Healing with Intelligence: A Review of AI-Enabled Healthcare Solutions
DOI:
https://doi.org/10.47709/ijmdsa.v4i3.6839Keywords:
Artificial intelligence, healthcare, diagnostics, personalized medicine, telehealth, hospital operations, public health.Abstract
Artificial intelligence (AI) is taking the healthcare field by storm as healthcare providers adopt its use to inform data-based decisions, improve clinical decision-making, and make their operations more efficient. This review discusses the fundamentals of AI, including machine learning, deep learning, and natural language processing technologies and how they can be applied to diagnostics, individualized treatment, remote patient monitoring, hospital operations, and population health monitoring. The strengths of AI are the ability to identify early disease, custom care plans, and precognitive analysis to direct resources. Nevertheless, integration in healthcare systems is stalled by risk of having biased algorithms, data privacy, interoperability, and changing demands of regulatory guidelines. A solution to such barriers is interdisciplinary: combining multiple views to develop and validate the models legitimately, with transparency and trustworthiness. Future trends, such as explainable AI, federated learning and integration of the robots aim at a more flexible and patient-centered future. After all, the best role that AI can play is to augment human expertise by providing more precise, proactive and fair care but without losing that critical human touch in healthcare.
References
1. Abbasian M, Khatibi E, Azimi I, et al. Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI. NPJ Digit Med 2024; 7:82.
2. Moreira MWL, Rodrigues JJPC, Korotaev V, AlMuhtadi J, Kumar N. A comprehensive review on smart decision support systems for health care. IEEE Syst J 2019; 13:3536-3545.
3. Reddy S, Rogers W, Makinen VP, et al. Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ Health Care Inform 2021; 28: e100444.
4. Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. Lecture Notes in Computer Science. New York: Springer, 2006; 1015-1021. 530 S. S. SINGH RANA ET AL.
5. Zhang D, Wang J, Zhao X. Estimating the uncertainty of average F1 scores. In: Proceedings of the 2015 International Conference on the Theory of Information Retrieval. New York: Association for Computing Machinery, 2015; 317-320.
6. Shankar, K., Perumal, E., Díaz, V. G., Tiwari, P., Gupta, D., Saudagar, A. K. J., & Muhammad, K. (2021). An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Applied Soft Computing, 113, and 107878.
7. Sharma, G. D., Yadav, A., & Chopra, R. (2020). Artificial intelligence and effective governance: A review, critique and research agenda. Sustainable Futures, 2, 100004.
8. Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., Shen, D. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Reviews in Biomedical Engineering.
9. Sim, S., & Cho, M. (2021). Convergence model of AI and IoT for virus disease control system. Personal and Ubiquitous Computing, 1-11.
10. J. Bajwa, U. Munir, A. Nori, B. Williams, Artificial intelligence in healthcare: transforming the practice of medicine, Future Health J. 8 (2) (2021) e188–e194, https://doi.org/10.7861/fhj.2021-0095.
11. Kerketta A., Balasundaram S. “Leveraging AI Tools to Bridge the Healthcare Gap in Rural Areas in India.” Published online August 1, 2024. doi:10.1101/2024.07.30 .24311228.
12. V.S. Baljepally, W. Metheny, Rural-urban disparities in baseline health factors and procedure outcomes, J. Natl. Med. Assoc. 114 (2) (2022) 227–231, https://doi.org/10.1016/j.jnma.2022.01.001.
13. E.C. Loccoh, K.E. Joynt Maddox, Y. Wang, D.S. Kazi, R.W. Yeh, R.K. Wadhera, Rural-urban disparities in outcomes of myocardial infarction, heart failure, and stroke in the United States, J. Am. Coll. Cardiol. 79 (3) (2022) 267–279, https://doi.org/10.1016/j.jacc.2021.10.045.
14. S. Bhatia, W. Landier, E.D. Paskett, et al., Rural–urban disparities in cancer outcomes: opportunities for future research, JNCI J. Natl. Cancer Inst. 114 (7) (2022) 940–952, https://doi.org/10.1093/jnci/djac030.
15. Javeedullah M. Big Data and Health Informatics: Managing Privacy, Accuracy, and Scalability. Global Trends in Science and Technology. 2025 Jul 3;1(3):29-47.
