دورية أكاديمية

Generative AI and large language models: A new frontier in reverse vaccinology

التفاصيل البيبلوغرافية
العنوان: Generative AI and large language models: A new frontier in reverse vaccinology
المؤلفون: Kadhim Hayawi, Sakib Shahriar, Hany Alashwal, Mohamed Adel Serhani
المصدر: Informatics in Medicine Unlocked, Vol 48, Iss , Pp 101533- (2024)
بيانات النشر: Elsevier, 2024.
سنة النشر: 2024
المجموعة: LCC:Computer applications to medicine. Medical informatics
مصطلحات موضوعية: Reverse vaccinology, Large language models (LLMs), AI, Generative AI, Vaccine candidate identification, AI ethics, Computer applications to medicine. Medical informatics, R858-859.7
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2352-9148
العلاقة: http://www.sciencedirect.com/science/article/pii/S2352914824000893Test; https://doaj.org/toc/2352-9148Test
DOI: 10.1016/j.imu.2024.101533
الوصول الحر: https://doaj.org/article/660c1456a28b43a9869572e75ae9dd6dTest
رقم الانضمام: edsdoj.660c1456a28b43a9869572e75ae9dd6d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:23529148
DOI:10.1016/j.imu.2024.101533