, DALL-E, and Bard have recently caught the attention of industry and academia equally. GAI applications span various industries like art, gaming, fashion, and healthcare. In healthcare, GAI shows promise in medical research, diagnosis, treatment, and patient care and is already making strides in real-world deployments. There has yet to be any detailed study concerning the applications and scope of GAI in healthcare. Addressing this research gap, we explore several applications, real-world scenarios, and limitations of GAI in healthcare. We examine how GAI models like ChatGPT and DALL-E can be leveraged to aid in the applications of medical imaging, drug discovery, personalized patient treatment, medical simulation and training, clinical trial optimization, mental health support, healthcare operations and research, medical chatbots, human movement simulation, and a few more applications. Along with applications, we cover four real-world healthcare scenarios that employ GAI: visual snow syndrome diagnosis, molecular drug optimization, medical education, and dentistry. We also provide an elaborate discussion on seven healthcare-customized LLMs like Med-PaLM, BioGPT, DeepHealth, etc.,Since GAI is still evolving, it poses challenges like the lack of professional expertise in decision making, risk of patient data privacy, issues in integrating with existing healthcare systems, and the problem of data bias which are elaborated on in this work along with several other challenges. We also put forward multiple directions for future research in GAI for healthcare.

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Cited by

Sorted by citations

Authors
Sai, S., Gaur, A., Sai, R., Chamola, V., Guizani, M., & Rodrigues, J. J. P. C.
Affiliations
  • Birla Institute of Technology and Science, Pilani (IN)
  • Department of Electrical and Communication Engineering, Maharaja Agrasen Institute of Technology, Delhi, New Delhi, India
  • Mohamed bin Zayed University of Artificial Intelligence (AE)
  • University of Lisbon (PT)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024
Volume
12
Pages
31078-31106
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2024.3367715
OpenAlex
W4391974599
Cited by
268
References
9
Citations by year
2026: 82 · 2025: 138 · 2024: 48
FWCI
22.79
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Fund provided by the Mohamed Bin Zayed University of Artificial Intelligence (8481000021)
  • Chanakya Fellowship Program of the Technology Innovation Hub (TIH) Foundation for Internet of Things (IoT) Internet of Everything (CFP/2022/027)
  • Brazilian National Council for Scientific and Technological Development—CNPq via (306607/2023-9)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
generative AI, medical applications, large language models, ChatGPT, DALL-E, diagnostic imaging, drug discovery, personalized treatment, clinical trial optimization, mental health support, healthcare chatbots, medical education, BioGPT, patient data privacy, data bias, healthcare system integration

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