⚠ Official Notice: www.ijisrt.com is the official website of the International Journal of Innovative Science and Research Technology (IJISRT) Journal for research paper submission and publication. Please beware of fake or duplicate websites using the IJISRT name.



Personalized Medicine using Genomics and Artificial Intelligence: Transforming Healthcare in the Era of Precision Medicine


Authors : Mukesh Kumari; Dr. Preveen Kumari

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/y42d47f5

Scribd : https://tinyurl.com/rkk77nmz

DOI : https://doi.org/10.38124/ijisrt/26mar1536

PlumX Metrics

Semantic Scholar

ResearchGate

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Personalized medicine, also known as precision medicine, represents a paradigm shift in healthcare by tailoring diagnosis, prevention, and treatment strategies to an individual’s genetic, environmental, and lifestyle factors. The integration of genomics and artificial intelligence (AI) has accelerated this transformation by enabling high-throughput data analysis, predictive modelling, and targeted therapeutic interventions. This paper explores the convergence of genomics and AI, highlighting technological advancements, applications in disease management, challenges, and future prospects. Recent studies indicate that AI-driven genomic analysis can improve diagnostic accuracy by over 20–30% and reduce adverse drug reactions by up to 50% in pharmacogenomic applications. The paper also proposes a novel integrative framework combining multi-omics data and explainable AI for next-generation personalized healthcare.

Keywords : Personalized Medicine, Genomics, Artificial Intelligence, Precision Medicine, Pharmacogenomics, Machine Learning, Biomarkers.

References :

  1. Collins, F. S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793–795.
  2. Ashley, E. A. (2016). The Precision Medicine Initiative: A New National Effort. JAMA, 313(21), 2119–2120.
  3. Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018). The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics, 19, 581–590.
  4. Manolio, T. A. et al. (2019). Implementing genomic medicine in the clinic. Nature Reviews Genetics, 20, 491–507.
  5. Green, E. D. et al. (2020). Strategic vision for improving human health at The Forefront of Genomics. Nature, 586, 683–692.
  6. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25, 44–56.
  7. Esteva, A. et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24–29.
  8. Miotto, R. et al. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.
  9. Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16, 321–332.
  10. Angermueller, C. et al. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878.
  11. Eraslan, G. et al. (2019). Deep learning: new computational modelling techniques for genomics. Nature Reviews Genetics, 20, 389–403.
  12. Zou, J. et al. (2019). A primer on deep learning in genomics. Nature Genetics, 51, 12–18.
  13. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318.
  14. Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in Bioinformatics, 18(5), 851–869.
  15. Ching, T. et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141).
  16. Relling, M. V., & Evans, W. E. (2015). Pharmacogenomics in the clinic. Nature, 526, 343–350.
  17. Dunnenberger, H. M. et al. (2015). Implementation of pharmacogenomics in clinical practice. Clinical Pharmacology & Therapeutics, 97(4), 394–402.
  18. Caudle, K. E. et al. (2017). Clinical Pharmacogenetics Implementation Consortium guidelines. Clinical Pharmacology & Therapeutics, 101(3), 317–320.

Personalized medicine, also known as precision medicine, represents a paradigm shift in healthcare by tailoring diagnosis, prevention, and treatment strategies to an individual’s genetic, environmental, and lifestyle factors. The integration of genomics and artificial intelligence (AI) has accelerated this transformation by enabling high-throughput data analysis, predictive modelling, and targeted therapeutic interventions. This paper explores the convergence of genomics and AI, highlighting technological advancements, applications in disease management, challenges, and future prospects. Recent studies indicate that AI-driven genomic analysis can improve diagnostic accuracy by over 20–30% and reduce adverse drug reactions by up to 50% in pharmacogenomic applications. The paper also proposes a novel integrative framework combining multi-omics data and explainable AI for next-generation personalized healthcare.

Keywords : Personalized Medicine, Genomics, Artificial Intelligence, Precision Medicine, Pharmacogenomics, Machine Learning, Biomarkers.

Paper Submission Last Date
30 - April - 2026

SUBMIT YOUR PAPER CALL FOR PAPERS
Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe