Health care of Kyrgyzstan
Zdravoohraneniye Kyrgyzstana

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SWOT analysis of Artificial Intelligence Implementation in Primary Health Care

https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122
SWOT analysis of Artificial Intelligence Implementation in Primary Health Care
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Abstract About the authors References

Abstract

Introduction. In the context of a global shortage of health workers and increasing demands on the quality of medical services, the introduction of artificial intelligence technologies in primary health care is of strategic importance. The purpose of this study is to conduct a SWOT analysis of the prospects for the introduction of artificial intelligence in primary health care in Kyrgyzstan to develop recommendations for its effective implementation. Materials and methods. The study is based on a systematic analysis of international publications, regulations and reports of international organizations using the principles of SWOT analysis and strategic planning. The literature review for the period 2015-2023 included publications from PubMed, Web of Science and other databases with an emphasis on clinical trials and meta-analyses. Results and discussion. The SWOT analysis revealed strengths, such as government digitalization policy and a developed primary health care network, as well as weaknesses, including limited digital infrastructure and staff shortages. Opportunities include international cooperation and the development of epidemiological surveillance, while threats are associated with cybersecurity risks and insufficient funding. Conclusion. The results show that the implementation of artificial intelligence in primary health care in Kyrgyzstan can improve the quality and accessibility of health services. However, existing limitations such as staff shortages and inadequate regulatory framework need to be overcome. Successful integration of artificial intelligence in primary health care requires a comprehensive government approach, including full funding, development of a regulatory framework, digital infrastructure, and staff training. Further research should focus on assessing the effectiveness of artificial intelligence in real clinical practice.

About the authors

Садырбеков Кубатбек Каныбекович, кандидат медицинских наук, и.о. доцента, заведующий кафедрой «Общественное здравоохранение» Международной школы медицины, Международного университета Кыргызстана, Бишкек, Кыргызская Республика

Дуйшенов Дамир Арыпбекович, ректор Университета Южной Азии, Бишкек, Кыргызская Республика

Садырбекова Алтынай Кубатбековна, студент факультета глобального законодательства Университета Турина, Турин, Италия

Садырбеков Анвар Кубатбекович, студент лечебного факультета Кыргызской государственной медицинской академии им. И.К. Ахунбаева, Бишкек, Кыргызская Республика

Sadyrbekov Kubatbek Kanybekovich, Candidate of Medical Sciences, Acting Associate Professor, Head of the Department of Public Health, International School of Medicine, International University of Kyrgyzstan, Bishkek, Kyrgyz Republic

Duishenov Damir Arypbekovich, Rector of the University of South Asia, Bishkek, Kyrgyz Republic

Sadyrbekova Altynay Kubatbekovna, student of the Faculty of Global Legislation of the University of Turin, Turin, Italy

Sadyrbekov Anvar Kubatbekovich, student of the medical faculty of the Kyrgyz State Medical Academy named after I.K. Akhunbaev, Bishkek, Kyrgyz Republic

Садырбеков Кубатбек Каныбекович, медицина илимдеринин кандидаты, доценттин милдетин аткаруучу, Кыргызстан Эл аралык университетинин Эл аралык медицина мектебинин коомдук саламаттыкты сактоо кафедрасынын башчысы, Бишкек, Кыргыз Республикасы

Дүйшөнов Дамир Арыпбекович, Түштүк Азия университетинин ректору, Бишкек, Кыргыз Республикасы

Садырбекова Алтынай Кубатбековна, Турин университетинин глобалдык мыйзамдуулук факультетинин студенти, Турин, Италия

Садырбеков Анвар Кубатбекович, И.К. атындагы Кыргыз мамлекеттик медициналык академиясынын дарылоо факультетинин студенти. Ахунбаев, Бишкек, Кыргыз Республикасы

