AI-Driven Personalized Medicine: Assessing the Impact of Federal Policies on Advancing Patient-Centric Care
DOI:
https://doi.org/10.53555/eijmhs.v6i2.203Keywords:
Patient-Centric Care, AI-driven personalized medicine, healthcare, Environmental, ● LifestyleAbstract
AI-driven personalized medicine represents a transformative approach in healthcare, promising to tailor treatment and preventive care to individual patient profiles. This paradigm shift, powered by advancements in artificial intelligence (AI), genomics, and data analytics, has the potential to dramatically improve patient outcomes and healthcare efficiency. However, the realization of its full potential is intricately linked to the regulatory and policy landscape shaped by federal entities. This article assesses the impact of federal policies on advancing AI-driven personalized medicine and fostering patient-centric care. Through a comprehensive review and analysis, it explores how current regulations support or hinder innovation in personalized healthcare, highlights the challenges and opportunities presented by the integration of AI technologies in medical practices, and proposes strategies to enhance policy frameworks to better accommodate the rapid pace of technological advancements. The findings underscore the critical role of federal policies in enabling the effective integration of AI into healthcare, advocating for a collaborative approach among policymakers, healthcare providers, and AI developers to create a conducive environment for the growth of AI-driven personalized medicine.
References
Wiljer, D., & Hakim, Z. (2019). Developing an artificial intelligence–enabled health care practice: rewiring health care professions for better care. Journal of medical imaging and radiation sciences, 50(4), S8-S14.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17, 1-9.
Lin, H. S., & Stead, W. W. (Eds.). (2009). Computational technology for effective health care: immediate steps and strategic directions.
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.
van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011). A holistic framework to improve the uptake and impact of eHealth technologies. Journal of medical Internet research, 13(4), e1672.
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28.
Holmes, J., Sacchi, L., & Bellazzi, R. (2004). Artificial intelligence in medicine. Ann R Coll Surg Engl, 86, 334-8.
Gilvary, C., Madhukar, N., Elkhader, J., & Elemento, O. (2019). The missing pieces of artificial intelligence in medicine. Trends in pharmacological sciences, 40(8), 555-564.
Shortliffe, E. H. (2019). Artificial intelligence in medicine: weighing the accomplishments, hype, and promise. Yearbook of medical informatics, 28(01), 257-262.
Van Hartskamp, M., Consoli, S., Verhaegh, W., Petkovic, M., & Van de Stolpe, A. (2019). Artificial intelligence in clinical health care applications. Interactive journal of medical research, 8(2), e12100.
Becker, A. (2019). Artificial intelligence in medicine: What is it doing for us today?. Health Policy and Technology, 8(2), 198-205.
Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hyppönen, H., Nykänen, P., ... & Georgiou, A. (2019). Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearbook of medical informatics, 28(01), 128-134.
