




































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 17, No. 2 (2023), pp. 110-120 

 

110 
 

IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE 

IN DEVELOPING COUNTRIES: PERSPECTIVES AND CHALLENGES 

 

N. MIKAVA, T. MAMULAIDZE 

 

Nino Mikava1, Tamta Mamulaidze2 

Business and Technology University, Georgia 
1 ORCID ID https://orcid.org/0000-0002-9567-3958, E-mail: nino.mikava@btu.edu.ge  
2 ORCID ID https://orcid.org/0009-0006-3457-2842  

 

Abstract: 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. 

Keywords: AI in healthcare, implications of AI in healthcare, AI for healthcare 

management, AI implications in developing countries. 

 

INTRODUCTION  

The convergence of AI and healthcare has ushered in a new era of medical innovation. AI-

powered clinics and intelligent medical systems are revolutionizing healthcare practices, ranging 

from diagnosis and treatment to data analysis and administrative efficiency. These advancements 

hold great promise in enhancing healthcare quality and accessibility while optimizing resource 

utilization.  

https://orcid.org/0000-0002-9567-3958
mailto:nino.mikava@btu.edu.ge
https://orcid.org/0009-0006-3457-2842


Nino MIKAVA, Tamta MAMULAIDZE 

 

111 
 

Transforming Healthcare Landscape AI clinics represent a paradigm shift in healthcare 

service delivery. Through machine learning algorithms, patient data can be analyzed rapidly and 

accurately, aiding in early diagnosis and predictive treatment strategies. Global initiatives in AI 

clinics are addressing the shortage of medical professionals, particularly in remote areas, by 

providing virtual consultations and intelligent diagnostic tools.  

Another area where AI's impact is far-reaching is healthcare quality. It not only assists 

medical professionals in making accurate diagnoses but also supports personalized treatment plans 

based on individual patient profiles. Machine learning algorithms, when trained on vast datasets, 

can identify hidden patterns in patient data, leading to optimized treatment strategies and reduced 

adverse effects. One of the areas of healthcare quality where AI is showing promising results is 

hospital-acquired infection rate reduction. Recent study conducted in Mayo Clinic found that risk 

prediction for Hospital-Acquired-Infections (HAI) with utilization of AI model can estimate risk 

of infection per individual patient, based on patient’s clinical features and characteristics of similar 

patients. This model was trained on 38,327 unique hospitalizations and a distinct model for 

surgical site infection prediction was trained on 18,609 hospitalizations. Accordingly, researchers 

conclude that this model could enable hospitals to prevent early detection of HAIs, which in turn 

can result in decreased length of stay, mortality, and costs (Wolff et al., 2020).  

It is well acknowledged that costs of medical care and national expenditures in this regard 

are dramatically increasing in all countries of the world. Among several causal factors, increasing 

life longevity and increased number of chronic patients carry substantial part. Even though there 

is a lack of compelling scientific evidence justifying cost-efficiency of utilization of AI in 

healthcare (Khanna et al., 2022), it still can be forecasted to cause substantial savings, according 

to the effectiveness and process optimization it demonstrates (Mathias, 2023). Regarding cost-

efficiency of AI in healthcare, special attention should be paid towards the Internet of Medical 

Technology (IoMT). IoMT represents the fastest growing sectors of IoT market, predicted to reach 

176 billion US dollars by 2026 (Shah&Chircu, 2018). The most popular area of IoMT utilization 

is remote patient monitoring and such directions, as glucose monitoring, heart rate monitoring, 

depression and mood monitoring, Parkinson’s disease monitoring, IoT connected inhalers, 

ingestible sensors, connected contact lenses and robotic surgery (Rizwan et al., 2017). 

To return to the topic of healthcare quality mentioned above, according to the report 

delivered at the conference for the Association of Professionals in Infection Control Epidemiology 

utilization of IoT for hand hygiene “showed a 61.4% decrease in HAIs (Hospital-Acquired-

