• Nino Mikava
  • Tamta Mamulaidze



The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into the healthcare sector has catalyzed transformative changes across the globe. This comprehensive article delves into the multifaceted impact of AI and ML on healthcare quality, data management, and clinical practices. Moreover, it examines these trends within both a global context and the specific framework of the Republic of Georgia. The purpose of this study was to specify the most important implications of AI in healthcare for developing countries and to assess perspectives and challenges of implementation. At the first stage, desk research was performed. Fifty relevant scientific articles and reports were identified, by key words, with utilization of various scientific bases and analyzed. Moreover, the major findings of the desk research, regarding implications of AI in healthcare in developing countries and challenges were used for the qualitative research. More specifically, in-depth interviews (overall 10) were conducted with various stakeholders of Georgia’s healthcare system and two focus-group discussions (FGD) were moderated with medical professionals and specialists. The purpose of in-depth interviews and FGDs was assessment of attitudes and perceptions of major stakeholders about AI implementation and utilization. According to the reviewed literature, perceptions and attitudes of stakeholders are very important for the successful implementation. However, this issue is not evaluated sufficiently, especially in developing countries. According to the results of the study AI can have substantial economic benefit for the developing countries, with consideration of the monetary savings, improved level of healthcare quality and increased patient safety. As the findings of the qualitative research demonstrate attitudes and perceptions of the doctors and important stakeholders represent a challenge for the successful implementation of AI. Consequently, it is strongly recommended to centralize and prioritize this issue on a system’s level in the process of policy and strategy design.




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