Inclusive Approaches in Future Health Policy: Ethical Challenges of Using Artificial Intelligence in Medicine

Hassan Al-Mutawa (1), Fatima Al-Mazrouei (2)
(1) Higher Colleges of Technology, United Arab Emirates,
(2) Khalifa University, United Arab Emirates

Abstract

The rapid integration of artificial intelligence in medicine has sparked significant advancements in patient care, diagnostics, and treatment planning. However, as artificial intelligence technologies become increasingly prevalent in healthcare, they raise complex ethical challenges that must be addressed to ensure their responsible use. These challenges include concerns about data privacy, algorithmic bias, accountability, and the potential for unequal access to artificial intelligence-based medical interventions. This study explores the ethical implications of using artificial intelligence in medicine and proposes inclusive approaches for future health policy. A qualitative research methodology was employed, including expert interviews and policy document analysis, to examine the ethical issues surrounding artificial intelligence integration in medical practice. The findings indicate that while artificial intelligence holds great promise for improving healthcare efficiency and accuracy, its implementation must be accompanied by robust regulatory frameworks that prioritize equity, inclusivity, and accountability. The study emphasizes the need for collaborative policy-making involving stakeholders from various sectors to ensure that artificial intelligence technologies are developed and deployed in ways that benefit all populations, particularly marginalized communities. The research concludes that inclusive approaches to artificial intelligence integration in healthcare policy can help mitigate ethical risks and foster a healthcare system that is both innovative and ethically sound.


 

Full text article

Generated from XML file

References

Aggarwal, S. (2021). The long road to health: Healthcare utilization impacts of a road pavement policy in rural India. Journal of Development Economics, 151(Query date: 2025-02-03 13:19:02). https://doi.org/10.1016/j.jdeveco.2021.102667

Ali, S. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.inffus.2023.101805

Ali, U. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.enbuild.2021.111073

Alowais, S. A. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04698-z

Ayers, J. W. (2023). Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine, 183(6), 589–596. https://doi.org/10.1001/jamainternmed.2023.1838

Bag, S. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.techfore.2020.120420

Barker, T. H. (2022). Revising the JBI quantitative critical appraisal tools to improve their applicability: An overview of methods and the development process. JBI Evidence Synthesis, 21(3), 478–493. https://doi.org/10.11124/JBIES-22-00125

Bauer, G. R. (2021). Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM - Population Health, 14(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.ssmph.2021.100798

Bradfield, O. M. (2021). Spoonful of honey or a gallon of vinegar? A conditional COVID-19 vaccination policy for front-line healthcare workers. Journal of Medical Ethics, 47(7), 467–472. https://doi.org/10.1136/medethics-2020-107175

Collins, G. S. (2021). Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 11(7). https://doi.org/10.1136/bmjopen-2020-048008

Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y

Dong, K. (2021). The effect of organizational information security climate on information security policy compliance: The mediating effect of social bonding towards healthcare nurses. Sustainability (Switzerland), 13(5), 1–25. https://doi.org/10.3390/su13052800

Ghassemi, M. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11). https://doi.org/10.1016/S2589-7500(21)00208-9

He, A. J. (2022). Seeking policy solutions in a complex system: Experimentalist governance in China’s healthcare reform. Policy Sciences, 55(4), 755–776. https://doi.org/10.1007/s11077-022-09482-2

Hwang, G. J. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.caeai.2022.100082

Khan, H. u. R. (2022). The impact of carbon pricing, climate financing, and financial literacy on COVID-19 cases: Go-for-green healthcare policies. Environmental Science and Pollution Research, 29(24), 35884–35896. https://doi.org/10.1007/s11356-022-18689-y

Kumar, R. (2021). Scalable and secure access control policy for healthcare system using blockchain and enhanced Bell–LaPadula model. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2321–2338. https://doi.org/10.1007/s12652-020-02346-8

Letaief, K. B. (2022). Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications. IEEE Journal on Selected Areas in Communications, 40(1), 5–36. https://doi.org/10.1109/JSAC.2021.3126076

Lu, J. (2022). The change of drug utilization in China’s public healthcare institutions under the “4 + 7” centralized drug procurement policy: Evidence from a natural experiment in China. Frontiers in Pharmacology, 13(Query date: 2025-02-03 13:19:02). https://doi.org/10.3389/fphar.2022.923209

Maltezou, H. C. (2022). Vaccination policies for healthcare personnel: Current challenges and future perspectives. Vaccine: X, 11(Query date: 2025-02-03 13:19:02). https://doi.org/10.1016/j.jvacx.2022.100172

Misra, N. N. (2022). IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet of Things Journal, 9(9), 6305–6324. https://doi.org/10.1109/JIOT.2020.2998584

Moor, M. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259–265. https://doi.org/10.1038/s41586-023-05881-4

Nooraie, R. Y. (2020). Social Network Analysis: An Example of Fusion Between Quantitative and Qualitative Methods. Journal of Mixed Methods Research, 14(1), 110–124. https://doi.org/10.1177/1558689818804060

Pan, Y. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.autcon.2020.103517

Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism and Mass Communication Educator, 78(1), 84–93. https://doi.org/10.1177/10776958221149577

Pelau, C. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.chb.2021.106855

Puntoni, S. (2021). Consumers and Artificial Intelligence: An Experiential Perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847

Santos, R. G. D. (2021). The use of classic hallucinogens/psychedelics in a therapeutic context: Healthcare policy opportunities and challenges. Risk Management and Healthcare Policy, 14(Query date: 2025-02-03 13:19:02), 901–910. https://doi.org/10.2147/RMHP.S300656

Secinaro, S. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01488-9

Shastri, B. J. (2021). Photonics for artificial intelligence and neuromorphic computing. Nature Photonics, 15(2), 102–114. https://doi.org/10.1038/s41566-020-00754-y

Shi, F. (2021). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14(Query date: 2025-02-03 13:18:24), 4–15. https://doi.org/10.1109/RBME.2020.2987975

Siontis, K. C. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews Cardiology, 18(7), 465–478. https://doi.org/10.1038/s41569-020-00503-2

Velden, B. H. M. van der. (2022). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 79(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.media.2022.102470

Vrontis, D. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398

Wang, H. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47–60. https://doi.org/10.1038/s41586-023-06221-2

White, J. (2022). The qualitative experience of telehealth access and clinical encounters in Australian healthcare during COVID-19: Implications for policy. Health Research Policy and Systems, 20(1). https://doi.org/10.1186/s12961-021-00812-z

Xu, Y. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation, 2(4). https://doi.org/10.1016/j.xinn.2021.100179

Yilmaz, M. A. (2020). Simultaneous quantitative screening of 53 phytochemicals in 33 species of medicinal and aromatic plants: A detailed, robust and comprehensive LC–MS/MS method validation. Industrial Crops and Products, 149(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.indcrop.2020.112347

Zhang, C. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23(Query date: 2025-02-03 13:18:24). https://doi.org/10.1016/j.jii.2021.100224

Zhang, J. (2021). Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things. IEEE Internet of Things Journal, 8(10), 7789–7817. https://doi.org/10.1109/JIOT.2020.3039359

Authors

Hassan Al-Mutawa
hassaaaan@gmail.com (Primary Contact)
Fatima Al-Mazrouei
Al-Mutawa, H., & Al-Mazrouei, F. (2025). Inclusive Approaches in Future Health Policy: Ethical Challenges of Using Artificial Intelligence in Medicine. Journal of World Future Medicine, Health and Nursing, 3(2), 183–192. https://doi.org/10.70177/health.v3i1.1911

Article Details