Infection) across 10 hospitals that use technology” (www.cleanhands-safehands.com). Hand 

hygiene is one of the most important areas in infection control in hospitals and one of the most 

challenging, as well, about compliance from medical personnel. To illustrate this technology, in 

the badges of clinicians there is Bluetooth technology, which communicates with IoT sensors 

affixed to soap and sanitizer dispensers in patient care areas. Once a clinician walks into or out of 

a patient’s room, IoT sensors detect this, and doctors have a certain number of seconds to dispense 

sanitizer or soap. If they don’t dispense, real-time voice reminder sound reminds saying “please 

sanitize”. In case of rooms where patient has Clostridioides difficile the voice changes to “soap 

http://www.clean/


IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE IN DEVELOPING 

COUNTRIES: PERSPECTIVES AND CHALLENGES 

 

112 
 

and water only” upon exit, as sanitizer does not kill C. difficile spores. Furthermore, according to 

the data from ten hospitals, from the same study it has shown prevention of 372 total infections 

per year, translating into 9.5 million US dollar direct cost savings and 39 lives saved each year 

(Fitzpatrick, 2020). 

 

Literature review 

In the realm of healthcare, the integration of artificial intelligence has been a subject of 

profound investigation by healthcare experts. An array of seminal works and scholarly articles 

underscores the paramount significance of amalgamating technologies to propel the advancement 

of healthcare. These scholarly works provide a comprehensive view of the contemporary landscape 

of artificial intelligence within the healthcare domain, underscoring its capacity to revolutionize 

industry. Specifically, it demonstrates its potential to enhance diagnostic capabilities, individualize 

treatment strategies, and elevate the quality of patient care. Additionally, these scholarly 

contributions conscientiously recognize the multifaceted challenges of ethical, regulatory, and 

technical nature that necessitate meticulous consideration to facilitate the broad and conscientious 

adoption of artificial intelligence in healthcare. 

To be specific, one of these foundational papers by Topol provides an overview of the 

potential of AI in healthcare, from diagnostics to treatment recommendations (Topol, 2019). It 

emphasizes AI's ability to augment and improve human clinical decision-making. - "Artificial 

Intelligence for Healthcare: On a Par with Human Clinical Decision Making?" In the research 

process, Manu Agarwal Senior Manager – Growth, as a knowledgeable and cooperative 

contributor, has facilitated the acquisition of valuable insights and information. Furthermore, the 

report featured on the Markets and Markets (Markets and Markets, 2023)) presents a notable 

projection for the artificial intelligence in healthcare market. According to this report, the market 

is poised for substantial growth, with an expected transition from USD 14.6 billion in 2023 to a 

noteworthy USD 102.7 billion by 2023. This growth trajectory is underpinned by a projected 

compound annual growth rate (CAGR) of 47.6% during the stipulated forecast period. 

Justus Wolff et Al’s article “The Economic Impact of Artificial Intelligence in Health 

Care” presents a systematic review of cost-effectiveness studies related to the economic impact of 

artificial intelligence (AI) in the healthcare industry (Wolff et al., 2020). The central objectives of 

the study are to assess the quality of these studies and to highlight areas for potential improvement. 

This article offers a valuable review of the state of economic impact assessments in the context of 

AI in healthcare. It highlights the need for more robust and comprehensive studies to better inform 

decision-making in this rapidly evolving field. 

Judy Mathias article “Use of AI to predict risk of HAIs” reports on a study conducted by 

researchers at the Mayo Clinic Arizona, which focuses on the development and evaluation of an 

artificial intelligence (AI) model for predicting the risk of healthcare-associated infections (HAIs) 

(Mathias, 2023).  

The study aimed to create an AI model capable of estimating individualized risk for HAIs 

by considering a patient's clinical characteristics and those of similar patients. This article presents 



Nino MIKAVA, Tamta MAMULAIDZE 

 

113 
 

promising results from the application of AI in healthcare, particularly in the context of HAIs. The 

AI model's ability to outperform traditional models and reduce costs is a significant advancement 

in the field. The findings highlight the potential of AI to enhance patient care and reduce the burden 

of HAIs on healthcare systems, underscoring the importance of continued research and 

development in this area. 

Additionally, "Challenges and Opportunities for Machine Learning in Healthcare" - 

Obermeyer and Emanuel (2016) discuss the potential of machine learning in healthcare while also 

highlighting challenges, such as bias in algorithms and data privacy concerns (Ghassemi et al., 

2020). Literature review effectively captures a range of perspectives on AI and machine learning 

in healthcare. It emphasizes the potential benefits of AI in improving patient care, reducing costs, 

and enhancing clinical decision-making. However, it also acknowledges the challenges related to 

bias, data privacy, and the need for more comprehensive economic impact assessments.  

