Revolutionizing AI-driven Hypertension Care: A Review of Current Trends and Future Directions
DOI:
https://doi.org/10.55662/JST.2024.5405Keywords:
Artificial intelligence, Machine learning, Hypertension management, Predictive modeling, EthicalAbstract
Almost all countries have patients with hypertension as a standard but far-reaching medical concern, and this brings notable financial consequences. The combination of Artificial Intelligence and Machine Learning in controlling hypertension holds the potential for timely recognition, individualized management approaches, and adherence to medication monitoring. Nevertheless, healthcare faces hurdles in adopting such technologies due to data quality, system integration, ethical considerations, and regulatory barriers. This literature review mainly deals with the current state of AI and ML use in the management of hypertension. Particular attention is paid to their prediction, monitoring, and individualization of the therapeutic approaches. Key areas of interest include early detection, risk prediction, and developing individualized care plans. To promote the responsible and ethical use of AI in healthcare, future research in this field might include but not be limited to continuous monitoring, chronic disease management, and the integration of multi-modal data. Patient privacy, data security, algorithmic bias, and informed consent are the ethical issues to consider. Furthermore, the review discusses the ethical dilemmas surrounding patient privacy, data security, and programming biases in AI-driven healthcare solutions. To ensure that these technologies are effectively implemented in clinical practice, we need to address issues relating to data quality, system integration, ethics, and regulation. This may have potential results such as transforming hypertension management through sustained innovation efforts, thus improving quality care among hypertensive patients. Finally, the review highlights the future potential of AI to transform clinical practice, individualize treatment approaches, and mitigate the global impact of hypertension on public health.
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References
News-Medical. Uncontrolled hypertension wreaks havoc on global health and economies. News-Medical. https://www.news-medical.net/news/20230920/Uncontrolled-hypertension-wreaks-havoc-on-global-health-and-economies.aspx. Published September 21, 2023.
High blood pressure facts. High Blood Pressure. https://www.cdc.gov/high-blood-pressure/data-research/facts-stats/index.html. Published May 15, 2024.
Kirkland, E. B., Heincelman, M., Bishu, K. G., Schumann, S. O., Schreiner, A., Axon, R. N., Mauldin, P. D., & Moran, W. P. (2018). Trends in Healthcare Expenditures Among US Adults with Hypertension: National Estimates, 2003–2014. Journal of the American Heart Association. Cardiovascular and Cerebrovascular Disease, 7(11). https://doi.org/10.1161/jaha.118.008731
2023 ESH Hypertension Guideline Update: Bringing us Closer Together across the pond - American College of Cardiology. (2024, February 5). American College of Cardiology. https://www.acc.org/Latest-in-Cardiology/Articles/2024/02/05/11/43/2023-ESH-Hypertension-Guideline-Update
Visco, V., Izzo, C., Mancusi, C., Rispoli, A., Tedeschi, M., Virtuoso, N., Giano, A., Gioia, R., Melfi, A., Serio, B., Rusciano, M. R., Di Pietro, P., Bramanti, A., Galasso, G., D'Angelo, G., Carrizzo, A., Vecchione, C., & Ciccarelli, M. (2023). Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. Journal of cardiovascular development and disease, 10(2), 74. https://doi.org/10.3390/jcdd10020074
Krittanawong, C., Virk, H. U. H., Bangalore, S., Wang, Z., Johnson, K. W., Pinotti, R., Zhang, H., Kaplin, S., Narasimhan, B., Kitai, T., Baber, U., Halperin, J. L., & Tang, W. H. W. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific reports, 10(1), 16057. https://doi.org/10.1038/s41598-020-72685-1
Ye, C., Fu, T., Hao, S., Zhang, Y., Wang, O., Jin, B., Xia, M., Liu, M., Zhou, X., Wu, Q., Guo, Y., Zhu, C., Li, Y. M., Culver, D. S., Alfreds, S. T., Stearns, F., Sylvester, K. G., Widen, E., McElhinney, D., & Ling, X. (2018). Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning. Journal of medical Internet research, 20(1), e22. https://doi.org/10.2196/jmir.9268
McManus, R. J., Mant, J., Franssen, M., Nickless, A., Schwartz, C., Hodgkinson, J., Bradburn, P., Farmer, A., Grant, S., Greenfield, S. M., Heneghan, C., Jowett, S., Martin, U., Milner, S., Monahan, M., Mort, S., Ogburn, E., Perera-Salazar, R., Shah, S. A., Yu, L. M., … TASMINH4 investigators (2018). Efficacy of self-monitored blood pressure, with or without telemonitoring, for titration of antihypertensive medication (TASMINH4): an unmasked randomised controlled trial. Lancet (London, England), 391(10124), 949–959. https://doi.org/10.1016/S0140-6736(18)30309-X
Visco, V., Izzo, C., Mancusi, C., Rispoli, A., Tedeschi, M., Virtuoso, N., Giano, A., Gioia, R., Melfi, A., Serio, B., Rusciano, M. R., Di Pietro, P., Bramanti, A., Galasso, G., D’Angelo, G., Carrizzo, A., Vecchione, C., & Ciccarelli, M. (2023). Artificial intelligence in hypertension Management: An ace up your sleeve. Journal of Cardiovascular Development and Disease, 10(2), 74. https://doi.org/10.3390/jcdd10020074
Challa, Babu, S., & Aparna, G. (2020). Blood Pressure Prediction using Machine Learning algorithms. https://www.semanticscholar.org/paper/Blood-Pressure-Prediction-using-Machine-Learning-Challa-Babu/91740790d574c8f0ef64ac03117b247c8fcf0015
