Convergence of AI and Healthcare: A Review of Machine Learning Applications
DOI:
10.47709/ijmdsa.v3i4.5158Keywords:
Keywords: Artificial Intelligence, machine learning, health care, diagnosis, cure, CDS, patient care, bias, privacy, drug development, robotic surgery, forecast, fairness, confidentiality, new drug development, robotics, big data analytics, legal, precise medicine and compliance.Dimension Badge Record
Abstract
The integration of AI and healthcare can act as the foundation for improving the medical business and the diagnosis and treatment process, as well as patient throughputs. Guided by the following objectives: the current review seeks to discuss what can perhaps be considered as the most revolutionary aspect of care: namely, use of the ML. Software such as the radiology algorithms as well as the prediction tools in healthcare are already narrowing down the error margin. This shifting of life through the practice of genetics by the improvement of AI in managing genetics as the world turns to individualized approach of patients. However, there is a list of challenges that arise when applying AI in the healthcare industry: Data protection and Algorithmic or and The question of who or what is responsible for the AI applications. Clinical decision involving the use of AI has ethical and regulatory issues that need to be addressed therein. But AI does hold a massive amount in advancing the clinical results, in reducing the costs and in making the health care system much stronger, proactive, personalized and efficient. As to the future trends for the use of AI in health care; it will be employed in pharmacology and drug development; in surgery through robot control; and patient management through tele monitoring; as well as in precision care and health information analytics and forecasting. New solutions to still pending issues in data protection, data sharing and objectivity will be important in the future of AI in health care. In sum, this paper proposed that AI is an innovative tool in healthcare’s, that has the potential to redefine the possibilities of how patient care can be delivered, and clinical work can be done, provided the steering wheel of ethical and regulatory burdens is pulled well.
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References
Brown, A. N., & Patel, D. (2017). Future Prospects of AI in Personalized Medicine. Journal of Personalized Medicine, 7(4), 15
Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullaina than S. Human decisions and machine predictions. Q J Econ. 2018; 133:237–93. https://doi.org/10.1093/qje/qjx032
Edwards V. Slave to the algorithm: Why a right to an explanation is probably not the remedy you are looking for. Duke Law Tech nol Rev. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/dltr16§ion=3
Binns R. Fairness in machine learning: Lessons from political philosophy. In: Friedler SA, Wilson C, editors. Proceedings of the 1st conference on fairness, accountability and transparency. PMLR; 2018. p. 149–59
Selbst AD, Boyd D, Friedler SA, Venkatasubramanian S, Ver tesi J. Fairness and abstraction in sociotechnical systems. In: Proceedings of the conference on fairness, accountability, and transparency. New York, USA. Association for Computing Machinery; 2019. https://doi.org/10.1145/3287560.3287598
Wong P-H. Democratizing algorithmic fairness. Philos Technol. 2020; 33:225–44. https://doi.org/10.1007/s13347-019-00355-w.
Abebe R, Barocas S, Kleinberg J, Levy K, Raghavan M, Robin son DG. Roles for computing in social change. In: Proceedings of the 2020 conference on fairness, accountability, and transpar ency. New York, USA. Association for Computing Machinery; 2020. https://doi.org/10.1145/3351095.3372871
Bærøe K, Gundersen T, Henden E, Rommetveit K. Can medical algorithms be fair? Three ethical quandaries and one dilemma. BMJ Health Care Inform. 2022. https://doi.org/10.1136/bmjhci-2021-100445.
Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021; 54:1–35. https://doi.org/10.1145/3457607
World Medical Association. Declaration of Geneva. World Medi cal Association; 1983 14. Ueda D, Shimazaki A, Miki Y. Technical and clinical overview of deep learning in radiology. Jpn J Radiol. 2019; 37:15–33. https://doi.org/10.1007/s11604-018-0795-3.
Yuba M, Iwasaki K. Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan. Sci Rep. 2022; 12:16874. https://doi.org/10.1038/s41598-022-21426-7.
