HSM 541 Assignment; Advancement of Artificial Intelligence in the Healthcare Industry

physicians can work more efficiently, while improving the outcome of patient care, then cognitive overload decreases. Cognitive burden or overload is the leading cause of burnout for many physicians (Iskander, 2018). Currently, physicians spend more than 50% of their time updating electronic health records (EHRs), which is a major contributor to burnout (Sahni & Carrus, 2023). By using NLP to make documenting faster and filling in CPT codes, we can reduce this burden and allow physicians to spend more time with patients. If AIM can lessen that burden and increase positive patient outcomes, then we might see more healthcare professionals staying in healthcare. Leveraging AI to Improve Treatment and Outcomes Artificial Intelligence can improve treatments, outcomes, and overall patient care in many ways. Using Natural Language Processing (NLP) integrated with chatbots, patients can have a conversation with AI that offers medical advice, potential diagnoses, treatment plans, and provides medical education (Kaul et al., 2020). AI can analyze data simultaneously and come up with more accurate predictions than humans, which would greatly reduce medical errors and misdiagnoses (Mayo Clinic Press Editors, 2024). Deep Learning (DL) is another subfield of AI that uses algorithms to create artificial neural networks, similar to the human brain, to learn and make decisions on its own (Kaul et al., 2020). Scientists are using DL to improve medical imaging accuracy, consistency, and efficiency in reporting, and the ability to diagnose patients quickly (Alowais et al., 2023). Hospitals and clinics are also using this technology in preventive medicine. For example, at the Mayo Clinic, patients come in for cancer screenings. If AI is used instead as a tool for analyzing images, tumor sizes and structures, and other measurements, then the results can be processed much faster (Mayo Clinic Press Editors, 2024). Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z American Medical Association & American Medical Association. (2024, December 13). Health care technology trends 2025: AI benefits, wearable use cases, and telehealth expansion. American Medical Association. https://www.ama-assn.org/practice-management/digital-health/health-care-technology- trends-2025-ai-benefits-wearable-use-0 Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188. https://doi.org/10.7861/fhj.2021-0095 Boniol, M., Kunjumen, T., Nair, T. S., Siyam, A., Campbell, J., & Diallo, K. (2022). The global health workforce stock and distribution in 2020 and 2030: A threat to equity and ‘universal’ health coverage? BMJ Global Health, 7(6), e009316. https://doi.org/10.1136/ bmjgh-2022-009316 Dankwa-Mullan, I. (2024). Health equity and ethical considerations in using artificial intelligence in public health and medicine. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.240245