From Data to Decisions: The Impact of AI on Healthcare Systems
Keywords:
AI in diagnosis, diagnosis, machine learning, deep learning, wise therapeutics, clinical decision support system, operations management, data protection, bias, explain ability, and regulations, drugs and molecules, telemedicine, international and domestic health care, fair health careAbstract
Artificial Intelligence (AI) has gradually permeated almost all aspects of healthcare delivery emerging as a critical enabler for continued advancement. Let us investigate literature on AI in the subject domain of healthcare which comprises diagnostics, clinical decision, medicine customization and health care regulation systems. Machine learning deep learning, natural language processing are improving the clinical analytics by improving diagnosis, treatment, and care. Furthermore, also integrally involves AI in activities and tasks that aimed at improving the facility’s operations such as the distribution of resources, patients’ traffic and numerous administrative duties. This paper acknowledges that there is a great potential in the use of AI in the healthcare sector, but there arises important ethical questions around the use of AI such as data ownership, fairness of the algorithm, and fears around transparency and accountability. Remaining issues affecting its growth include regulatory issues since the existing frameworks cannot cope with the emerging AI innovations. The future of adopting AI has potentialities on early detection of diseases and illnesses, discovery of new drugs, tele-medicine, and in the international health sector. AI will help with increasing the availability of healthcare for patients in the regions where the necessary facilities are scarce and advancing the creation of individual treatment plans. But for that to happen, it is important to solve ethical, regulating and data problems connected with AI deployment. The successful implementation of AI in HC has possibilities to achieve better patient outcomes, decrease costs and general advancement of quality and accessibility of HC for everyone in the world.
References
Yasaka K, Akai H, Sugawara H, Tajima T, Akahane M, Yosh ioka N, et al. Impact of deep learning reconstruction on intracra nial 1.5 T magnetic resonance angiography. Jpn J Radiol. 2022. https://doi.org/10.1007/s11604-021-01225-2.
Nomura Y, Hanaoka S, Nakao T, Hayashi N, Yoshikawa T, Miki S, et al. Performance changes due to diferences in training data for cerebral aneurysm detection in head MR angiography images. Jpn J Radiol. 2021; 39:1039–48. https://doi.org/10.1007/s11604-021-01153-1.
Ishihara M, Shiiba M, Maruno H, Kato M, Ohmoto-Sekine Y, Antoine C, et al. Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s fnal layer for distinguishing between aneurysm and infundibu lar dilatation. Jpn J Radiol. 2023; 41:131–41. https://doi.org/10.1007/s11604-022-01341-7.
Miki S, Nakao T, Nomura Y, Okimoto N, Nyunoya K, Nakamura Y, et al. Computer-aided detection of cerebral aneurysms with magnetic resonance angiography: usefulness of volume render ing to display lesion candidates. Jpn J Radiol. 2021; 39:652–8. https://doi.org/10.1007/s1160402101099-4.
Nakaura T, Kobayashi N, Yoshida N, Shiraishi K, Uetani H, Nagayama Y, et al. Update on the use of artifcial intelligence in hepatobiliary MR imaging. Magn Reson Med Sci. 2023; 22:147– 56. https://doi.org/10.2463/mrms.rev.2022-0102.
Naganawa S, Ito R, Kawai H, Kawamura M, Taoka T, Sakai M, et al. MR imaging of endolymphatic hydrops in fve minutes. Magn Reson Med Sci. 2022; 21:401–5. https://doi.org/10.2463/ mrms.ici.2021-0022.
Koretsune Y, Sone M, Sugawara S, Wakatsuki Y, Ishihara T, Hattori C, et al. Validation of a convolutional neural network for the automated creation of curved planar reconstruction images along the main pancreatic duct. Jpn J Radiol. 2023; 41:228–34. https://doi.org/10.1007/s11604-022-01339-1.
Anai K, Hayashida Y, Ueda I, Hozuki E, Yoshimatsu Y, Tsu kamoto J, et al. The efect of CT texture-based analysis using machine learning approaches on radiologists’ performance in dif ferentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol. 2022; 40:1156–65. https://doi.org/10.1007/s11604-022-01298-7 .
Cay N, Mendi BAR, Batur H, Erdogan F. Discrimination of lipoma from atypical lipomatous tumor/well-diferentiated lipo sarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol. 2022; 40:951–60.https://doi.org/10.1007/s1160402201278
Smith, J. A., & Johnson, R. B. (2018). Advances in Artificial Intelligence: Implications for Healthcare. Journal of Health Informatics, 10(2), 87-104. 2. Chen, L., & Wang, Y. (2019). Ethical Challenges in AI-Driven Healthcare: A Comprehensive Review. Journal of Medical Ethics, 45(3), 154-162.
Gonzalez, R., & Alvim, M. S. (2020). AI and Healthcare Disparities: A Critical Analysis. Journal of Bioethical Inquiry, 17(1), 37-48.
Kim, E., & Kim, M. S. (2019). Privacy Concerns in AI Healthcare Applications: A Survey. Journal of Cybersecurity and Privacy, 1(1), 45-56.
