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Alsaedi S, Ogasawara M, Alarawi M, Gao X, Gojobori T. AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare. NAR Genom Bioinform 2025; 7:lqaf038. [PMID: 40330081 PMCID: PMC12051108 DOI: 10.1093/nargab/lqaf038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2024] [Revised: 02/11/2025] [Accepted: 04/17/2025] [Indexed: 05/08/2025] Open
Abstract
The convergence of artificial intelligence (AI) and biomedical data is transforming precision medicine by enabling the use of genetic risk factors (GRFs) for customized healthcare services based on individual needs. Although GRFs play an essential role in disease susceptibility, progression, and therapeutic outcomes, a gap exists in exploring their contribution to AI-powered precision medicine. This paper addresses this need by investigating the significance and potential of utilizing GRFs with AI in the medical field. We examine their applications, particularly emphasizing their impact on disease prediction, treatment personalization, and overall healthcare improvement. This review explores the application of AI algorithms to optimize the use of GRFs, aiming to advance precision medicine in disease screening, patient stratification, drug discovery, and understanding disease mechanisms. Through a variety of case studies and examples, we demonstrate the potential of incorporating GRFs facilitated by AI into medical practice, resulting in more precise diagnoses, targeted therapies, and improved patient outcomes. This review underscores the potential of GRFs, empowered by AI, to enhance precision medicine by improving diagnostic accuracy, treatment precision, and individualized healthcare solutions.
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Affiliation(s)
- Sakhaa Alsaedi
- Computer Science, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- College of Computer Science and Engineering (CCSE), Taibah University, 42353 Madinah, Kingdom of Saudi Arabia
| | - Michihiro Ogasawara
- Department of Internal Medicine and Rheumatology, Juntendo University, 113-8431 Tokyo, Japan
| | - Mohammed Alarawi
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
- Marine Open Innovation Institute (MaOI), 113-8431 Shizuoka, Japan
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