Enhancing healthcare quality through AI is an ongoing process that holds immense promise for 

improving patient outcomes, reducing healthcare costs, and enhancing the overall healthcare 

experience. It is essential for healthcare providers to continue to research, develop, and 

implement AI technologies while ensuring they meet the highest standards of accuracy, privacy, 

and ethical considerations. 

The exponential growth of healthcare data necessitates advanced management strategies. 

AI and ML play a pivotal role in processing and analyzing these massive datasets to extract 

meaningful insights. Predictive modeling, data-driven decision-making, and patient risk 

stratification are some of the areas were AI shines in healthcare data management.  

Machine learning algorithms are redefining clinical practices by enabling early disease 

detection, prognosis prediction, and treatment response monitoring. These algorithms 

continuously learn from new data, evolving their accuracy over time. AI's ability to analyze multi-

modal data, such as medical images and genomics, opens avenues for precise diagnostics and 

treatment personalization. In the Georgian healthcare landscape, where certain diseases are more 

prevalent due to regional factors, AI-driven clinical practices can offer tailored interventions. This 

approach can significantly impact disease management, reduce treatment costs, and improve 

patient outcomes. The implementation of AI and ML in healthcare is not without challenges. 

Ethical concerns, data privacy, algorithm bias, and the need for healthcare professional 

engagement require careful consideration. Striking a balance between human expertise and AI's 

capabilities is essential. In the global context, including Georgia, fostering interdisciplinary 

collaboration between technologists, medical professionals, and policymakers will be crucial in 

harnessing the full potential of AI in healthcare.  

 

Methodology 

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.  



IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE IN DEVELOPING 

COUNTRIES: PERSPECTIVES AND CHALLENGES 

 

114 
 

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 FDGs was assessment of 

attitudes and perceptions of major stakeholders regarding AI implementation and utilization. 

According to the reviewed literature perceptions and attitudes of stakeholders are very important 

for the successful implementation. Nevertheless, this issue is not evaluated sufficiently, especially 

in developing countries. Furthermore, the FGDs were conducted online using Zoom platform, led 

by an experienced moderator using appropriate „Discussion guide“ and supported by a research 

assistant. The assistant was pre-trained in FGD method and specific aspects of transcript 

preparation.  

A total of ten in-depth interviews were conducted, six interviews were face-to-face and 

four by video calls. The average duration of an interview was 50 minutes. Based upon informed 

consent all discussions and in-depth interviews were audio/video recorded and transcripts prepared 

based on them. Transcript data were analyzed using ‘content-analysis’ methodology. Analysis 

started immediately after the FGDs were conducted and in-depth interviews ceased, respectively. 

Consequently, ‘main ideas’ or ‘themes’ were summarized and highlighted using ‘concept map’ 

approach. 

 

Results 

In the specific context of healthcare research in Georgia, a burgeoning landscape is 

emerging as the country embarks on its initial forays into exploring the applications of artificial 

intelligence (AI). This research trajectory represents a pioneering effort, marking Georgia's first 

exploration of the multifaceted possibilities of AI within the healthcare domain. It highlights the 

country's dedication to leveraging technological advancements to enhance its healthcare 

infrastructure and services. Within this research landscape, Georgia seeks to comprehensively 

evaluate the potential and implications of AI technologies in healthcare, a venture that is 

characterized by its novelty and forward-looking orientation. As the country takes these initial 

steps, the aim is to discern how AI can be effectively integrated to not only augment healthcare 

delivery but also to address the unique healthcare challenges that are intrinsic to developing 

nations. This pioneering research signifies a promising journey for Georgia as it seeks to harness 

the transformative power of AI to optimize healthcare practices within its specific national context. 

This exploration is poised to lay the foundation for a deeper understanding of the applications, 

benefits, and potential limitations of AI in healthcare, fostering a rich research landscape within 

the country and contributing to the global discourse on AI in healthcare.  

The utilization of AI in developing nations carries substantial potential to enhance the 

quality of healthcare services and broaden healthcare access. Nevertheless, this endeavor is 

currently grappling with fundamental challenges. These obstacles include a deficiency in public 

health infrastructure, a shortage of adequately trained healthcare professionals, and a prevalent 



Nino MIKAVA, Tamta MAMULAIDZE 

 

115 
 

apprehension of job displacement, as underscored in Joshi et al.'s recent study (2022) (Joshi et al., 

2022). The fear of potential job loss among healthcare professionals engenders skepticism and 

mistrust towards AI and related technologies, thereby impeding their effective integration into 

healthcare practices, as noted in de Abreu et al.'s research (2021), as cited by Joshi et al. (2022).  