Zhu S, Gilbert M, Chetty I, The SF. landscape of FDA-approved artifcial intelligence/machine learning-enabled medical devices: an analysis of the characteristics and intended use. Int J Med Inform. 2021. https://doi.org/10.1016/j.ijmedinf.2022.104828.
Kelly BS, Judge C, Bollard SM, Cliford SM, and Healy GM, and Aziz A, et al. Radiology artifcial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol. 2022; 32:7998– 8007. https://doi.org/10.1007/s00330-022-08784-6
Nagwanshi, Kapil Kumar, Dubey, Sipi, 2018. Statistical feature analysis of human footprint for personal identification using BigML and IBM Watson analytics. Arab. J. Sci. Eng. 43 (6), 2703–2712. 18
Khan, R., Zainab, H., Khan, A. H., & Hussain, H. K. (2024). Advances in Predictive Modeling: The Role of Artificial Intelligence in Monitoring Blood Lactate Levels Post-Cardiac Surgery. International Journal of Multidisciplinary Sciences and Arts, 3(4), 140-151.
Kumar, S. Chauhan and L.K. Awasthi Engineering Applications of Artificial Intelligence 120 (2023) 105894
Zainab, H., Khan, A. H., Khan, R., & Hussain, H. K. (2024). Integration of AI and Wearable Devices for Continuous Cardiac Health Monitoring. International Journal of Multidisciplinary Sciences and Arts, 3(4), 123-139.
Nancy, A. Angel, Ravindran, Dakshanamoorthy, Vincent, PM. Durai Raj, Srinivasan, Kathiravan, Reina, Daniel Gutierrez, 2022. Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics 11 (15), 2292.
Narula, Sukrit, Shameer, Khader, Omar, Alaa Mabrouk Salem, Dudley, Joel T., Sengupta, Partho P., 2016. Machine-learning algorithms to automate morphological and functional assessments in 2d echocardiography. J. Am. Coll. Cardiol. 68 (21), 2287–2295.
Bian, Kai, Chen, Weijiang, Wang, Litian, Shen, Haibin, Li, Chengrong, Wang, Yanli, Zhao, Haijun, 2013. Lightning protection of traction power supply catenary of highspeed railway. In: Xuebao, Zhongguo Dianji Gongcheng (Ed.), Proceedings of the Chinese Society of Electrical Engineering. Vol. 33. Chinese Society for Electrical Engineering, pp. 191–199. B
iswas, Sitanath, Dash, Sujata, 2022. LSTM-CNN deep learning–based hybrid system for real-time COVID-19 data analysis and prediction using twitter data. In: Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer, pp. 239–257.
Bollier, David, 2017. Artificial intelligence comes of age. In: The Promise and Challenge of Integrating AI into Cars, Healthcare and Journalism. The Aspen Institute Communications and Society Program, Washington, DC.
Bordoloi, Dibyahash, Singh, Vijay, Sanober, Sumaya, Buhari, Seyed Mohamed, Ujjan, Javed Ahmed, Boddu, Rajasekhar, 2022. Deep learning in healthcare system for quality of service. J. Healthc. Eng. 2022.
Borenstein, Jason, Pearson, Yvette, 2010. Robot caregivers: harbingers of expanded freedom for all? Ethics Inform. Technol. 12 (3), 277–288.
Khan, M. I., Arif, A., & Khan, A. R. A. (2024). AI-Driven Threat Detection: A Brief Overview of AI Techniques in Cybersecurity. BIN: Bulletin Of Informatics, 2(2), 248-261.
Boulding, William, Glickman, Seth W., Manary, Matthew P., Schulman, Kevin A., Staelin, Richard, 2011. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am. J. Managed Care 17 (1), 41–48.
Bronfenbrenner, Urie, 1977. Toward an experimental ecology of human development. Am. Psychol. 32 (7), 513.
