Main Article Content
Abstract
A formidable and innovative field of computer science, artificial intelligence (AI) has the potential to drastically alter medical practice and healthcare delivery. We explain a roadmap for developing safe, dependable, and successful AI systems, examine the potential future direction of AI-augmented healthcare systems, and highlight recent advancements in the use of AI in healthcare in this review paper. Artificial intelligence (AI) has significantly revolutionized the healthcare sector. This study provides a comprehensive review of existing literature to highlight AI's influence across several crucial areas: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug development, (iv) patient engagement and adherence, (v) rehabilitation, and (vi) administrative tasks. AI has shown remarkable potential in identifying diseases through medical imaging, aiding in the early detection and containment of COVID-19, enabling virtual healthcare through intelligent systems, optimizing the management of electronic health records, enhancing patient involvement and adherence to treatment, easing the administrative burden on healthcare providers, facilitating the discovery of new medications and vaccines, minimizing prescription errors, supporting large-scale data storage and analysis, and improving rehabilitation through advanced technologies.
Keywords
Artificial Intelligence (AI)
Digital Health Technologies (DHTs)
Healthcare Transformation
Patient-Centric Care
Big Data in Healthcare
Post-COVID-19 Healthcare Innovation
Article Details
How to Cite
Artificial Intelligence in Healthcare. (2025). International Journal of Research in Pharmacology & Pharmacotherapeutics, 14(3), 334-341. https://ijrpp.com/ijrpp/article/view/704
How to Cite
Artificial Intelligence in Healthcare. (2025). International Journal of Research in Pharmacology & Pharmacotherapeutics, 14(3), 334-341. https://ijrpp.com/ijrpp/article/view/704
References
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References
1. Snowdon A. Digital Health: A Framework for Healthcare Transformation. 2020. [(accessed on 23 January 2023)]. Available online: https://www.gs1ca.org/documents/digital_health-affht.pdf.
2. Williams O.D. COVID-19 and Private Health: Market and Governance Failure. Development. 2020;63:181–190. doi: 10.1057/s41301-020-00273-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Tabriz A.A., Nouri E., Vu H.T., Nghiem V.T., Bettilyon B., Gholamhoseyni P., Kiapour N. What should accountable care organizations learn from the failure of health maintenance organizations? A theory based systematic review of the literature. Soc. Determ. Health. 2017;3:222–247. doi: 10.22037/SDH.V3I4.20919. [DOI] [Google Scholar]
4. Rand Review Chronic Conditions in America: Price and Prevalence. 2017. [(accessed on 11 July 2021)]. Available online: https://www.rand.org/blog/rand-review/2017/07/chronic-conditions-in-america-price-and-prevalence.html.
5. World Health Organization Naming the Coronavirus Disease (COVID-19) and the Virus that Causes It. 2020. [(accessed on 6 July 2021)]. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(COVID-2019)-and-the-virus-that-causes-it.
6. Butcher C.J.T., Hussain W. Digital Healthcare: The Future, RCP Journals. Royal College of Physicians. 2022. [(accessed on 16 January 2023)]. Available online: https://www.rcpjournals.org/content/futurehosp/9/2/113.
7. McCarthy J. What is artificial intelligence? John McCarthy, 1998. [Google Scholar]
8. Shukla SS, Jaiswal V. Applicability of artificial intelligence in different fields of life. IJSER 2013;1:28–35. [Google Scholar]
9. Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition 2009:248–55. [Google Scholar]
10. Quinn TP, Senadeera M, Jacobs S, Coghlan S, Le V. Trust and medical AI: the challenges we face and the expertise needed to overcome them. J Am Med Inform Assoc 2021;28:890–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Binns R, Gallo V. Trade-offs. Information Commissioner's Office, 2019. https://ico.org.uk/about-the-ico/news-and-events/ai-blog-trade-offs
12. Hoffman RR, Johnson M, Bradshaw JM, Underbrink A. Trust in Automation. IEEE Intell. Syst 2013 Jan;28(1):84-88. [CrossRef]
13. Mcknight DH, Carter M, Thatcher JB, Clay PF. Trust in a specific technology. ACM Trans. Manage. Inf. Syst 2011 Jun;2(2):1-25. [CrossRef]
14. Parikh RB, Obermeyer Z, Navathe AS. Regulation of predictive analytics in medicine. Science 2019 Feb 21;363(6429):810-812. [CrossRef]
15. AlHogail A. Improving IoT Technology Adoption through Improving Consumer Trust. Technologies 2018 Jul 07;6(3):64. [CrossRef]
2. Williams O.D. COVID-19 and Private Health: Market and Governance Failure. Development. 2020;63:181–190. doi: 10.1057/s41301-020-00273-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Tabriz A.A., Nouri E., Vu H.T., Nghiem V.T., Bettilyon B., Gholamhoseyni P., Kiapour N. What should accountable care organizations learn from the failure of health maintenance organizations? A theory based systematic review of the literature. Soc. Determ. Health. 2017;3:222–247. doi: 10.22037/SDH.V3I4.20919. [DOI] [Google Scholar]
4. Rand Review Chronic Conditions in America: Price and Prevalence. 2017. [(accessed on 11 July 2021)]. Available online: https://www.rand.org/blog/rand-review/2017/07/chronic-conditions-in-america-price-and-prevalence.html.
5. World Health Organization Naming the Coronavirus Disease (COVID-19) and the Virus that Causes It. 2020. [(accessed on 6 July 2021)]. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(COVID-2019)-and-the-virus-that-causes-it.
6. Butcher C.J.T., Hussain W. Digital Healthcare: The Future, RCP Journals. Royal College of Physicians. 2022. [(accessed on 16 January 2023)]. Available online: https://www.rcpjournals.org/content/futurehosp/9/2/113.
7. McCarthy J. What is artificial intelligence? John McCarthy, 1998. [Google Scholar]
8. Shukla SS, Jaiswal V. Applicability of artificial intelligence in different fields of life. IJSER 2013;1:28–35. [Google Scholar]
9. Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition 2009:248–55. [Google Scholar]
10. Quinn TP, Senadeera M, Jacobs S, Coghlan S, Le V. Trust and medical AI: the challenges we face and the expertise needed to overcome them. J Am Med Inform Assoc 2021;28:890–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Binns R, Gallo V. Trade-offs. Information Commissioner's Office, 2019. https://ico.org.uk/about-the-ico/news-and-events/ai-blog-trade-offs
12. Hoffman RR, Johnson M, Bradshaw JM, Underbrink A. Trust in Automation. IEEE Intell. Syst 2013 Jan;28(1):84-88. [CrossRef]
13. Mcknight DH, Carter M, Thatcher JB, Clay PF. Trust in a specific technology. ACM Trans. Manage. Inf. Syst 2011 Jun;2(2):1-25. [CrossRef]
14. Parikh RB, Obermeyer Z, Navathe AS. Regulation of predictive analytics in medicine. Science 2019 Feb 21;363(6429):810-812. [CrossRef]
15. AlHogail A. Improving IoT Technology Adoption through Improving Consumer Trust. Technologies 2018 Jul 07;6(3):64. [CrossRef]