Zapata, R., Rechdan, M., Brinkley, L., Louis-Jacques, A., Modave, F., & Lemas, D. J. (2024). 1192 The Evolving Landscape of Biomedical Informatics for Postpartum Hypertension: A scoping review. American Journal of Obstetrics and Gynecology, 230(1), S623–S624. https://doi.org/10.1016/j.ajog.2023.11.1216
Amaratunga, D., Cabrera, J., Sargsyan, D., Kostis, J. B., Zinonos, S., & Kostis, W. J. (2020). Uses and opportunities for machine learning in hypertension research. International Journal of Cardiology. Hypertension, 5, 100027. https://doi.org/10.1016/j.ijchy.2020.100027
Gudigar, A., Kadri, N. A., Raghavendra, U., Samanth, J., Maithri, M., Inamdar, M. A., Prabhu, M. A., Hegde, A., Salvi, M., Yeong, C. H., Barua, P. D., Molinari, F., & Acharya, U. R. (2024). Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023). Computers in Biology and Medicine, 108207. https://doi.org/10.1016/j.compbiomed.2024.108207
Sakka, Y., Qarashai, D., & Altarawneh, A. (2023). Predicting Hypertension using Machine Learning: A Case Study at Petra University. International Journal of Advanced Computer Science and Applications/International Journal of Advanced Computer Science & Applications, 14(3). https://doi.org/10.14569/ijacsa.2023.0140368
Chaikijurajai, T., Laffin, L. J., & Tang, W. H. W. (2020). Artificial intelligence and hypertension: recent advances and future outlook. American Journal of Hypertension, 33(11), 967–974. https://doi.org/10.1093/ajh/hpaa102
Santhanam, P., & Ahima, R. S. (2019). Machine learning and blood pressure. ˜the œJournal of Clinical Hypertension, 21(11), 1735–1737. https://doi.org/10.1111/jch.13700
Kaur, S., Bansal, K., & Kumar, Y. (2022). Artificial Intelligence approaches for Predicting Hypertension Diseases: Open Challenges and Research Issues. 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), 338-343.
Islam, M. M., Alam, M. J., Maniruzzaman, M., Ahmed, N. A. M. F., Ali, M. S., Rahman, M. J., & Roy, D. C. (2023). Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia. PloS one, 18(8), e0289613. https://doi.org/10.1371/journal.pone.0289613
Hu, Y., Huerta, J., Cordella, N., Mishuris, R. G., & Paschalidis, I. C. (2023). Personalized hypertension treatment recommendations by a data-driven model. BMC medical informatics and decision making, 23(1), 44. https://doi.org/10.1186/s12911-023-02137-z
Covvey, H. D. (2018). Healthcare as a complex adaptive system. In EHealth research, theory and development (pp. 69-90). Routledge.
Murdoch, Blake. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics. 22. 10.1186/s12910-021-00687-3.
Ethical concerns around privacy and data security in AI health monitoring for Parkinson’s disease: insights from patients, family members, and healthcare professionals. (n.d.). springerprofessional.de. https://www.springerprofessional.de/en/ethical-concerns-around-privacy-and-data-security-in-ai-health-m/26656398
Rigby, M.J.. (2019). Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA Journal of Ethics. 21. 121-124. 10.1001/amajethics.2019.121.
Li J. (2023). Security Implications of AI Chatbots in Health Care. Journal of medical Internet research, 25, e47551. https://doi.org/10.2196/47551
Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?. Frontiers in surgery, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. The New England journal of medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Abdullah, S., & Kristoffersson, A. (2023). Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features. Frontiers in cardiovascular medicine, 10, 1285066. https://doi.org/10.3389/fcvm.2023.1285066
Kelly, Christopher & Karthikesalingam, Alan & Suleyman, Mustafa & Corrado, Greg & King, Dominic. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine. 17. 10.1186/s12916-019-1426-2.
Stanton, M. (2023, November 6). New artificial intelligence program could help treat hypertension. Boston University. https://www.bu.edu/articles/2023/new-artificial-intelligence-program-could-help-treat-hypertension/
Rajput, M. (2023, March 7). AI in Healthcare: Emerging Trends to Follow In 2023. RTInsights. https://www.rtinsights.com/ai-in-healthcare-emerging-trends-to-follow-in-2023/
Visco, Valeria & Izzo, Carmine & Mancusi, Costantino & Rispoli, Antonella & Tedeschi, Michele & Virtuoso, Nicola & Giano, Angelo & Gioia, Renato & Melfi, Americo & Serio, Bianca & Rusciano, Maria & Bramanti, Alessia & Galasso, Gennaro & D'angelo, Gianni & Carrizzo, Albino & Vecchione, Carmine & Ciccarelli, Michele & Pietro, Paola. (2023). Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. Journal of Cardiovascular Development and Disease. 10. 10.3390/jcdd10020074.
Kohjitani, H., Koshimizu, H., Nakamura, K., & Okuno, Y. (2024). Recent developments in machine learning modeling methods for hypertension treatment. Hypertension research : official journal of the Japanese Society of Hypertension, 47(3), 700–707. https://doi.org/10.1038/s41440-023-01547-w
Shiwlani, A., Khan, M., Sherani, A. M. K., Qayyum, M. U., & Hussain, H. K. (2024, February 28). REVOLUTIONIZING HEALTHCARE: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PATIENT CARE, DIAGNOSIS, AND TREATMENT. https://jurnalmahasiswa.com/index.php/Jurihum/article/view/845
Shiwlani, Ashish & Ahmad, Ahsan & Umar, Muhammad & Dharejo, Nasrullah & Tahir, Anoosha & Shiwlani, Sheena. (2024). Analysis of Multi-modal Data Through Deep Learning Techniques to Diagnose CVDs: A Review. International Journal of Membrane Science and Technology. 11. 402-420.
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