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gh-generative-ai
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mental health burden from COVID-19. Telemed e-Health 26(4):377–379. doi.org/10.1089/tmj.2020.0068
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Int J Environ Res Public Health 16(14):2533. doi.org/10.3390/ijerph16142533
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doi.org/10.1001/jama.2019.15064
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present and future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
25. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273.
doi.org/10.1016/S1470-2045(19)30149-4
26. McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care ma
chine learning. Lancet Digit Health 2(5):e221–e223. doi.org/10.1016/S2589-7500(20)30065-0
27. Bresnick J (2018) Cost analysis of AI implementation in primary care. HealthITAnalytics. Available at: https://healthita nalytics.
com
28. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digit Med 1(1):5. doi.org/10.1038/s41746-017-
0012-2
29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2019) Deep learning for health informatics. IEEE
J Biomed Health Inform 21(1):4–21. doi.org/10.1109/JBHI.2016.2636665
30. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med
375(13):1216–1219. doi.org/10.1056/NEJMp1606181
31. Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. J Family Med Prim Care
8(7):2328–2332. doi.org/10.4103/jfmpc.jfmpc_440_19

1. Sadyrbekov KK (2024) PHC as a key component of the Global Public Health architecture. BIO Web Conf: XIII Int Sci Pract
Conf “Medico-biological and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001014
2. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Wang Y (2017) Artificial intelligence in healthcare: past, present, and
future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
3. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
4. Chen JH, Asch SM (2020) Machine learning and prediction in medicine — beyond the peak of inflated expectations. N Engl J
Med 376(26):2507–2509. doi.org/10.1056/NEJMp1702071
5. Sadyrbekov KK (2024) Strategic management of public health. In: BIO Web Conf: XIII Int Sci Pract Conf “Medico-biological
and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001013
6. World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. World Health Or
ganization. Available at: https://www.who.int. Accessed: 28.06.2021
7. Ministry of Health of the Kyrgyz Republic (2017) Order №23 dated 01/12/2017 “On the establishment of the institution of the
Electronic Health Center under the Ministry of Health of the Kyrgyz Republic”. Bishkek
8. President of the Kyrgyz Republic (2024) The concept of digital transformation of the Kyrgyz Republic for 2024–2028. Decree
№90, Bishkek.
9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer
with deep neural networks. Nature 542(7639):115–118. doi.org/10.1038/nature21056
10. Wang G, Liu X, Shen J et al (2021) A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-
19 pneumonia from chest X-ray images. Nat Biomed Eng. 5:509–521. doi.org/10.1038/s41551-021-00704-1
11. Liu X et al (2021) A comparison of deep learning performance against healthcare professionals. Lancet Digit Health 3(4):e215–
e224
12. Ardila D, Kiraly AP, Bharadwaj S et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-
dose chest computed tomography. Nat Med 25(6):954–961. doi.org/10.1038/s41591-019-0447-x
13. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. doi.org/10.1001/jama.2016.17216
14. Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep
learning on whole slide images. Nat Med 25(8):1301–1309. doi.org/10.1038/s41591-019-0508-1
15. Somashekhar SP, Sepúlveda MJ, Puglielli S et al (2017) Early experience with IBM Watson for Oncology (WFO) in breast
cancer treatment. Ann Oncol 28(Suppl 5):v605–v649. doi.org/10.1093/annonc/mdx440
16. Accenture (2023) Accenture invests $150 million in AI to help clients reinvent business through generative AI [Press release].
Available at: https://newsroom.accenture.com/news/accenture-invests-150-million-in-ai-to-help-clients-reinvent-business-throu
gh-generative-ai
17. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in
medicine. Nat Med 25(1):30–36. doi.org/10.1038/s41591-018-0307-0
18. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
19. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2021) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol. 6(1). doi.org/10.1136/svn-2020-000483

20. Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, Smith AC (2020) The role of telehealth in reducing the
mental health burden from COVID-19. Telemed e-Health 26(4):377–379. doi.org/10.1089/tmj.2020.0068
21. Rajkomar A, Dean J, Kohane I (2018) Machine learning in medicine. N Engl J Med 378(14):1347–1358. doi.org/10.1056/NE
JMra1814259
22. Oviedo S, Vehi J, Calm R, Armengol J (2019) A review of personalized blood glucose prediction strategies for T1DM patients.
Int J Environ Res Public Health 16(14):2533. doi.org/10.3390/ijerph16142533
23. Price WN, Gerke S, Cohen IG (2019) Potential liability for physicians using artificial intelligence. JAMA 322(18):1765–1766.
doi.org/10.1001/jama.2019.15064
24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
25. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273.
doi.org/10.1016/S1470-2045(19)30149-4
26. McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care ma
chine learning. Lancet Digit Health 2(5):e221–e223. doi.org/10.1016/S2589-7500(20)30065-0
27. Bresnick J (2018) Cost analysis of AI implementation in primary care. HealthITAnalytics. Available at: https://healthita nalytics.
com
28. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digit Med 1(1):5. doi.org/10.1038/s41746-017-
0012-2
29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2019) Deep learning for health informatics. IEEE
J Biomed Health Inform 21(1):4–21. doi.org/10.1109/JBHI.2016.2636665
30. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med
375(13):1216–1219. doi.org/10.1056/NEJMp1606181
31. Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. J Family Med Prim Care
8(7):2328–2332. doi.org/10.4103/jfmpc.jfmpc_440_19

