The Southern Medical Journal (SMJ) is the official, peer-reviewed journal of the Southern Medical Association. It has a multidisciplinary and inter-professional focus that covers a broad range of topics relevant to physicians and other healthcare specialists.
SMJ // Article
Review Article
Genomics and Personalized Medicine: The DNA Revolution Reshaping Patient Care by 2035
Abstract
The rapid evolution of genomic technologies is poised to transform patient care during the next decade, integrating precision medicine with artificial intelligence (AI) to enhance diagnostic accuracy, therapeutic decision making, and personalized healthcare strategies. This review explores key advancements in genomics, including whole-genome sequencing, single-cell genomics, pharmacogenomics, and gene-editing technologies, and their potential impact on clinical practice. AI is emerging as a critical tool in genomic data interpretation, improving disease risk prediction, patient stratification, and drug-response assessments. The synergy between AI and genomics is fostering novel insights into complex diseases such as cancer, cardiovascular conditions, and neurodegenerative disorders. In addition, epigenomics and liquid biopsies are expanding early disease detection capabilities, allowing for minimally invasive diagnostics and real-time treatment monitoring. Despite these promising developments, challenges remain in accessibility, cost, and ethical considerations, particularly regarding genomic data privacy and algorithmic bias. This review highlights ongoing global initiatives, such as the UK Biobank and the 100,000 Genomes Project, as models for integrating genomic data into mainstream health care. Furthermore, the widespread adoption of gene-editing techniques such as CRISPR holds potential for curative interventions in hereditary diseases. By 2030, genomics will likely be embedded in routine clinical practice, shifting medicine toward a predictive, preventive, and personalized model. Equitable access and ethical governance must be prioritized to ensure the responsible implementation of these transformative technologies, however. This review underscores the need for interdisciplinary collaboration to maximize the clinical and societal benefits of genomic innovation.This content is limited to qualifying members.
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References
1. World Health Organization. Genomics. https://www.who.int/health-topics/genomics#tab=tab_1. Accessed February 5, 2025.
2. World Health Organization, . Accelerating access to genomics for global health. https://www.who.int/publications/i/item/9789240052857. Published July 12, 2022. Accessed February 5, 2025.
3. Baskaran AB, Kumthekar P, Heimberger AB, et al. American Society of Clinical Oncology 2021 Annual Meeting updates on primary brain tumors and CNS metastatic tumors. Future Oncol 2021;17:4425–4429.
4. Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–209.
5. Chen Z. Precision public health in China: opportunities and challenges. CCDC Wkly 2022;4: 695–696.
6. Chen R. Early bioinformatics research in China. Quant Biol 2021;9:242–250.
7. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med 2019;25:24–29.
8. Global Alliance for Genomics and Health. Framework for responsible sharing of genomic and health-related data. https://www.ga4gh.org/product/framework-for-responsible-sharing-of-genomic-and-health-related-data/. Published 2019. Accessed October 29, 2025.
9. Denny JC, Collins FS. Precision medicine in 2030—seven ways to transform healthcare. Cell 2021;184:1415–1419.
10. Méndez-Vidal C, Bravo-Gil N, Pérez-Florido J, et al. A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice. J Transl Med 2025;23:86.
11. European Commission. Horizon 2020. https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-2020_en. Accessed October 29, 2025.
12. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;372: 793–795.
13. Girisha KM, Moosa S. Genomic testing in low- and middle-income countries (LMIC). Eur J Hum Genet 2024;32:1193–1194.
14. Chu KM, Weiser TG. Real-world implementation challenges in low-resource settings. Lancet Glob Health 2021;9:e1341–e1342.
15. Knoppers BM. Framework for responsible sharing of genomic and health-related data. HUGO J 2014;8:3.
16. Rajkomar A, Hardt M, Howell MD, et al. Ensuring fairness in machine learning to advance health equity. Ann Intern Med 2018;169:866–872.
17. Turro E, Astle WJ, Megy K, et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature 2020;583:96–102.
18. Smedley D, Smith KR, Martin A, et al. 100,000 Genomes pilot on rare-disease diagnosis in health care—preliminary report. N Engl J Med 2021;385:1868–1880.
19. Sturm AC, Knowles JW, Gidding SS, et al. Clinical genetic testing for familial hypercholesterolemia: JACC Scientific Expert Panel. J Am Coll Cardiol 2018;72:662–680.
20. Parsamanesh N, Kooshkaki O, Siami H, et al. Gene and cell therapy approaches for familial hypercholesterolemia: an update. Drug Discov Today 2023;28:103470.
21. Koretsky MJ, Alvarado C, Makarious MB, et al. Genetic risk factor clustering within and across neurodegenerative diseases. Brain 2023;146:4486–4494.
22. Hirakawa MP, Krishnakumar R, Timlin JA, et al. Gene editing and CRISPR in the clinic: current and future perspectives. Biosci Rep 2020;40:BSR20200127.
23. Li T, Yang Y, Qi H, et al. CRISPR/Cas9 therapeutics: progress and prospects. Signal Transduct Target Ther 2023;8:36.
24. Bhinder B, Gilvary C, Madhukar NS, et al. Artificial intelligence in cancer research and precision medicine. Cancer Discov 2021;11:900–915.
