PRIORITIZING CHALLENGES IN AI APPLICATIONS IN HEALTHCARE: MULTI-CRITERIA DECISION MAKING APPROACH

Year 2022, Volume: 12 Issue: 1, 194 - 206, 31.03.2022

Gülper Basmacı

Abstract

Artificial Intelligence (AI) applications have become increasingly popular in recent years as technological capabilities have evolved. AI applications in healthcare are one of the areas that need to adapt quickly in order not to lose touch with the times. Focusing only on the opportunities that AI applications bring to healthcare can make this adaptation process problematic. Instead, it is important for the efficiency of the adaptation process to identify the challenges that may arise with AI applications in healthcare and prioritize these challenges. This study used the Analytical Hierarchy Process (AHP) method, which is the most widely used Multi-criteria Decision Making (MCDM) method. For the study, the opinions of 5 experts were obtained, 3 of whom are medical doctors and 2 are faculty members working on AI applications. The aim of the study is to provide guidance to policy makers and practitioners on the challenges they should focus on when adopting Artificial Intelligence in healthcare. The result of the study shows that the most important challenge is “Ethical Problems”. Among the “Ethical Problems”, the "Principle of Ethical Double Effect" is the most important with a value of 0,569.

Keywords

Artificial Intelligence, Healthcare, MCDM, AHP

References

• Al-Harbi, K. M. A. S. (2001). Application of the AHP in project management. International journal of project management, 19(1), 19-27.
• Baby, S. (2013). AHP modeling for multicriteria decision-making and to optimise strategies for protecting coastal landscape resources. International Journal of Innovation, Management and Technology, 4(2), 218.
• Briganti, G., & Le Moine, O. (2020). Artificial intelligence in medicine: today and tomorrow. Frontiers in medicine, 7, 27. Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609.
• Doğan, N. Ö., & Derici, S. (2019). Project Management and Efficiency of the Projects in the Industry 4.0 Era. In Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution (pp. 188-209). IGI Global.
• Kumar, G., & Parimala, N. (2020). An integration of sentiment analysis and MCDM approach for smartphone recommendation. International Journal of Information Technology & Decision Making, 19(04), 1037-1063.
• Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. Liberatore, M. J., & Nydick, R. L. (1997). Group decision making in higher education using the analytic hierarchy process. Research in Higher Education, 38(5), 593-614.
• Lin, C. C., Wang, W. C., & Yu, W. D. (2008). Improving AHP for construction with an adaptive AHP approach (A3). Automation in construction, 17(2), 180-187.
• Ngai, E. W. T. (2003). Selection of web sites for online advertising using the AHP. Information & management, 40(4), 233-242.