AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare

Authors

  • Ali Husnain Chicago State University, USA
  • Saad Rasool Concordia University Chicago, United States
  • Ayesha Saeed University of Lahore, Pakistan
  • Ahmad Yousaf Gill American National University, USA
  • Hafiz Khawar Hussain DePaul University Chicago, Illinois, USA

DOI:

https://doi.org/10.58344/jws.v2i10.448

Keywords:

artificial intelligence, healthcare, disruptive impact, personalized medicine, clinical decision-making, drug development

Abstract

In the realm of healthcare, artificial intelligence (AI) emerges as a transformative force, reshaping established practices and offering unprecedented advancements. This comprehensive analysis delves into the multifaceted ways AI is revolutionizing healthcare, focusing on its transformative capabilities, inherent challenges, and the crucial ethical complexities entwined in its application. The challenge lies in balancing transparency and accountability amid the intricate algorithms, particularly concerning the interpretability of AI-generated insights. The analysis explores ethical dilemmas tied to patient autonomy and the evolving responsibilities of healthcare providers. It advocates for open dialogue among AI systems, patients, and healthcare professionals, navigating the delicate balance between innovation and patient welfare. The article emphasizes the imperative for robust ethical frameworks and regulations governing AI implementation in healthcare. The comprehensive investigation concludes by exploring AI's potential applications in healthcare, envisioning improved medical procedures, drug discoveries, remote patient monitoring, and diagnostic enhancements. To harness AI's transformative power while safeguarding patient interests, collaboration between healthcare professionals, data scientists, policymakers, and ethicists is paramount. This abstract encapsulates the profound shifts AI has initiated in healthcare, underscoring the vital need to harness its potential while addressing the ethical and regulatory complexities arising with its integration. Ultimately, it portrays a holistic view of AI's evolving role in healthcare, highlighting its potential to revolutionize patient care, medical practices, and the entire healthcare landscape.

References

Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., & Edinbarough, I. (2022). State of Industry 5.0—Analysis and identification of current research trends. Applied System Innovation, 5(1), 27.

Alahmari, N., Alswedani, S., Alzahrani, A., Katib, I., Albeshri, A., & Mehmood, R. (2022). Musawah: a data-driven ai approach and tool to co-create healthcare services with a case study on cancer disease in Saudi Arabia. Sustainability, 14(6), 3313.

Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. (2022). Biosensors, 12(8), 562.

Bhattad, P. B., & Jain, V. (2020). Artificial intelligence in modern medicine–the evolving necessity of the present and role in transforming the future of medical care. Cureus, 12(5).

Blasimme, A., & Vayena, E. (2019). The ethics of AI in biomedical research, patient care and public health. Patient Care and Public Health (April 9, 2019). Oxford Handbook of Ethics of Artificial Intelligence, Forthcoming.

Dabla, P. K., Gruson, D., Gouget, B., Bernardini, S., & Homsak, E. (2021). Lessons learned from the COVID-19 pandemic: emphasizing the emerging role and perspectives from artificial intelligence, mobile health, and digital laboratory medicine. Ejifcc, 32(2), 224.

De Togni, G., Erikainen, S., Chan, S., & Cunningham-Burley, S. (2021). What makes AI ‘intelligent’and ‘caring’? Exploring affect and relationality across three sites of intelligence and care. Social Science & Medicine, 277, 113874.

Drabiak, K., Kyzer, S., Nemov, V., & El Naqa, I. (2023). AI and machine learning ethics, law, diversity, and global impact. The British Journal of Radiology, 96, 20220934.

Fosso Wamba, S., & Queiroz, M. M. (2021). Responsible artificial intelligence as a secret ingredient for digital health: Bibliometric analysis, insights, and research directions. Information Systems Frontiers, 1–16.

Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., & Abraham, A. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514.

Hack-Polay, D., Mahmoud, A. B., Ikafa, I., Rahman, M., Kordowicz, M., & Verde, J. M. (2023). Steering resilience in nursing practice: Examining the impact of digital innovations and enhanced emotional training on nurse competencies. Technovation, 120, 102549.

Higgins, O., Short, B. L., Chalup, S. K., & Wilson, R. L. (2023). Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review. International Journal of Mental Health Nursing.

Joda, T., Bornstein, M. M., Jung, R. E., Ferrari, M., Waltimo, T., & Zitzmann, N. U. (2020). Recent trends and future direction of dental research in the digital era. International Journal of Environmental Research and Public Health, 17(6), 1987.

Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93.

Junaid, S. B., Imam, A. A., Abdulkarim, M., Surakat, Y. A., Balogun, A. O., Kumar, G., Shuaibu, A. N., Garba, A., Sahalu, Y., & Mohammed, A. (2022). Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery. Applied Sciences, 12(20), 10271.

Khurana, B., Seltzer, S. E., Kohane, I. S., & Boland, G. W. (2020). Making the ‘invisible’visible: transforming the detection of intimate partner violence. BMJ Quality & Safety, 29(3), 241–244.

Kilroy, A., Garner, C., Parkinson, C., Kagan, C., & Senior, P. (2007). Towards transformation: Exploring the impact of culture, creativity and the arts of health and wellbeing. Arts for Health.

Lin, S. Y., Mahoney, M. R., & Sinsky, C. A. (2019). Ten ways artificial intelligence will transform primary care. Journal of General Internal Medicine, 34, 1626–1630.

Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, 1179546820927404.

McCoubrey, L. E., Gaisford, S., Orlu, M., & Basit, A. W. (2022). Predicting drug-microbiome interactions with machine learning. Biotechnology Advances, 54, 107797.

Mehta, N., Pandit, A., & Shukla, S. (2019). Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study. Journal of Biomedical Informatics, 100, 103311.

Mesko, B. (2017). The role of artificial intelligence in precision medicine. In Expert Review of Precision Medicine and Drug Development (Vol. 2, Issue 5, pp. 239–241). Taylor & Francis.

Nagaprasad, S., Padmaja, D. L., Qureshi, Y., Bangare, S. L., Mishra, M., & Mazumdar, B. D. (2021). Investigating the impact of machine learning in pharmaceutical industry. Journal of Pharmaceutical Research International, 33(46A), 6–14.

Nicholls, D. A., & Holmes, D. (2012). Discipline, desire, and transgression in physiotherapy practice. Physiotherapy Theory and Practice, 28(6), 454–465.

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022). AlphaFold, artificial intelligence (AI), and allostery. The Journal of Physical Chemistry B, 126(34), 6372–6383.

Ostrom, A. L., Field, J. M., Fotheringham, D., Subramony, M., Gustafsson, A., Lemon, K. N., Huang, M.-H., & McColl-Kennedy, J. R. (2021). Service research priorities: managing and delivering service in turbulent times. Journal of Service Research, 24(3), 329–353.

Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., Huang, K., Nikita, K. S., Veasey, B. P., Zervakis, M., Saltz, J. H., & Pattichis, C. S. (2020). AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE Journal of Biomedical and Health Informatics, 24(7), 1837–1857. https://doi.org/10.1109/JBHI.2020.2991043

Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013–1022.

Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022). Explainable AI for healthcare 5.0: opportunities and challenges. IEEE Access.

Terry, N. (2019). Of regulating healthcare AI and robots. Yale JL & Tech., 21, 133.

Thurzo, A., Strunga, M., Urban, R., Surovková, J., & Afrashtehfar, K. I. (2023). Impact of artificial intelligence on dental education: a review and guide for curriculum update. Education Sciences, 13(2), 150.

Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK.

Tregua, M., Mele, C., Russo-Spena, T., Marzullo, M. L., & Carotenuto, A. (2021). Digital Transformation in the Era of Covid-19. International Conference on Applied Human Factors and Ergonomics, 97–105.

Yu, C., & Helwig, E. J. (2022). The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artificial Intelligence Review, 1–21.

Yu, P., Xue, W., & Mahendran, R. (2022). The Development and Impact of China’s Digital Transformation in the Medical Industry. In Impact of Digital Transformation on the Development of New Business Models and Consumer Experience (pp. 97–128). IGI Global.

Zidaru, T., Morrow, E. M., & Stockley, R. (2021). Ensuring patient and public involvement in the transition to AI?assisted mental health care: A systematic scoping review and agenda for design justice. Health Expectations, 24(4), 1072–1124.

Downloads

Published

2023-10-30

How to Cite

Husnain, A., Rasool, S. ., Saeed, A. ., Yousaf Gill, A. ., & Khawar Hussain, H. . (2023). AI’S Healing Touch: Examining Machine Learning’s Transformative Effects On Healthcare. Journal of World Science, 2(10), 1681–1695. https://doi.org/10.58344/jws.v2i10.448