25. Brlek P, Bulić L, Bračić M, et al. Implementing whole genome sequencing (WGS) in clinical practice: advantages, challenges, and future perspectives. Cells 2024;13:504.
26. Ma L, Guo H, Zhao Y, et al. Liquid biopsy in cancer: current status, challenges and future prospects. Sig Transduct Target Ther 2024;9:336.
27. Manolio TA, Chisholm RL, Ozenberger B, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med 2013;15:258–267.
28. Liu J, Zhao H, Huang Y, et al. Genome-wide cell-free DNA methylation analyses improve accuracy of non-invasive diagnostic imaging for early-stage breast cancer. Mol Cancer 2021;20:36.
29. Patil BM, Joshi RC, Toshniwal D. (2010). Hybrid prediction model for Type-2 diabetic patients. Expert Syst Appl 2010;37:8102–8108.
30. Fregoso-Aparicio L, Noguez J, Montesinos L, et al. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab Syndr 2021;13:148.
31. Long H, Yin H, Wang L, et al. The critical role of epigenetics in systemic lupus erythematosus and autoimmunity. J Autoimmun 2016;74:118–138.
32. Zhang Z, Zhang R. Epigenetics in autoimmune diseases: pathogenesis and prospects for therapy. Autoimmun Rev 2015;14:854–863.
33. Mazzone R, Zwergel C, Artico M, et al. The emerging role of epigenetics in human autoimmune disorders. Clin Epigenet 2019;11:34.
34. Gupta B, Hawkins RD. Epigenomics of autoimmune diseases. Immunol Cell Biol 2015;93: 271–276.
35. Chen X, Miragaia RJ, Natarajan KN, et al. A rapid and robust method for single cell chromatin accessibility profiling. Nat Commun 2018;9:5345.
36. Villanueva L, Álvarez-Errico D, Esteller M. The Contribution of epigenetics to cancer immunotherapy. Trends Immunol 2020;41:676–691.
37. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 2018;18:35–45.
38. Ke M, Elshenawy B, Sheldon H, et al. Single cell RNA-sequencing: a powerful yet still challenging technology to study cellular heterogeneity. Bioessays 2022;44:e2200084.
39. Zeng L, Yang K, Zhang T, et al. Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: a review. J Autoimmun 2022;133: 102919.
40. Kelsey G, Stegle O, Reik W. Single-cell epigenomics: recording the past and predicting the future. Science 2017;358:69–75.
41. Aradhya S, Facio FM, Metz H, et al. Applications of artificial intelligence in clinical laboratory genomics. Am J Med Genet C Semin Med Genet 2023;193:e32057.
42. Álvarez-Machancoses Ó, DeAndrés Galiana EJ, Cernea A, et al. On the role of artificial intelligence in genomics to enhance precision medicine. Pharmgenomics Pers Med 2020;13: 105–119.
43. Chafai N, Bonizzi L, Botti S, et al. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024;61:140–163.
44. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022;39:120.
45. Shajari S, Kuruvinashetti K, Komeili A, et al. The emergence of AI-based wearable sensors for digital health technology: a review. Sensors (Basel) 2023;23:9498.
46. Xie Y, Lu L, Gao F, et al. Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare. Curr Med Sci 2021;41:1123–1133.
47. McDermott JH, Leach M, Sen D, et al. The role of CYP2C19 genotyping to guide antiplatelet therapy following ischemic stroke or transient ischemic attack. Expert Rev Clin Pharmacol 2022;15:811–825.
48. Dean L, Kane M. Clopidogrel therapy and CYP2C19 genotype. https://www.ncbi.nlm.nih.gov/books/NBK84114/. Updated 2022. Accessed October 29, 2025.
49. Etienne-Grimaldi M-C, Pallet N, Boige V, et al. Current diagnostic and clinical issues of screening for dihydropyrimidine dehydrogenase deficiency. Eur J Cancer 2022;181:3–17.
50. Lešnjaković L, Ganoci L, Bilić I, et al. DPYD genotyping and predicting fluoropyrimidine toxicity: where do we stand? Pharmacogenomics 2023;24:93–106.
51. Henricks LM, Lunenburg CATC, de Man FM, et al. DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: a prospective safety analysis. Lancet Oncol 2018;19:1459–1467.
52. Taherdoost H, Ghofrani A. AI’s role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy. Intell Pharm 2024;2:643–650.
53. Singh S, Kumar R, Payra S, et al. Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus 2023;15:e44359.
54. Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Annu Rev Genomics Hum Genet 2014;15:349–370.
55. Noor J, Chaudhry A, Noor R, et al. Advancements and applications of liquid biopsies in oncology: a narrative review. Cureus 2023;15:e42731.
56. Pacesa M, Pelea O, Jinek M. Past, present, and future of CRISPR genome editing technologies. Cell 2024;187:1076–1100.
57. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 2023;23:689.
58. Mohammed Yakubu A, Chen YP. Ensuring privacy and security of genomic data and functionalities. Brief Bioinform 2020;21:511–526.
59. Antoniadi AM, Du Y, Guendouz Y, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl Sci 2021;11:5088.
60. Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. N Engl J Med 2018;378:981–983.
61. National Academies of Sciences, Engineering, and Medicine. Human Genome Editing: Science, Ethics, and Governance. Washington, DC: The National Academies Press; 2017.