Buntin, Melinda Beeuwkes, Burke, Matthew F., Hoaglin, Michael C., Blumenthal, David, 2011. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff. 30 (3), 464–471.
Burlacu, Alexandru, Iftene, Adrian, Busoiu, Eugen, Cogean, Dragos, Covic, Adrian, 2020. Challenging the supremacy of evidence-based medicine through artificial intelligence: the time has come for a change of paradigms.
Cho, Kyunghyun, Courville, Aaron, Bengio, Yoshua, 2015. Describing multimedia content using attention-based encoder–decoder networks. IEEE Trans. Multimed. 17 (11), 1875–1886. Coeckelbergh, Mark, 2010. Health care, capabilities, and AI assistive technologies. Ethical Theory Moral Pract. 13 (2), 181–190.
Coeckelbergh, Mark, 2016. Care robots and the future of ICT- mediated elderly care: a response to doom scenarios. AI Soc. 31 (4), 455–462.
Cruz-Miguel, Edson E., García-Martínez, José R., Rodríguez-Reséndiz, Juvenal, CarrilloSerrano, Roberto V., 2020. A new methodology for A retrofitted self-tuned controller with open-source Fpga. Sensors 20 (21), 6155.
Davenport, Thomas, Kalakota, Ravi, 2019. The potential for artificial intelligence in healthcare. Future Healthcare J. 6 (2), 94.
Debauche, Olivier, Mahmoudi, Saïd, Manneback, Pierre, Assila, Abdessamad, 2019. Fog iot for health: A new architecture for patients and elderly monitoring. Procedia Comput. Sci. 160, 289–297.
Kumamaru KK, Machitori A, Koba R, Ijichi S, Nakajima Y, Aoki S. Global and Japanese regional variations in radiologist poten tial workload for computed tomography and magnetic resonance imaging examinations. Jpn J Radiol. 2018; 36:273–81. https://doi.org/10.1007/s11604-01807245
Cozzi D, Cavigli E, Moroni C, Smorchkova O, Zantonelli G, Pradella S, et al. Ground-glass opacity (GGO): a review of the diferential diagnosis in the era of COVID-19. Jpn J Radiol. 2021; 39:721–32. https://doi.org/10.1007/s11604-021-01120-w
Aoki R, Iwasawa T, Hagiwara E, Komatsu S, Utsunomiya D, Ogura T. Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity. Jpn J Radiol. 2021;39:451–8. https://doi.org/10.1007/s11604-020-01085-2.
Zhu QQ, Gong T, Huang GQ, Niu ZF, Yue T, Xu FY, et al. Pul monary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19. Jpn J Radiol. 2021; 39:589–97. https://doi.org/10.1007/s11604-021-01094-9
Fukuda A, Yanagawa N, Sekiya N, Ohyama K, Yomota M, Inui T, et al. An analysis of the radiological factors associated with respiratory failure in COVID-19 pneumonia and the CT features among diferent age categories. Jpn J Radiol. 2021;39:783–90. https://doi.org/10.1007/s11604-021-01118-4.
Özer H, K?l?nçer A, Uysal E, Yormaz B, Cebeci H, Durmaz MS, et al. Diagnostic performance of radiological society of North America structured reporting language for chest computed tomography fndings in patients with COVID-19. Jpn J Radiol. 2021; 39:877–88. https://doi.org/10.1007/s11604-021-01128-2
Zhuang Y, Lin L, Xu X, Xia T, Yu H, Fu G, et al. Dynamic changes on chest CT of COVID-19 patients with solitary pul monary lesion in initial CT. Jpn J Radiol. 2021; 39:32–9. https://doi.org/10.1007/s11604-020-01037-w.
Kanayama A, Tsuchihashi Y, Otomi Y, Enomoto H, Arima Y, Takahashi T, et alAssociation of severe COVID-19 outcomes with radiological scoring and cardiomegaly: Findings from the COVID-19 inpatients database, Japan. Jpn J Radiol. 2022 https://doi.org/10.1007/s11604-022-01300-2
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