16. S. Yaemsiri, J.M. Alfier, E. Moy, et al., Healthy people 2020: rural areas lag in achieving targets for major causes of death, Health Aff. 38 (12) (2019) 2027–2031, https://doi.org/10.1377/hlthaff.2019.00915.
17. Weeks W.B., Chang J.E., Pagan ´ J.A., et al. Rural-urban disparities in health outcomes, clinical care, health behaviors, and social determinants of health and an action-oriented, dynamic tool for visualizing them, Wang Z, ed., PLOS Glob Public Health. 2023; 3(10):e0002420. doi:10.1371/journal.pgph.0002420.
18. J. Loftus, E.M. Allen, K.T. Call, S.A. Everson-Rose, Rural-urban differences in access to preventive health care among publicly insured Minnesotans, J. Rural Health 34 (S1) (2018), https://doi.org/10.1111/jrh.12235.
19. Rn Bell, Msn, D. Mha S, Lawrence, C. MSc, S. Dobrin, et al., Near-term digital health predictions: a glimpse into tomorrow’s ai-driven healthcare, Telehealth Med. Today 8 (5) (2023), https://doi.org/10.30953/thmt.v8.452.
20. S. Khavandi, F. Zaghloul, A. Higham, E. Lim, N. De Pennington, L.A. Celi, Investigating the impact of automation on the health care workforce through autonomous telemedicine in the cataract pathway: protocol for a multicenter study, JMIR Res. Protoc. 12 (2023) e49374, https://doi.org/10.2196/49374.
21. Javeedullah M. Integrating Health Informatics Into Modern Healthcare Systems: A Comprehensive Review. Global Journal of Universal Studies.;2(1):1-21.
22. J. Guo, B. Li, The application of medical artificial intelligence technology in rural areas of developing countries, Health Equity 2 (1) (2018) 174–181, https://doi.org/10.1089/heq.2018.0037.
23. F. Jiang, Y. Jiang, H. Zhi, et al., Artificial intelligence in healthcare: past, present and future, Stroke Vasc. Neurol. 2 (4) (2017) 230–243, https://doi.org/10.1136/svn-2017-000101.
24. J. Khubchandani, S. Banerjee, R.A. Yockey, K. Batra, Artificial intelligence for medicine, surgery, and public health, J. Med. Surg. Public Health 3 (2024) 100141, https://doi.org/10.1016/j.glmedi.2024.100141.
25. Paine SJ, Benator SG. JCAHO initiative seeks to improve patient safety. Medscape. 2003; 15(1):23–24
26. Winters BD, Cvach MM, Bonafide CP, Hu X, Konkani A, O'Connor MF, Rothschild JM, Selby NM, Pelter MM, McLean B, Kane-Gill SL, Society for Critical Care Medicine AlarmAlert Fatigue Task Force Technological Distractions (Part 2): A Summary of Approaches to Manage Clinical Alarms With Intent to Reduce Alarm Fatigue. Crit Care Med. 2018 Jan;46(1):130–137.
27. Hu X. An algorithm strategy for precise patient monitoring in a connected healthcare enterprise. NPJ Digit Med. 2019;2:30.
28. Javeedullah M. Future of Health Informatics: Bridging Technology and Healthcare. Global Trends in Science and Technology. 2025 Apr 4;1(1):143-59.
29. Woodward S. Moving towards a safety II approach. J Patient Safe Risk Manage. 2019 Jun 08;24(3):96–99.
30. J., Yang, H., Jin, R., Tang, X., Han, Q., Feng, H., Jiang, S., Zhong, B., Yin, & X., Hu, “Harnessing the power of llms in practice: A survey on chatgpt and beyond”, ACM Transactions on Knowledge Discovery from Data, 18(6), pp. 1-32, 2024, DOI: https://doi.org/10.1145/3649506
31. J. L., Ba, J. R., Kiros, & G. E., Hinton, (2016). Layer normalization. arXiv: 1607.06450. Retrieved December 12, 2024, from https://arxiv.org/abs/1607.06450
32. A., Ziaee, & E., Çano, “Batch Layer Normalization A new normalization layer for CNNs and RNNs”, In Proceedings of the 6th International Conference on Advances in Artificial Intelligence (pp. 40-49), 2022, October, DOI: https://doi.org/10.1145/3571560.3571566
33. Javeedullah M. Security and Privacy in Health Informatics: Safeguarding Patient Data in A Digital World. AlgoVista: Journal of AI and Computer Science.;2(3):52-68.
34. S., Naseem, “Advancing Health Literacy Through Generative AI: The Utilization of Open-Source Large Language Models (LLMS) for Text Simplification and Readability”, Master thesis, Michigan Technological University, 2024, DOI: https://doi.org/10.37099/mtu.dc.etdr/1762
35. A., Vaswani, N., Shazeer, N., Parmar, J., Uszkoreit, L., Jones, A. N., Gomez, L., Kaiser, & I., Polosukhin, (2017). Attention is all you need. Advances in Neural Information Processing Systems, arXiv (Cornell University), 30, pp. 5998– 6008. Retrieved December 12, 2024, from https://arxiv.org/pdf/1706.03762v5
36. B., Lutkevich, & E., Burns, (2021). Natural language processing (NLP). TechTarget: Newton, MA, USA. Retrieved December 12, 2024, from https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP
37. Smith-Bindman, R., Kwan, M. L., Marlow, E. C., Theis, M. K., Bolch, W., Cheng, S. Y., Bowles, E. J., Duncan, J. R., Greenlee, R. T., & Kushi, L. H. (2019). Trends in Use of Medical Imaging in US Health Care systems and in Ontario, Canada, 2000–2016. Journal of the American Medical Association, 322, 843–856
38. Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, Foran D, Do N, Golemati S, Kurc T, Huang K, Nikita KS, Veasey BP, Zervakis M, Saltz JH, Pattichis CS. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform. 2020 Jul; 24(7):1837-1857
39. Kulikowski CA, ?Medical imaging informatics: Challenges of definition and integration,? J. Amer. Med. Inform. Assoc, vol. 4, pp. 252–3, 1997.
40. Hsu W, Markey MK, and Wang MD, ?Biomedical imaging informatics in the era of precision medicine: Progress, challenges, and opportunities,? J. Amer. Med. Inform. Assoc, vol. 20, pp. 1010–1013, 2013
41. Evens R, Kaitin K. The evolution of biotechnology and its impact on health care. Health Aff (Millwood). 2015; 34(2):210–9. https://doi.org/10.1377/hlthaff.2014.1023.
42. Au L, da Silva RGL. Globalizing the Scientific Bandwagon: Trajectories of Precision Medicine in China and Brazil. Science, Technology, and Human Values. 2021. 46 (1):192–225.
43. Raimbault B, Cointet J-P, Joly P-B. Mapping the emergence of Synthetic Biology. PLoS ONE. 2016; 11(9):e0161522. https://doi.org/10.1371/journal.pone.0161522.
44. Lock K, Nguyen V-K. An Anthropology of Biomedicine. Malden, MA: WileyBlackwell; 2010. xii+506 pp. 20. Aspuru-Guzik A. A decade of Artificial Intelligence in Chemistry and materials. Digit Discovery. 2022; 2:10.
45. Papanastassiou M, Pearce R, Zanfei A. Changing perspectives on the internationalization of R&D and innovation by multinational enterprises: a review of the literature. J Int Bus Stud. 2020; 51:623–64.
46. Pascazio L, Rihm S, Naseri A, Mosbach S, Akroyd J, Kraft M. Chemical species Ontology for Data Integration and Knowledge Discovery. J Chem Inf Model. 2023 Oct; 26. https://doi.org/10.1021/acs.jcim.3c00820.
47. Soldatova LN, Clare A, Sparkes A, King RD. An ontology for a Robot scientist. Bioinformatics. 2006; 22(14):e464–71. https://doi.org/10.1093/bioinformatics/btl207.
48. King RD, Rowland J, Oliver SG, Young M, Aubrey W, Byrne E, Liakata M, Markham M, Pir P, Soldatova LN, Sparkes A, Whelan KE, Clare A. Autom Sci Sci. 2009;324(5923):85–9.
49. Sparkes A, Aubrey W, Byrne E, et al. Towards Robot scientists for autonomous scientific discovery. Autom Exp. 2010; 2(1). https://doi.org/10.1186/1759-4499-2-1.
50. Choi N, Kim H. Technological Convergence of Blockchain and Artificial Intelligence: A Review and Challenges. Electronics. 2025 Jan; 14(1):84.
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