Applications of Artificial Intelligence in Healthcare: From Diagnosis to Treatment Optimization
--- DOI:
https://doi.org/10.64803/jodsie.v1i1.13Keywords:
Artificial Intelligence, Healthcare, Medical Diagnosis, Treatment Optimization, Machine Learning, Ethical AIAbstract
The development of Artificial Intelligence (AI) has brought significant changes to the healthcare sector, particularly in improving diagnostic accuracy, optimizing treatment, and operational efficiency of medical services. Integrating intelligent algorithms with large-scale medical data allows healthcare systems to shift from conventional approaches toward more predictive, personalized, and data-driven services. This research aims to comprehensively examine the application of AI in healthcare, ranging from diagnostic support systems to optimizing patient care and treatment. The method used is a Systematic Literature Review (SLR) with a qualitative-descriptive approach to scientific articles published between 2020 and 2025, sourced from reputable databases such as Scopus, Web of Science, PubMed, and IEEE Xplore. The study results indicate that AI has been widely applied in medical image-based diagnosis, predictive analytics, personalized treatment planning, clinical workflow optimization, and robot-assisted surgery. Although AI has proven to improve the accuracy, speed, and adaptability of healthcare services, the main challenges still lie in ethical aspects, data privacy, algorithmic transparency, and infrastructure gaps between healthcare systems. This research concludes that the successful integration of AI in healthcare services is highly dependent on the implementation of a strong ethical framework, cross-disciplinary collaboration, and the development of transparent and reliable AI models. This finding is expected to serve as a reference for researchers and practitioners in developing sustainable and patient safety-oriented AI solutions.
References
[1] D. Talati, “AI in healthcare domain,” J. Knowl. Learn. Sci. Technol. ISSN 2959-6386, vol. 2, no. 3, pp. 256–262, 2023, doi: 10.60087/jklst.vol2.n3.p253.
[2] S. A. Alowais et al., “Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC Med. Educ., vol. 23, no. 1, 2023, doi: 10.1186/s12909-023-04698-z.
[3] S. S. Abrar, S. A. M. Isa, S. M. Hairon, M. P. Ismail, and M. N. B. N. A. Kadir, “Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models,” Obstet. Gynecol. Sci., vol. 68, no. 4, pp. 247–259, 2025, doi: 10.5468/ogs.25041.
[4] A. K. Philip, B. A. Samuel, S. Bhatia, S. A. M. Khalifa, and H. R. El-Seedi, “Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors,” Life, vol. 13, no. 1, p. 24, 2023, doi: 10.3390/life13010024.
[5] D. Dixon et al., “Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review,” Cureus, 2024, doi: 10.7759/cureus.59954.
[6] J. Iqbal et al., “Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine,” Cureus, 2023, doi: 10.7759/cureus.44658.
[7] J. Zhang, S. Xiang, and L. Li, “Economic implications of artificial intelligence-driven recommended systems in healthcare: a focus on neurological disorders,” Front. Public Heal., vol. 13, 2025, doi: 10.3389/fpubh.2025.1588270.
[8] Charles Chukwudalu Ebulue, Ogochukwu Virginia Ekkeh, Ogochukwu Roseline Ebulue, and Chukwunonso Sylvester Ekesiobi, “Developing predictive models for HIV Drug resistance: A genomic and AI approach,” Int. Med. Sci. Res. J., vol. 4, no. 5, pp. 521–543, 2024, doi: 10.51594/imsrj.v4i5.1119.
[9] M. Elhaddad and S. Hamam, “AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential,” Cureus, 2024, doi: 10.7759/cureus.57728.
[10] O. Agafonov, A. Babic, S. Sousa, and S. Alagaratnam, “Editorial: Trustworthy AI for healthcare,” Front. Digit. Heal., vol. 6, 2024, doi: 10.3389/fdgth.2024.1427233.
[11] J. Wang, “The Power of AI-Assisted Diagnosis,” EAI Endorsed Trans. e-Learning, vol. 8, no. 4, p. e3, 2023, doi: 10.4108/eetel.3772.
[12] Y.-Y. Fu, Y. Jiao, Y.-H. Liu, and S.-S. Dong, “Application and challenges of artificial intelligence in predicting perioperative complications of colorectal cancer,” World J. Gastrointest. Surg., vol. 17, no. 7, 2025, doi: 10.4240/wjgs.v17.i7.106340.
[13] H. Guo, J. Li, H. Liu, and J. He, “Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study,” BMC Med. Inform. Decis. Mak., vol. 22, no. 1, 2022, doi: 10.1186/s12911-022-01774-0.
[14] R. Meshaka, T. Gaunt, and S. C. Shelmerdine, “Artificial intelligence applied to fetal MRI: A scoping review of current research,” Br. J. Radiol., vol. 96, no. 1147, 2023, doi: 10.1259/bjr.20211205.
[15] K. Maheshwari, J. B. Cywinski, F. Papay, A. K. Khanna, and P. Mathur, “Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence,” Anesth. Analg., vol. 136, no. 4, pp. 637–645, 2023, doi: 10.1213/ANE.0000000000005952.
[16] I. Veneziani et al., “Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review,” J. Pers. Med., vol. 14, no. 1, p. 113, 2024, doi: 10.3390/jpm14010113.
[17] S. Moazemi et al., “Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review,” Front. Med., vol. 10, 2023, doi: 10.3389/fmed.2023.1109411.
[18] D. Van De Sande et al., “Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter,” BMJ Heal. Care Informatics, vol. 29, no. 1, p. e100495, 2022, doi: 10.1136/bmjhci-2021-100495.
[19] D. J. Patel, K. Chaudhari, N. Acharya, D. Shrivastava, and S. Muneeba, “Artificial Intelligence in Obstetrics and Gynecology: Transforming Care and Outcomes,” Cureus, 2024, doi: 10.7759/cureus.64725.
[20] A. Clement David-Olawade et al., “AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review,” Diagnostics, vol. 15, no. 6, p. 689, 2025, doi: 10.3390/diagnostics15060689.
[21] A. Vallée, “Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint,” J. Med. Internet Res., vol. 27, p. e72411, 2025, doi: 10.2196/72411.
[22] S. Author and S. Javanmard, “Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare,” Open Access J. Appl. Sci. Technol., vol. 2, no. 1, pp. 01–16, 2024, doi: 10.33140/oajast.02.01.07.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Muhammad Hasanuddin, Siti Khodijah (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
