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Review
. 2025 Apr 23;12(5):440.
doi: 10.3390/bioengineering12050440.

Large Language Models in Genomics-A Perspective on Personalized Medicine

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 , Teng Zhou  1
Affiliations
Review

Large Language Models in Genomics-A Perspective on Personalized Medicine

Shahid Ali et al. Bioengineering (Basel). .

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: artificial intelligence (AI); genomic data; large language models (LLMs); precision medicine.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Foundation models. FMs are highly versatile and can be fine-tuned to adapt to perform specialized tasks (translation, object recognition, sentiment analysis, etc.).
Figure 2
Figure 2
The relationship between AI, ML, DL, and LLM, illustrating how LLMs merge NLP capabilities with the advanced learning and cognitive functions provided by AI.
Figure 3
Figure 3
The architecture of a transformer introduced in [10].
Figure 4
Figure 4
The process to train an LLM for biological applications.
See this image and copyright information in PMC

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