Li, Y., & Wu, X. (2021). The Impact of AI on Patient Autonomy: Challenges and Solutions. Journal of Medical Ethics and History of Medicine, 14, 1-12.
Rodriguez, C., & Brown, J. (2018). AI and the Digital Divide in Healthcare: Examining Access Disparities. Journal of Health Communication, 23(10), 920-927.
Wang, H., & Li, Z. (2020). Predictive Analytics in Healthcare: A Comprehensive Review. Journal of Healthcare Management, 25(3), 112-125.
Patel, S., & Kumar, P. (2019). The Role of Natural Language Processing in Clinical Documentation: A Review. Journal of Health Information Management, 34(2), 78-86. Impact Factor: 19.6 8967:09CX
Jones, M., & White, K. P. (2017). Applications of AI in Radiology: A Comprehensive Survey. Journal of Medical Imaging and Radiation Sciences, 48(2), 123-134.
Johnson, C., & Davis, R. (2018). AI in Pathology: Current Trends and Future Directions. Journal of Pathology Informatics, 9, 38.
Wang, L., & Zhang, Y. (2021). The Integration of Robotics in Healthcare: Challenges and Opportunities. Journal of Robotics in Medicine, 6(2), 67-78.
Chen, T., & Liu, X. (2019). Block chain Applications in Healthcare: A Systematic Review. Journal of Healthcare Information Management, 33(4), 102-115.
Mitchell, S., & Baker, M. (2018). Augmented Reality in Healthcare: A State-of-the-Art Review. Journal of Augmented and Virtual Reality, 2(1), 32-45.
Lewis, G., & Harris, A. (2020). AI-driven Personalized Medicine: Opportunities and Challenges. Journal of Personalized Healthcare, 1(1), 18-27.
Lee, J., & Kim, H. (2017). Algorithmic Bias in Healthcare: A Critical Analysis. Journal of Ethics in Science and Technology, 12(3), 45-57.
Yang, R., & Wang, J. (2019). The Future of AI in Healthcare: A Roadmap for Research. Journal of Future Healthcare Technology, 6(2), 89-104.
Khan, M. I., Arif, A., & Khan, A. R. A. (2024). AI's Revolutionary Role in Cyber Defense and Social Engineering. International Journal of Multidisciplinary Sciences and Arts, 3(4), 57-66.
Arif, A., Khan, A., & Khan, M. I. (2024). Role of AI in Predicting and Mitigating Threats: A Comprehensive Review. JURIHUM: Jurnal Inovasi dan Humaniora, 2(3), 297-311.
Carter, B., & Anderson, M. (2021). Exploring the Impact of AI on the Healthcare Provider Patient Relationship. Journal of Health Communication, 26(8), 620-630.
Gupta, S., & Sharma, A. (2018). Regulatory Challenges in AI-driven Healthcare: A Global Perspective. Journal of Law, Medicine & Ethics, 46(4), 922-931.
Thomas, L., & Williams, C. (2020). AI and Data Security in Healthcare: An Integrated Approach. Journal of Cybersecurity Research, 5(1), 25-36.
Wong LM, Ai QYH, Mo FKF, Poon DMC, King AD. Convolu tional neural network in nasopharyngeal carcinoma: How good is automatic delineation for primary tumor on a non-contrastenhanced fat-suppressed T2-weighted MRI? Jpn J Radiol. 2021; 39:571–9. https://doi.org/10.1007/s11604-021-01092-x.
Kabasawa H, Kiryu S. Pulse sequences and reconstruction in Fast MR imaging of the liver. Magn Reson Med Sci. 2023; 22:176–90. https://doi.org/10.2463/mrms.rev.2022-0114.
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28.
MailMyStatements, 6 Major Challenges Facing the Healthcare Industry in 2020, Nov 15, 2018 derived from: https://medium.com/@MailMyStatement/5-majorchallengesfacingthehealthcare-industry-in-2019-60218336385f
Rita Sharma, Top 10 Challenges Healthcare Companies Face today, derived from: https://www.finoit.com/blog/top-10-healthcare-challenges/
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94
Meskó, B., Hetényi, G., & Győrffy, Z. (2018). Will artificial intelligence solve the human resource crisis in healthcare?. BMC health services research, 18(1), 545
Lin, S. Y., Mahoney, M. R., & Sinsky, C. A. (2019). Ten ways artificial intelligence will transform primary care. Journal of general internal medicine, 34(8), 1626-1630
Ganapathy, K., Abdul, S. S., & Nursetyo, A. A. (2018). Artificial intelligence in neurosciences: A clinician's perspective. Neurology India, 66(4), 934.
Etienne, H., Hamdi, S., Le Roux, M., Camuset, J., Khalife-Hocquemiller, T., Giol, M., & Assouad, J. (2020). Artificial intelligence in thoracic surgery: past, present, perspective and limits. European Respiratory Review, 29(157).
Nadikattu, R. R. (2017). Artificial Intelligence in Cardiac Management. International Journal of Creative Research Thoughts, 5(3). 10. Robert, N. (2019). How artificial intelligence is changing nursing. Nursing management, 50(9), 30-39
