PERSONALIZED MEDICINE WITH THE APPLICATION OF ARTIFICIAL INTELLIGENCE: A REVOLUTION IN DIAGNOSIS AND THERAPY

Marko Kimi Milić; Šćepan Sinanović; Tatjana Kilibarda; Saša Bubanj

doi:10.5937/afmnai42-55697

Marko Kimi Milić High Medical College of Professional Studija „Milutin Milanković”, Belgrade, Serbia
Šćepan Sinanović High Medical College of Professional Studies „Milutin Milanković”, Belgrade, Serbia https://orcid.org/0000-0002-8125-7873
Tatjana Kilibarda The Academy of Applied Preschool Teaching and Health Studies Kruševac - Department in Ćuprija, Serbia
Saša Bubanj University of Niš, Faculty of Sport and Physical Education, Niš, Serbia

DOI: https://doi.org/10.5937/afmnai42-55697

Keywords: Artificial Intelligence, Personalized Medicine, Diagnostics, Therapeutics, Drug Discovery, Chronic Disease Management

Abstract

Artificial intelligence (AI) has become a pivotal tool in transforming personalized medicine by enabling precise diagnostics, tailored therapeutics, and accelerated drug discovery. This study explores the role of AI in healthcare by conducting a systematic literature review and meta-analysis, supplemented with case studies to evaluate its impact across oncology, chronic diseases, and pharmaceutical research. The findings reveal that AI diagnostic models achieve accuracy rates exceeding 94% in detecting malignant lung nodules, significantly outperforming traditional methods. AI-driven therapeutic decision-making not only enhances treatment efficacy by 22% but also reduces adverse drug reactions by 30%. Furthermore, AI-enabled drug discovery shortens timelines by 85%, demonstrating its efficiency in identifying promising therapeutic candidates. In chronic disease management, AI-powered predictive analytics improve glycemic control by 40%, empowering patients and enhancing long-term health outcomes. Despite its potential, challenges such as algorithmic bias, data privacy, and the lack of transparent frameworks for AI adoption persist. Addressing these issues is essential for equitable and effective implementation. This study underscores AI’s transformative potential in healthcare while emphasizing the need for ethical, inclusive, and regulatory advancements to ensure its widespread adoption and long-term impact.

References

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
2. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118.
3. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–1720.
4. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358.
5. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018;71(23):2668–2679.
6. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944.
7. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–453.
8. Maddox TM, Rumsfeld JS, Payne PRO. Questions for Artificial Intelligence in Health Care. JAMA. 2019;321(1):31–32.
9. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities, and challenges. Brief Bioinform. 2018;19(6):1236–1246.
10. Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA. 2016;315(6):551–552.
11. Turing AM. Computing machinery and intelligence. Mind. 1950;59(236):433–460.
12. Saillard C, Schmauch B, Laifa O, Moarii M, Toldo S, Zaslavskiy M, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology. 2020;72(6):1932–1946.
13. Obermeyer Z, Emanuel EJ. Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219.
14. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–98.
15. Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–e273.
16. Alkhatib A, Bernstein MS. Street–level algorithms: A theory at the gaps between policy and decisions. CHI Conf Hum Factors Comput Syst. 2019;2019:530.
17. Vincent JL, Singer M. Critical care: advances and future perspectives. Lancet. 2010;376(9749):1354–1361.
18. Topol EJ. The creative destruction of medicine: how the digital revolution will create better health care. Basic Books; 2012.
19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444.
20. Zhang H, Li S, Wu J. Deep learning-based diagnostic accuracy for lung cancer detection using CT imaging. Comput Methods Programs Biomed. 2023;230:107318.
21. Lopez A, Wang H, Garcia M. Predicting drug responses in rheumatoid arthritis using AI: A multi-omics approach. Nat Med. 2022;28(3):543–552.
22. Kim J, Lee Y, Chen D. Accelerating drug discovery with GANs: A case study on glioblastoma. Sci Transl Med. 2023;15(124):e012345.
23. Nguyen T, Harris K, Cooper J. Real-time glycemic control with AI-powered monitoring systems. Diabetes Care. 2024;47(1):67–74.
24. Brown A, Davis K, White L. Addressing algorithmic bias in cardiovascular risk prediction. Lancet Digital Health. 2023;5(9):e467–e475.
25. Wang G, Li X, Deng Z. AI-enhanced detection of lung nodules: A clinical evaluation of CT-based diagnostic systems. Eur Radiol. 2023;33(2):1789–1797.
26. Gupta A, Wang Y, Shankar S. Ensuring fairness in AI diagnostics: Addressing bias in training datasets. J Biomed Inform. 2022;129:104005.
27. Patel M, Joshi R, Arora K. Multi-omics machine learning for personalized drug response prediction. BMC Genomics. 2023;24(1):45.
28. Simmons S, Kang L, Blake D. Trustworthy AI in healthcare: Exploring explainability frameworks for clinical adoption. IEEE Trans Med Imaging. 2022;41(8):2015–2023.
29. Chen X, Gao Y, He Q. AI in drug discovery: Lessons learned from the COVID-19 pandemic. Curr Opin Pharmacol. 2022;67:102300.
30. Sharma P, Singh S, Kaur R. Regulatory considerations for AI-driven drug discovery. Nat Rev Drug Discov. 2023;22(3):182–185.
31. Lee J, Park H, Kim S. Wearable technology for diabetes management: AI-powered solutions. Diabetes Technol Ther. 2024;26(1):45–52.
32. Yoon H, Kang J, Choi M. Addressing cost and accessibility challenges in AI-based chronic disease management. Front Public Health. 2023;11:1095874.
33. Taylor L, Fairclough K, Harris M. Algorithmic bias in cardiovascular AI: Assessing impact and mitigation strategies. Heart. 2023;109(4):375–380.
34. Ahmed N, Barakat A, Rashid M. Data privacy in AI-powered health systems: A systematic review. Health Inf J. 2023;29(1):146045822311003.
35. Reynolds T, Cooper L, Zhang J. Interpretability in AI systems: Key strategies for improving transparency. J Med Syst. 2023;47(1):12.