Artificial Intelligence in Addressing Cost, Efficiency, and Access Challenges in Healthcare
Tufael1*, Atiqur Rahman Sunny2, Md Tahsin Salam3, Kaniz Fatima Bari4, Md Sohel Rana5
Journal of Primeasia 4(1) 1-5 https://doi.org/10.25163/primeasia.419798
Submitted: 17 July 2023 Revised: 20 September 2023 Published: 27 September 2023
Abstract
Objective: The healthcare industry is undergoing a transformative phase marked by rising costs and a shortage of healthcare professionals, necessitating the adoption of information technology-based solutions (Shaheen, 2021). Methods: This study explores the integration of artificial intelligence (AI) in addressing these challenges and enhancing healthcare systems. Using a systematic literature review, we examined the role of AI in overcoming issues such as limited access, high costs, inefficiencies, and an aging population (Greenberg et al., 2020; Maphumulo & Bhengu, 2019). Our analysis focused on applications of AI in drug discovery, clinical trials, and patient care, including intelligent care systems and healthcare robotics (Chan et al., 2019; Vaishya et al., 2020). Results: The findings suggest that AI significantly accelerates drug discovery, streamlines clinical trials, and improves patient outcomes through advanced diagnostics and treatment support (Díaz et al., 2019; Woo, 2019). AI has proven particularly effective during the COVID-19 pandemic, where it aided in diagnosis, contact tracing, and treatment decision-making, highlighting its potential to address existing vulnerabilities in healthcare systems (Pavli et al., 2021). Conclusion: AI offers a paradigm shift in healthcare by providing clinicians with real-time access to robust clinical evidence and best practices, reducing reliance on anecdotal experience and cognitive biases (McNeill & Walton, 2002; Shaheen, 2021a). By harnessing the power of AI, healthcare systems can improve efficiency, reduce costs, and enhance patient outcomes, marking a significant step towards a more efficient, effective, and equitable healthcare system (Mayorga-Ruiz et al., 2019; Luengo-Oroz et al., 2020).
Keywords: Artificial Intelligence, Healthcare Efficiency, Cost Reduction, Access Improvement, Pandemic Response.
References
Agrawal, J. (2018). Stethee, an AI powered electronic stethoscope. Anaesthesia, Pain & Intensive Care, 22(3), 412–413.
Agrawal, P. (2018). Artificial intelligence in drug discovery and development. J Pharmacovigil, 6(2).
Angus, D. C. (2020). Randomized clinical trials of artificial intelligence. Jama, 323(11), 1043–1045.
Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019). Advancing drug discovery via artificial intelligence. Trends in Pharmacological Sciences, 40(8), 592–604.
Cruciger, O., Schildhauer, T. A., Meindl, R. C., Tegenthoff, M., Schwenkreis, P., Citak, M., & Aach, M. (2016). Impact of locomotion training with a neurologic controlled hybrid assistive limb (HAL) exoskeleton on neuropathic pain and health related quality of life (HRQoL) in chronic SCI: A case study. Disability and Rehabilitation: Assistive Technology, 11(6), 529–534.
Díaz, Ó., Dalton, J. A. R., & Giraldo, J. (2019). Artificial intelligence: A novel approach for drug discovery. Trends in Pharmacological Sciences, 40(8), 550–551.
Greenberg, N., Docherty, M., Gnanapragasam, S., & Wessely, S. (2020). Managing mental health challenges faced by healthcare workers during COVID-19 pandemic. BMJ, 368.
Habermann, J. (2021). Psychological impacts of COVID-19 and preventive strategies: A review.
Harrer, S., Shah, P., Antony, B., & Hu, J. (2019). Artificial intelligence for clinical trial design. Trends in Pharmacological Sciences, 40(8), 577–591.
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? ArXiv Preprint ArXiv:1712.09923.
Hummel, P., & Braun, M. (2020). Just data? Solidarity and justice in data-driven medicine. Life Sciences, Society and Policy, 16(1), 1–18.
Lee, E. (2021). How do we build trust in machine learning models? Available at SSRN 3822437.
Lip, S., Visweswaran, S., & Padmanabhan, S. (2020). Transforming clinical trials with artificial intelligence. In Artificial Intelligence (pp. 297–306). Productivity Press.
Luengo-Oroz, M., Pham, K. H., Bullock, J., Kirkpatrick, R., Luccioni, A., Rubel, S., Wachholz, C., Chakchouk, M., Biggs, P., & Nguyen, T. (2020). Artificial intelligence cooperation to support the global response to COVID-19. Nature Machine Intelligence, 2(6), 295–297.
Maphumulo, W. T., & Bhengu, B. R. (2019). Challenges of quality improvement in the healthcare of South Africa post-apartheid: A critical review. Curationis, 42(1), 1–9.
Mayorga-Ruiz, I., Jiménez-Pastor, A., Fos-Guarinos, B., López-González, R., García-Castro, F., & Alberich-Bayarri, Á. (2019). The role of AI in clinical trials. In Artificial Intelligence in Medical Imaging (pp. 231–243). Springer.
McNeill, P. M., & Walton, M. (2002). Medical harm and the consequences of error for doctors. The Medical Journal of Australia, 176(5), 222–225.
Pavli, A., Theodoridou, M., & Maltezou, H. C. (2021). Post-COVID syndrome: Incidence, clinical spectrum, and challenges for primary healthcare professionals. Archives of Medical Research.
Prabu, A. (2021). SmartScope: An AI-powered digital auscultation device to detect cardiopulmonary diseases.
Ross, S., Bond, C., Rothnie, H., Thomas, S., & Macleod, M. J. (2009). What is the scale of prescribing errors committed by junior doctors? A systematic review. British Journal of Clinical Pharmacology, 67(6), 629–640.
Shaheen, M. Y. (2021a). Adoption of machine learning for medical diagnosis.
Shaheen, M. Y. (2021b). AI in healthcare: Medical and socio-economic benefits and challenges.
Shi, D., Zhang, W., Zhang, W., & Ding, X. (2019). A review on lower limb rehabilitation exoskeleton robots. Chinese Journal of Mechanical Engineering, 32(1), 1–11.
Sucharitha, G., & Chary, D. V. (2021). Predicting the effect of COVID-19 by using artificial intelligence: A case study. Materials Today: Proceedings.
Ting, D. S. W., Liu, Y., Burlina, P., Xu, X., Bressler, N. M., & Wong, T. Y. (2018). AI for medical imaging goes deep. Nature Medicine, 24(5), 539–540.
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339.
van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., McKinney, E., Jarrett, D., Lio, P., & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1–14.
Woo, M. (2019). An AI boost for clinical trials. Nature, 573(7775), S100–S100.
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