These challenges collectively constitute the primary impediments to the successful 

adoption of AI in developing countries. Nevertheless, there is potential for a more harmonious 

alignment of expectations between healthcare professionals and AI technologies. Moreover, 

fostering collaborative co-creation in the design and implementation of AI systems holds promise 

in mitigating these hurdles. This notion is consistent with findings in a systematic literature review 

by Hogg et al. (2023) which addressed AI's role in healthcare (Hogg, 2023). It is important to 

highlight that a notable gap exists in research pertaining to AI in healthcare within the context of 

developing countries, as indicated by Hogg et al.'s review. Further exploration of this subject may 

yield valuable insights to overcome these challenges and enhance the integration of AI in 

healthcare within developing nations.  

Also, Healthcare systems in developing nations frequently grapple with a chronic shortage 

of medical personnel. AI applications hold promise in addressing this persistent workforce deficit. 

The World Health Organization (WHO) acknowledges that the rapid integration of AI technology 

in healthcare comes with a set of notable challenges. One significant concern lies in the potential 

lack of comprehension regarding the intricate workings of AI technologies, often referred to as the 

'black box' problem. This lack of understanding can lead to the risk of patient harm. Additionally, 

AI technologies, especially those reliant on machine learning processes, are susceptible to biases 

inherent in the model itself and the data used for training.  

Furthermore, AI applications in healthcare frequently involve access to sensitive personal 

information. Consequently, there is a pressing need to establish effective measures for 

safeguarding and responsibly managing this data, as well as overseeing the purposes for which AI 

is deployed in the healthcare sector. 

The primary highlights revolve around three substantial challenges in the integration of AI 

in healthcare: a deficiency in transparency, regulatory and governance complexities, and a 

fundamental lack of comprehension among healthcare practitioners and patients. 

Developing countries, including Georgia, often encounter challenges related to the 

geographical distribution of healthcare professionals and the adoption of modern technologies and 

AI in healthcare. According to the opinions of the respondents, in the Georgian context, AI 

empowered digital health platforms have the potential to bridge healthcare disparities between 

urban and rural regions. Like many other developing countries, Georgia’s healthcare system faces 

a challenge of medical professional asymmetry. More specifically, 70% of healthcare professionals 

work in the capital city and only 30% of the medical personnel serve the rest 70% of the country’s 

population in the regions. Telemedicine empowered by AI can connect patients (even in the most 

distant areas of the country) with medical experts, overcoming geographical barriers and 

improving access to specialized care. Moreover, interdisciplinary approach and value-based 

healthcare are the most accentuated aspects of the healthcare systems, that should be met for 



IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE IN DEVELOPING 

COUNTRIES: PERSPECTIVES AND CHALLENGES 

 

116 
 

patient-centered care, better outcomes, and higher quality of treatment. In Georgia, as in most 

developing countries, many regions and rural areas do not have specialists such as 

endocrinologists, ophthalmologists etc. This results in fragmented care and lack of care continuity. 

However, telemedicine provides a platform to offer multidisciplinary team approach to the patients 

living in rural areas. Even more, shortage of specialists in certain areas represents significant 

challenge in Georgia’s healthcare system, as well as in other developing countries. “Brain drain”, 

migration of talents to developed countries leaves developing countries scarce of highly qualified 

specialists. With the application of AI optimization of resources becomes available. For instance, 

in telemedicine settings with AI involvement preliminary diagnosis can be made with analysis of 

various images, scans and recordings of lung and heart sounds. And human intelligence should be 

used only for special cases. This frees time for specialists for more significant cases and decreases 

time for diagnosis for the patients. 

Rather interesting topic emerged in the Focus Group discussions. According to the opinions 

of some respondents, there can be a trend observed in the displaced population – forced to leave 

their places of residence after the wars in August of 2008 and in 1990s. Healthcare specialists from 

the Samegrelo region (where large number of displaced citizens is concentrated) shared findings 

of several charity check-ups conducted in this population. According to their observations, there 

is a trend of increased oncological disease prevalence among these individuals compared to other 

citizens in the same region. One of the suggestions is that increased levels of stress these 

individuals underwent through, could contribute to greater exposure to oncological conditions. 

