Large Language Models in Genomics—A Perspective on Personalized Medicine

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Publicado en:Bioengineering vol. 12, no. 5 (2025), p. 440
Autor principal: Ali, Shahid
Otros Autores: Qadri Yazdan Ahmad, Khurshid, Ahmad, Lin Zhizhe, Man-Fai, Leung, Kim, Sung Won, Vasilakos, Athanasios V, Zhou, Teng
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MDPI AG
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024 7 |a 10.3390/bioengineering12050440  |2 doi 
035 |a 3211859438 
045 2 |b d20250101  |b d20251231 
100 1 |a Ali, Shahid  |u School of Cyberspace Security, Hainan University, Haikou 570228, China; ali.ali.md111@gmail.com (S.A.); linzhizhe@hainanu.edu.cn (Z.L.) 
245 1 |a Large Language Models in Genomics—A Perspective on Personalized Medicine 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
610 4 |a Food & Drug Administration--FDA 
651 4 |a United States--US 
653 |a Language 
653 |a Software 
653 |a Artificial intelligence 
653 |a Data processing 
653 |a Datasets 
653 |a Precision medicine 
653 |a Biomedical research 
653 |a Drug development 
653 |a Disease prevention 
653 |a Genomics 
653 |a Customization 
653 |a Chatbots 
653 |a Health care 
653 |a Natural language 
653 |a Accuracy 
653 |a Large language models 
653 |a Medical research 
653 |a Clinical decision making 
653 |a Genomic analysis 
700 1 |a Qadri Yazdan Ahmad  |u School of Computer Science and Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si 38541, Gyeongsangbuk-do, Republic of Korea; yazdan@yu.ac.kr (Y.A.Q.); swon@yu.ac.kr (S.W.K.) 
700 1 |a Khurshid, Ahmad  |u Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; k.ahmad@qu.edu.sa 
700 1 |a Lin Zhizhe  |u School of Cyberspace Security, Hainan University, Haikou 570228, China; ali.ali.md111@gmail.com (S.A.); linzhizhe@hainanu.edu.cn (Z.L.) 
700 1 |a Man-Fai, Leung  |u School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK; man-fai.leung@aru.ac.uk 
700 1 |a Kim, Sung Won  |u School of Computer Science and Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si 38541, Gyeongsangbuk-do, Republic of Korea; yazdan@yu.ac.kr (Y.A.Q.); swon@yu.ac.kr (S.W.K.) 
700 1 |a Vasilakos, Athanasios V  |u Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway 
700 1 |a Zhou, Teng  |u School of Cyberspace Security, Hainan University, Haikou 570228, China; ali.ali.md111@gmail.com (S.A.); linzhizhe@hainanu.edu.cn (Z.L.) 
773 0 |t Bioengineering  |g vol. 12, no. 5 (2025), p. 440 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3211859438/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3211859438/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3211859438/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch