Role of Artificial Intelligence in Health Care Decision Making: Balancing Innovation and Caution

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 14 | 03 | Page :
    By

    Manita Paneri,

  • Vikas Gupta,

  • Prashant Sevta,

Abstract

Healthcare is undergoing a transformation powered by artificial intelligence, which improves monitoring, diagnosis, and treatment capabilities. Among Artificial Intelligence (AI’s) shortcomings is the dearth of an emotional relationship between individuals and medical personnel. Robotic surgery procedures pose the possibility of malfunctioning machinery and mistaken assumptions. So, the present systematic review focused on exploring the boon and bane of the role of AI in predicting various abnormalities in advance to improve primary preventive measures and lower the risk of recurrent events. PRISMA guidelines 2020 have been followed for this systematic review. PubMed and Google Scholar databases are used for literature search by using Boolean words ‘Artificial Intelligence’, ‘and’, ‘Health care’, ‘free full text’. A total of 73,090 records were found on the electronic databases: PubMed and Google Scholar. By following PRISMA guidelines, 59 articles were taken into consideration. Each article has been read by all the three reviewers, and finally, 14 articles were mutually selected to assess the role of AI as a boon or bane in health care decision making. AI in healthcare has the potential to enhance clinical trials, healthcare decision-making, and medical diagnosis; however, biases as well as personalization need to be resolved. Scientific funding, teamwork, as well as cybersecurity approaches are all needed for appropriate deployment. Building trust, educating patients, and adhering to ethical standards are also crucial.

Keywords: Artificial Intelligence, AI, Health Care, PRISMA, Cardiovascular Disease

How to cite this article:
Manita Paneri, Vikas Gupta, Prashant Sevta. Role of Artificial Intelligence in Health Care Decision Making: Balancing Innovation and Caution. Research and Reviews : Journal of Computational Biology. 2025; 14(03):-.
How to cite this URL:
Manita Paneri, Vikas Gupta, Prashant Sevta. Role of Artificial Intelligence in Health Care Decision Making: Balancing Innovation and Caution. Research and Reviews : Journal of Computational Biology. 2025; 14(03):-. Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=212663


References

  1. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188–e194. doi:10.7861/fhj.2021-0095
  2. Chustecki M. Benefits and risks of AI in health care: narrative review. Interact J Med Res. 2024;13:e53616. doi:10.2196/53616
  3. Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res. 2023;28(1). doi:10.1186/s40001-023-01065-y
  4. Khan MA, Hashim MJ, Mustafa H, Baniyas MY, Suwaidi SKBMA, AlKatheeri R, et al. Global epidemiology of ischemic heart disease: results from the Global Burden of Disease Study. Cureus. 2020. doi:10.7759/cureus.9349
  5. Cho Y, Kwon J, Kim K, Medina-Inojosa JR, Jeon K, Cho S, et al. Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography. Sci Rep. 2020;10(1). doi:10.1038/s41598-020-77599-6
  6. Mason F, Pandey AC, Gadaleta M, Topol EJ, Muse ED, Quer G. AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment. NPJ Digit Med. 2024;7(1). doi:10.1038/s41746-024-01193-7
  7. Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol. 2023;13. doi:10.3389/fonc.2023.1065402
  8. Griem J, Eich M, Schallenberg S, Pryalukhin A, Bychkov A, Fukuoka J, et al. Artificial intelligence–based tool for tumor detection and quantitative tissue analysis in colorectal specimens. Mod Pathol. 2023;36(12):100327. doi:10.1016/j.modpat.2023.100327
  9. Khan B, Fatima H, Qureshi A, Kumar S, Hanan A, Hussain J, et al. Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Deleted J. 2023;1(2):731–738. doi:10.1007/s44174-023-00063-2
  10. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88:105906.
  11. Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, Bouzid Z, Faramand Z, Alrawashdeh MO, et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med. 2023;29(7):1804–13. https://doi.org/10.1038/s41591-023-02396-3
  12. Miller RJH, Bednarski BP, Pieszko K, Kwiecinski J, Williams MC, Shanbhag A, et al. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study. EBioMedicine. 2024;99:104930. https://doi.org/10.1016/j.ebiom.2023.104930
  13. Toprak B, Solleder H, Di Carluccio E, Greenslade JH, Parsonage WA, Schulz K, et al. Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study. Lancet Digit Health. 2024;6(10):e729–38. https://doi.org/10.1016/S2589-7500(24)00191-2
  14. Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021;11(4):900–15. https://doi.org/10.1158/2159-8290.cd-21-0090
  15. Chen S, Phuc PT, Nguyen P, Burton W, Lin S, Lin W, et al. A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning. Cancer Med. 2023;12(19):19987–99. https://doi.org/10.1002/cam4.6547
  16. Kakileti ST, Madhu HJ, Krishnan L, Manjunath G, Sampangi S, Ramprakash H. Observational study to evaluate the clinical efficacy of thermalytix for detecting breast cancer in symptomatic and asymptomatic women. JCO Glob Oncol. 2020;6:1472–80. https://doi.org/10.1200/go.20.00168
  17. Grist JT, Withey S, Bennett C, Rose HEL, MacPherson L, Oates A, et al. Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors. Sci Rep. 2021;11(1):96189. https://doi.org/10.1038/s41598-021-96189-8
  18. Zhao S, Yang W, Wang S, Pan P, Wang R, Chang X, et al. Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning. World J Gastroenterol. 2021;27(31):5232–46. https://doi.org/10.3748/wjg.v27.i31.5232
  19. Nakao M, Shirotsuki K, Sugaya N. Cognitive–behavioral therapy for management of mental health and stress-related disorders: Recent advances in techniques and technologies. BioPsychoSoc Med. 2021;15(1):2. https://doi.org/10.1186/s13030-021-00219-w
  20. Gutierrez G, Stephenson C, Eadie J, Asadpour K, Alavi N. Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review. Front Psychiatry. 2024;15:1356773. https://doi.org/10.3389/fpsyt.2024.1356773
  21. Hasan MM, Young GJ, Shi J, Mohite P, Young LD, Weiner SG, et al. A machine learning based two-stage clinical decision support system for predicting patients’ discontinuation from opioid use disorder treatment: retrospective observational study. BMC Med Inform Decis Mak. 2021;21(1):192. https://doi.org/10.1186/s12911-021-01692-7
  22. Hussain A, Tahir A, Hussain Z, Sheikh Z, Gogate M, Dashtipour K, et al. Artificial Intelligence-Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study. J Med Internet Res. 2021;23(4):e26627. https://doi.org/10.2196/26627
  23. Habicht J, Dina L, McFadyen J, Stylianou M, Harper R, Hauser TU, et al. Generative AI–Enabled Therapy support tool for improved clinical outcomes and patient engagement in group therapy: Real-World observational study. J Med Internet Res. 2025;27:e60435. https://doi.org/10.2196/60435
  24. Canali S, Schiaffonati V, Aliverti A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digit Health. 2022;1(10):e0000104. https://doi.org/10.1371/journal.pdig.0000104
  25. Shin J, Byeon N, Yu H, Lee D, Lee H, Lee W. Effect of wearable robot Bot Fit’s hip joint-centered assist torque and voice coach on walking. BMC Musculoskelet Disord. 2024;25(1):181. https://doi.org/10.1186/s12891-024-08181-8
  26. Cheema C, Baldwin J, Rodeghero J, Werneke MW, Mioduski JE, Jeffries L, et al. Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis. J Man Manip Ther. 2025;33(1):63–72. https://doi.org/10.1080/10669817.2024.2424619
  27. Islam MT, Quispe C, Herrera-Bravo J, Sarkar C, Sharma R, Garg N, et al. Production, Transmission, Pathogenesis, and Control of Dengue Virus: A Literature-Based Undivided Perspective. Biomed Res Int. 2021;2021:4224816. https://doi.org/10.1155/2021/4224816
  28. Karolcik S, Manginas V, Chanh HQ, Daniels J, Giang NT, Huyen VNT, et al. Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study. EBioMedicine. 2024;104:105164. https://doi.org/10.1016/j.ebiom.2024.105164
  29. Huang Q, Le Y, Li S, Bian Y. Signaling pathways and potential therapeutic targets in acute respiratory distress syndrome (ARDS). Respir Res. 2024;25(1):2678. https://doi.org/10.1186/s12931-024-02678-5
  30. Maddali MV, Churpek M, Pham T, Rezoagli E, Zhuo H, Zhao W, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir Med. 2022;10(4):367–77. https://doi.org/10.1016/s2213-2600(21)00461-6
  31. Enrico B, Cristian F, Stefano B, Luigi P. Patient-ventilator asynchronies: types, outcomes and nursing detection skills. Acta Biomed. 2018;89(7-S):6–18. https://doi.org/10.23750/abm.v89i7-s.7737
  32. De Haro C, Santos-Pulpón V, Telías I, Xifra-Porxas A, Subirà C, Batlle M, et al. Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques. Crit Care. 2024;28(1):845. https://doi.org/10.1186/s13054-024-04845-y
  33. Cordero D. The downsides of artificial intelligence in healthcare. Korean J Pain. 2023;37(1):87–8. https://doi.org/10.3344/kjp.23312
  34. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22(1):10. https://doi.org/10.1186/s12910-021-00687-3
  35. Chen Z. Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanit Soc Sci Commun. 2023;10(1):2079. https://doi.org/10.1057/s41599-023-02079-x
  36. Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bull World Health Organ. 2020;98(4):251–6. https://doi.org/10.2471/blt.19.237487
  37. Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag. 2019;57:101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  38. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol (Berl). 2023;13(2):203–13. https://doi.org/10.1007/s12553-023-00738-2
Ahead of Print Subscription Original Research
Volume 14
03
Received 27/04/2025
Accepted 25/05/2025
Published 06/06/2025
Publication Time 40 Days
436

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