Nevertheless, this fact needs further in-depth research and implementation of AI can support trend 

identification, disease prediction and prevention. With a diverse population and a wide spectrum 

of health conditions, AI-driven analytics can aid in tailoring treatment approaches to the country's 

unique demographic and epidemiological landscape.  

Another area for AI's potential is enhancement of healthcare quality in the provider sector. 

Government of Georgia envisions to position the country on the global medical tourism market, 

as a destination country. However, to realize this vision one of the central requirements is to offer 

adequate healthcare quality to medical tourists. Currently, Georgia’s provider sector faces many 

challenges regarding healthcare quality, like many other developing country contexts. Therefore, 

respondents accentuated the role of AI as a solution to the number of problems in this regard. To 

illustrate, utilization of AI for the early prognosis of infections in hospitalized patients could 

significantly decrease costs of the treatment of complications and save more lives. Even though 

electronic medical records are not fully implemented in all clinics, this process will be completed 

soon. Georgia's nascent electronic health records system can benefit immensely from AI-driven 

data management solutions. By effectively organizing and analyzing health data, the country can 

identify emerging health trends, improve clinical research, and facilitate evidence-based policy 

formulation. The greater problem, in this scope, is a lack of data analytics. Respondents extended 

this topic further on the national level. According to the representatives of non-governmental 

organizations, working with the ministry of healthcare and involved in various projects, data from 

different registries (birth, oncological etc.) and digital platforms in the country are not processed 



Nino MIKAVA, Tamta MAMULAIDZE 

 

117 
 

and analyzed adequately, thus living serious potential for evidence-based decisions unrealized. 

Consequently, implementation of AI is sought as a solution to this problem on a provider, as well 

as on a state level.  

Another area of concern in Georgia’s healthcare system is polypharmacy and excessive 

instrumental or laboratory investigation – “overmedicalization over-investigation”. According to 

the respondents this problem also could be solved, and patients could benefit from AI utilization. 

To cite one of the respondents – “currently, the ministry of healthcare cannot control this problem 

as more than 90% of clinics are private, for-profit. The only way to correct this problem is to 

leverage control and regulatory capacity. And this can be achieved only by creation of evidence 

pool and data analytics. To identify trends in medicine prescription and “treatment behavior””. 

Moreover, through the identification of mentioned trends and data analytics with AI total 

healthcare expenditures can be decreased and quality of treatment enhanced. Still another direction 

in healthcare quality for the prevention of infections and complications is hand hygiene. 

Respondents weren’t familiar with AI empowered solutions for hand hygiene compliance 

(mentioned above, in the literature review findings). During interviews and FGDs this information 

was shared with them by researchers. Most of the respondents found this technology very useful 

for Georgia’s context.  

One of the significant challenges in Georgia’s healthcare system is a lack of therapeutic 

education and self-management among chronic patients and parents of the children with chronic 

diseases. Among the respondents were two experts with several years’ experience in diabetes 

research. To illustrate their opinion regarding AI’s role in patient education – “we conducted 

several studies to assess the needs and problems of the patients with diabetes, adults, and children. 

One of the biggest problems in this regard is lack of education among parents of children having 

diabetes and adult diabetic patients. Diabetes is a disease which heavily depends on self-

management and behavior change. Lack of knowledge hinders serious behavior change and 

outcomes are much worse. If we could use AI, to increase access to the newest information for 

them results could be much better”. According to the discussion, with utilization of generative AI 

tools access to up-to-date information can be increased and digital libraries (video library as well) 

can be created. This concerns other chronic conditions as well, where modification of risks is 

possible with self-management and behavior change. Once personalized health content generation 

becomes possible patients can easily get information tailored to their specific condition. To 

continue the topic of chronic diseases, which represents a significant burden for the country’s 

budget, introduction of AI empowered chronic disease assistance platforms can also improve 

health condition of patients and quality of life. To illustrate, one of such digital applications, on a 

global market, is Dario, where AI can assess/recognize mental and physical state of the patient 

based on voice recognition and text comprehension technologies, directs patient accordingly to the 

relevant specialist and offers additional assistance. This kind of applications customized to 

Georgian patients can decrease healthcare expenditures on top of the benefits mentioned above. 

