AI and ML Transformations in Healthcare for Enhanced Patient Care
Keywords:
AI, ML, Healthcare, Patient Care, Advanced Techniques, Data Privacy, TelemedicineAbstract
Advancing patient care in healthcare relies increasingly on advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper explores the current trends and future directions of AI and ML applications in healthcare. Through a comprehensive review of recent advancements, this research highlights the transformative impact of AI and ML on patient diagnosis, treatment, and overall healthcare delivery. From predictive analytics to personalized medicine, AI and ML technologies are revolutionizing healthcare practices, enhancing efficiency, accuracy, and patient outcomes. This paper discusses the challenges and opportunities associated with the adoption of AI and ML in healthcare and proposes strategies for maximizing their potential benefits while addressing ethical and regulatory considerations. By understanding the current landscape and anticipating future developments, healthcare stakeholders can harness the power of AI and ML to drive innovation and improve patient care.
References
Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." Nature, vol. 542, no. 7639, 2019, pp. 115-118.
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Suleyman, M. (2020). International evaluation of an AI system for breast cancer detection. Nature, 577(7788), 89–94. Retrieved from https://www.nature.com/articles/s41586-019-1799-6
Rajkomar, A., Dean, J., & Kohane, I. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 18. Retrieved from https://www.nature.com/articles/s41746-018-0029-1
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Cell, 176(1-2), 113–129. Retrieved from https://www.cell.com/fulltext/S0092-8674(19)31074-6
Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., ... & Kingsbury, B. (2018). Deep learning for healthcare. Proceedings of the National Academy of Sciences, 115(50), 12551–12558. Retrieved from https://www.pnas.org/content/115/50/12551
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. Retrieved from https://www.science.org/doi/10.1126/science.aax2342
Rajkomar, A., et al. "Scalable and accurate deep learning with electronic health records." NPJ Digital Medicine, vol. 1, no. 1, 2018, p. 18.
Obermeyer, Z., & Emanuel, E. J. "Predicting the future—big data, machine learning, and clinical medicine." New England Journal of Medicine, vol. 375, no. 13, 2016, pp. 1216-1219.
Valaboju, V. K. (2024). Reinforcement Learning in AI-Driven Assessments: Enhancing Continuous Learning and Accessibility. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(5), 297–305.
Bayyapu, S. (2021). Bridging the gap: Overcoming data, technological, and human roadblocks to AI-driven healthcare transformation. Journal of Management (JOM), 8(1), 7-14.
Choi, Y., et al. "Machine learning in healthcare." Methods, vol. 174, 2020, pp. 20-33.
Bayyapu,S. (2020). Blockchain healthcare: Redefining data ownership and trust in the medical ecosystem. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(11), 2748-2755.
Topol, E. J. "High-performance medicine: the convergence of human and artificial intelligence." Nature Medicine, vol. 25, no. 1, 2019, pp. 44-56.
Valaboju, V. K. (2024). AI-Driven Compliance Training in Finance and Healthcare: A Paradigm Shift in Regulatory Adherence. International Journal for Multidisciplinary Research (IJFMR), 6(6), 1–14.
Bayyapu, S. (2023). How data analysts can help healthcare organizations comply with HIPAA and other data privacy regulations. International Journal For Advanced Research in Science & Technology, 13(12), 669-674.
Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med. 2023 Jul 31;13(8):1214. doi: 10.3390/jpm13081214. PMID: 37623465; PMCID: PMC10455458.
Bayyapu, S. (2024). Enhancing administrative efficiency with HIT in federal healthcare. Caribbean Journal of Science and Technology, 11(2), 16-20.
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020 Oct;92(4):807-812. doi: 10.1016/j.gie.2020.06.040. Epub 2020 Jun 18. PMID: 32565184.
Valaboju, V. K. (2024). Nanoscale Innovations: Recent Advances in Materials Science and Biomedical Applications of Nanotechnology. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 854–863.
Bayyapu, S. (2022). Optimizing IT sourcing in healthcare: Balancing control, cost, and innovation. International Journal of Computer Applications, 3(1), 14-20.
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017 Jun 21;2(4):230-243. doi: 10.1136/svn-2017-000101. PMID: 29507784; PMCID: PMC5829945.
Bayyapu, S. (2023). Impact of the Internet of Medical Things (IoMT) on healthcare cybersecurity. International Journal for Innovative Engineering and Management Research, 12(12), 146-153.
Tang X. The role of artificial intelligence in medical imaging research. BJR Open. 2019 Nov 28;2(1):20190031. doi: 10.1259/bjro.20190031. PMID: 33178962; PMCID: PMC7594889.
How Can Artificial Intelligence Change Medical Imaging?. January 25, 2022. TechTarget, Inc. https://healthitanalytics.com/features/how-can-artificial-intelligence-change-medical-imaging
ALICE PARK AND VIDEO BY ANDREW D. JOHNSON. 2022. How AI Is Changing Medical Imaging to Improve Patient Care. TIME USA, LLC.
Vanessa McMains. AI in Medical Imaging Could Magnify Health Inequities, Study Finds. May 02, 2023. University of Maryland School of Medicine.
Kapelner A, Bleich J, Levine A, Cohen ZD, DeRubeis RJ, Berk R. Evaluating the Effectiveness of Personalized Medicine With Software. Front Big Data. 2021 May 18;4:572532. doi: 10.3389/fdata.2021.572532. PMID: 34085036; PMCID: PMC8167073.
Yvette C. Terrie. Advancing Research in Personalized Medicine. US Pharm. 2023;48(2):31-36.
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