Generative AI and large language models: A new frontier in reverse vaccinology
Document Type
Article
Source of Publication
Informatics in Medicine Unlocked
Publication Date
1-1-2024
Abstract
Reverse vaccinology is an emerging concept in the field of vaccine development as it facilitates the identification of potential vaccine candidates. Biomedical research has been revolutionized with the recent innovations in Generative Artificial Intelligence (AI) and Large Language Models (LLMs). The intersection of these two technologies is explored in this study. In this study, the impact of Generative AI and LLMs in the field of vaccinology is explored. Through a comprehensive analysis of existing research, prospective use cases, and an experimental case study, this research highlights that LLMs and Generative AI have the potential to enhance the efficiency and accuracy of vaccine candidate identification. This work also discusses the ethical and privacy challenges, such as data consent and potential biases, raised by such applications that require careful consideration. This study paves the way for experts, researchers, and policymakers to further investigate the role and impact of Generative AI and LLM in vaccinology and medicine.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
48
Disciplines
Computer Sciences
Keywords
AI, AI ethics, Generative AI, Large language models (LLMs), Reverse vaccinology, Vaccine candidate identification, Vaccines
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Hayawi, Kadhim; Shahriar, Sakib; Alashwal, Hany; and Serhani, Mohamed Adel, "Generative AI and large language models: A new frontier in reverse vaccinology" (2024). All Works. 6639.
https://zuscholars.zu.ac.ae/works/6639
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series