Among the participants of FGDs were two professionals working on mental wellness issues (one 

psychiatrist and another- representative of NGO working on mental health). During discussions, 



IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE IN DEVELOPING 

COUNTRIES: PERSPECTIVES AND CHALLENGES 

 

118 
 

the significance of deteriorating mental health of the population was discussed and alarming rate 

of emerging problems such as anxiety disorders and panic attacks were mentioned. It should be 

noted that prevalence of mental problems dramatically increased after COVID-19 and after the 

war in Ukraine. As Georgian population underwent two wars in 1990s and in 2008, war in Ukraine 

caused significant re-traumatization among citizens. Official statistics do not illustrate the real 

situation in this regard, as due to stigma and cultural issues the number of individuals suffering 

mental problems is much greater. According to the respondents, implementation of solutions, such 

as AI coupled with Virtual Reality Goggles and Biofeedback devices can bring substantial results 

for the treatment of patients with different phobias, post-traumatic-stress disorders (PTSD) and 

anxieties. It should be noted that neurofeedback devices empowered with AI diagnostics is newly 

introduced on Georgia’s market and has attracted attention of patients and specialists in very short 

period of time. It should be outlined that none of the respondents of interviews and FGDs 

mentioned robotic surgeries among listed significant AI implications in healthcare. 

In developing countries, the adoption of AI in healthcare is often driven by the need to 

address resource constraints, access issues, and the delivery of cost-effective care. The specific 

technologies used may vary, but the common goal is to leverage AI to improve healthcare access, 

quality, and efficiency. AI can help these countries maximize their limited healthcare resources 

and enhance the overall health outcomes of their populations. It's worth noting that while these 

technologies offer significant promise, their successful implementation in developing countries 

may also face challenges related to infrastructure, affordability, and regulatory frameworks. 

Nevertheless, the potential benefits in terms of improving healthcare access and quality make AI 

an important focus for healthcare development in these regions. 

However, despite all the benefits and potential implications of AI in healthcare that were 

discussed during the study, challenges and skepticism among the respondents should also be 

mentioned. During the FGDs, healthcare specialists expressed concerns regarding replacement of 

doctors with AI and machine learning. Moreover, skepticism and fear of change were evident in 

the discussion process and in the concerns that were expressed. It is well known and researched 

how this kind of attitudes and resistance to change, as well as fears hinder innovations, especially 

in regard with implementation of new technologies. Another challenge named by the respondents 

was lack of flexibility and coordination of efforts on a system’s level in the country. More 

specifically, many processes in scope of digitalization of healthcare are hindered by a lack of 

collaboration among different ministries and state units. Still another challenge named in FGDs 

and interviews is deficiency of specific and clear vision of government concerning digitalization 

of healthcare and AI. Moreover, absence of the relevant technological infrastructure, weak 

regulatory system and loose monitoring from the government’s side were named as substantial 

hindering barriers for effective implementation and utilization of AI in healthcare.  

To summarize, despite these challenges, there is growing recognition of the potential 

benefits of AI in healthcare for developing countries, including improving healthcare access, 

quality, and efficiency. Efforts are underway to overcome these obstacles through international 

partnerships, capacity-building initiatives, and the development of AI solutions tailored to the 



Nino MIKAVA, Tamta MAMULAIDZE 

 

119 
 

specific needs and resources of these regions. For the effective realization of AI’s potential in 

healthcare the following requirements should be met – clear and specific vision and strategy should 

be developed by the government. Furthermore, Government in a flagmenship role should 

coordinate collaboration and integration of efforts among various ministries and institutions in 

order to develop technological infrastructure and framework in the country. Provider sector and 

all the stakeholders should be involved from the very initial stage of AI implementation - vision 

and strategy development etc. Even more, encouragement and stimulation of the provider sector 

should be thought to enhance AI adoption by the private clinics and hospitals. As for the provider 

sector, management of clinics need to consider and utilize change management principles for the 

effective transformation. To illustrate, so called “champions” – specialists supporting and 

enthusiastic about AI implementation should be engaged actively in the groundwork processes to 

change attitudes and perceptions of the personnel in favor of technological progress. 

