ADVANCING THE FRONTIERS OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING HEALTHCARE: A COMPREHENSIVE LITERATURE REVIEW
Main Article Content
Keywords
Artificial Intelligence, Healthcare, Diagnostics, Treatment Planning, Drug Discovery, Patient Engagement, Administrative Processes, Data Privacy, Algorithmic Bias, Regulatory Frameworks, Interoperability, Ethical Use
Abstract
The integration of artificial intelligence (AI) in healthcare has ushered in a new era of innovation, promising transformative advancements in diagnostics, treatment planning, patient care, and operational efficiency. This paper presents a comprehensive review of the significant progress and promising applications of AI in healthcare, focusing on key areas such as disease diagnosis, treatment planning, drug discovery, patient engagement, and administrative processes. While AI offers immense potential to revolutionize healthcare delivery and improve patient outcomes, challenges including data privacy, algorithmic bias, regulatory frameworks, and interoperability must be carefully addressed to ensure ethical and effective implementation. Collaborative efforts among healthcare professionals, technologists, ethicists, and policymakers are crucial to overcoming these challenges and driving progress in the field of AI in healthcare.
References
2. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-29.
3. Beam AL, Kohane IS. Big data and machine learning in health care. Jama. 2018;319(13):1317-1318.
4. Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. N Engl J Med. 2018;378(11):981.
5. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
6. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. nature. 2017;542(7639):115-118.
7. Magrabi F, Ammenwerth E, McNair JB, et al. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications: A Position Paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for Assessment of Health Information Systems. Yearb Med Inform. 2019;28(01):128-134. doi:10.1055/s-0039-1677903
8. Kasula BY. Framework Development for Artificial Intelligence Integration in Healthcare: Optimizing Patient Care and Operational Efficiency. Trans Latest Trends IoT. 2023;6(6):77-83.
9. Abràmoff MD, Tarver ME, Loyo-Berrios N, et al. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med. 2023;6(1):170.
10. Agarwal R, Bjarnadottir M, Rhue L, et al. Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy Technol. 2023;12(1):100702.
11. Verma M. AI Safety and Regulations: Navigating the Post-COVID Era: Aims, Opportunities, and Challenges: A ChatGPT Analysis. Publ Int J Trend Sci Res Dev Ijtsrd ISSN. Published online 2023:2456-6470.
12. Lehne M, Sass J, Essenwanger A, Schepers J, Thun S. Why digital medicine depends on interoperability. NPJ Digit Med. 2019;2(1):79.
13. Sullivan BA, Beam K, Vesoulis ZA, et al. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol. Published online 2023:1-11.
14. Kiradoo G. Unlocking the Potential of AI in Business: Challenges and Ethical Considerations. Recent Prog Sci Technol. 2023;6:205-220.
15. Alhur AA, Alhur AA, Aldhafeeri MD, et al. Telemental health and artificial intelligence: knowledge and attitudes of Saudi Arabian individuals towards ai-integrated telemental health. J Popul Ther Clin Pharmacol. 2023;30(17):1993-2009.
16. Alhur A, Alhur A, Alhur A, et al. Evaluating Computer Science Students’ Experiences and Motivation Towards Learning Artificial Intelligence. Br J Teach Educ Pedagogy. 2023;2(3):49-56.
17. Sinha A, Kumar G, Khuman LY, Swamy S. Breast Cancer Detection Using Machine Learning. Accessed February 28, 2024. https://www.academia.edu/download/107757563/ijraset.2022.pdf
18. Leimanis A, Palkova K. Ethical guidelines for artificial intelligence in healthcare from the sustainable development perspective. Eur J Sustain Dev. 2021;10(1):90-90.
19. Bjerring JC, Busch J. Artificial Intelligence and Patient-Centered Decision-Making. Philos Technol. 2021;34(2):349-371. doi:10.1007/s13347-019-00391-6
20. Dwivedi YK, Hughes L, Ismagilova E, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag. 2021;57:101994.