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Bioengineering
Volume 12
Issue 5
10.3390/bioengineering12050440
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Review

Large Language Models in Genomics—A Perspective on Personalized Medicine

by
Shahid Ali
1,†,
Yazdan Ahmad Qadri
2,†,
Khurshid Ahmad
3,
Zhizhe Lin
1,
Man-Fai Leung
4,
Sung Won Kim
2,
Athanasios V. Vasilakos
5 and
Teng Zhou
1,*
1
School of Cyberspace Security, Hainan University, Haikou 570228, China
2
School of Computer Science and Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si 38541, Gyeongsangbuk-do, Republic of Korea
3
Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
4
School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK
5
Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Bioengineering 2025, 12(5), 440; https://doi.org/10.3390/bioengineering12050440
Submission received: 1 April 2025 / Revised: 21 April 2025 / Accepted: 22 April 2025 / Published: 23 April 2025
(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)
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Abstract

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs’ significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual’s genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs’ potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients.
Keywords: large language models (LLMs); artificial intelligence (AI); genomic data; precision medicine large language models (LLMs); artificial intelligence (AI); genomic data; precision medicine

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MDPI and ACS Style

Ali, S.; Qadri, Y.A.; Ahmad, K.; Lin, Z.; Leung, M.-F.; Kim, S.W.; Vasilakos, A.V.; Zhou, T. Large Language Models in Genomics—A Perspective on Personalized Medicine. Bioengineering 2025, 12, 440. https://doi.org/10.3390/bioengineering12050440

AMA Style

Ali S, Qadri YA, Ahmad K, Lin Z, Leung M-F, Kim SW, Vasilakos AV, Zhou T. Large Language Models in Genomics—A Perspective on Personalized Medicine. Bioengineering. 2025; 12(5):440. https://doi.org/10.3390/bioengineering12050440

Chicago/Turabian Style

Ali, Shahid, Yazdan Ahmad Qadri, Khurshid Ahmad, Zhizhe Lin, Man-Fai Leung, Sung Won Kim, Athanasios V. Vasilakos, and Teng Zhou. 2025. "Large Language Models in Genomics—A Perspective on Personalized Medicine" Bioengineering 12, no. 5: 440. https://doi.org/10.3390/bioengineering12050440

APA Style

Ali, S., Qadri, Y. A., Ahmad, K., Lin, Z., Leung, M.-F., Kim, S. W., Vasilakos, A. V., & Zhou, T. (2025). Large Language Models in Genomics—A Perspective on Personalized Medicine. Bioengineering, 12(5), 440. https://doi.org/10.3390/bioengineering12050440

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