 

CONCLUSIONS  

AI and machine learning have brought about transformative changes in healthcare quality, 

data management, and clinical practices on a global scale. In Georgia, these advancements hold 

immense promise in overcoming healthcare disparities and optimizing healthcare delivery. As AI 

continues to evolve, a harmonious integration with human expertise will pave the way for a future 

where healthcare is not only technologically advanced but also empathetic and patient centric. 

 

REFERENCES 

1. Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2020). The Economic Impact of Artificial 

Intelligence in Health Care: Systematic Review. Journal of medical Internet 

research, 22(2), e16866. https://doi.org/10.2196/16866 

2. Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., 

Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., Singh, I. M., Laird, J. 

R., Fatemi, M., Alizad, A., Saba, L., Agarwal, V., Sharma, A., Teji, J. S., Al-Maini, M., 

Rathore, V. Suri, J. S. (2022). Economics of Artificial Intelligence in Healthcare: Diagnosis 

vs. Treatment. Healthcare (Basel, Switzerland), 10(12), 2493. 

https://doi.org/10.3390/healthcare10122493 

3. Mathias J. (2023). Use of AI to Predict Risk of HAIs. 

https://www.ormanager.com/briefs/use-of-ai-to-predict-risk-of-

hais/#:~:text=This%20study%20led%20by%20researchers,and%20features%20of%20si

milar%20patients. 

4. Shah, Rushabh, and Alina Chircu (2018). "IoT and AI in healthcare: A systematic literature 

review." Issues in Information Systems 19, no. 3 (2018). 

5. Rizwan, P., M., R. B., & K., S. (2017). Design and development of low investment smart 

hospital using internet of things through innovative approaches. Biomedical Research 

(0970-938X), 28(11), 4979-4985. 

https://doi.org/10.2196/16866
https://doi.org/10.3390/healthcare10122493
https://www.ormanager.com/briefs/use-of-ai-to-predict-risk-of-hais/#:~:text=This%20study%20led%20by%20researchers,and%20features%20of%20similar%20patients
https://www.ormanager.com/briefs/use-of-ai-to-predict-risk-of-hais/#:~:text=This%20study%20led%20by%20researchers,and%20features%20of%20similar%20patients
https://www.ormanager.com/briefs/use-of-ai-to-predict-risk-of-hais/#:~:text=This%20study%20led%20by%20researchers,and%20features%20of%20similar%20patients


IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE IN DEVELOPING 

COUNTRIES: PERSPECTIVES AND CHALLENGES 

 

120 
 

6. How hospitals are reducing infections by over 60% (2019). The power of the voice to 

finally solve hand hygiene. http://cleanhands-safehands.com/wp-

content/uploads/2019/01/HAI-Reduction-White-Paper.pdf 

7. Fitzpatrick, F., Doherty, A., & Lacey, G. (2020). Using Artificial Intelligence in Infection 

Prevention. Current treatment options in infectious diseases, 12(2), 135–144. 

https://doi.org/10.1007/s40506-020-00216-7 

8. Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial 

intelligence. Nat Med 25, 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7 

9. Markets and Markets, Market research report (2023). Artificial Intelligence in Healthcare 

Market by Offering (Hardware, Software services), Technology (Machine learning, NLP, 

Context-aware computing, Computer vision), Application, End-user and Region- Global 

Forcast to 2028. (https://www.marketsandmarkets.com/Market-Reports/artificial-

intelligence-healthcare-market-54679303.html 

10. Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. 

(2020). A Review of Challenges and Opportunities in Machine Learning for Health. AMIA 

Joint Summits on Translational Science proceedings. AMIA Joint Summits on 

Translational Science, 2020, 191–200. 

11. Joshi, Geeta, Aditi Jain, Sabina Adhikari, Harshit Garg, and Mukund Bhandari (2022). 

"FDA approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical 

Devices: An updated 2022 landscape." medRxiv (2022): 2022-12. 

12. Hogg H. D. J et al. (2023). Evaluating the translation of implementation science to clinical 

artificial intelligence: a bibliometric study of qualitative research. 

https://www.frontiersin.org/articles/10.3389/frhs.2023.1161822 

http://cleanhands-safehands.com/wp-content/uploads/2019/01/HAI-Reduction-White-Paper.pdf
http://cleanhands-safehands.com/wp-content/uploads/2019/01/HAI-Reduction-White-Paper.pdf
https://doi.org/10.1007/s40506-020-00216-7
https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html
https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html