1. Sadyrbekov KK (2024) PHC as a key component of the Global Public Health architecture. BIO Web Conf: XIII Int Sci Pract
Conf “Medico-biological and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001014
2. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Wang Y (2017) Artificial intelligence in healthcare: past, present, and
future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
3. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
4. Chen JH, Asch SM (2020) Machine learning and prediction in medicine — beyond the peak of inflated expectations. N Engl J
Med 376(26):2507–2509. doi.org/10.1056/NEJMp1702071
5. Sadyrbekov KK (2024) Strategic management of public health. In: BIO Web Conf: XIII Int Sci Pract Conf “Medico-biological
and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001013
6. World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. World Health Or
ganization. Available at: https://www.who.int. Accessed: 28.06.2021
7. Ministry of Health of the Kyrgyz Republic (2017) Order №23 dated 01/12/2017 “On the establishment of the institution of the
Electronic Health Center under the Ministry of Health of the Kyrgyz Republic”. Bishkek
8. President of the Kyrgyz Republic (2024) The concept of digital transformation of the Kyrgyz Republic for 2024–2028. Decree
№90, Bishkek.
9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer
with deep neural networks. Nature 542(7639):115–118. doi.org/10.1038/nature21056
10. Wang G, Liu X, Shen J et al (2021) A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-
19 pneumonia from chest X-ray images. Nat Biomed Eng. 5:509–521. doi.org/10.1038/s41551-021-00704-1
11. Liu X et al (2021) A comparison of deep learning performance against healthcare professionals. Lancet Digit Health 3(4):e215–
e224
12. Ardila D, Kiraly AP, Bharadwaj S et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-
dose chest computed tomography. Nat Med 25(6):954–961. doi.org/10.1038/s41591-019-0447-x
13. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. doi.org/10.1001/jama.2016.17216
14. Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep
learning on whole slide images. Nat Med 25(8):1301–1309. doi.org/10.1038/s41591-019-0508-1
15. Somashekhar SP, Sepúlveda MJ, Puglielli S et al (2017) Early experience with IBM Watson for Oncology (WFO) in breast
cancer treatment. Ann Oncol 28(Suppl 5):v605–v649. doi.org/10.1093/annonc/mdx440
16. Accenture (2023) Accenture invests $150 million in AI to help clients reinvent business through generative AI [Press release].
Available at: https://newsroom.accenture.com/news/accenture-invests-150-million-in-ai-to-help-clients-reinvent-business-throu
gh-generative-ai
17. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in
medicine. Nat Med 25(1):30–36. doi.org/10.1038/s41591-018-0307-0
18. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
19. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2021) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol. 6(1). doi.org/10.1136/svn-2020-000483

20. Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, Smith AC (2020) The role of telehealth in reducing the
mental health burden from COVID-19. Telemed e-Health 26(4):377–379. doi.org/10.1089/tmj.2020.0068
21. Rajkomar A, Dean J, Kohane I (2018) Machine learning in medicine. N Engl J Med 378(14):1347–1358. doi.org/10.1056/NE
JMra1814259
22. Oviedo S, Vehi J, Calm R, Armengol J (2019) A review of personalized blood glucose prediction strategies for T1DM patients.
Int J Environ Res Public Health 16(14):2533. doi.org/10.3390/ijerph16142533
23. Price WN, Gerke S, Cohen IG (2019) Potential liability for physicians using artificial intelligence. JAMA 322(18):1765–1766.
doi.org/10.1001/jama.2019.15064
24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
25. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273.
doi.org/10.1016/S1470-2045(19)30149-4
26. McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care ma
chine learning. Lancet Digit Health 2(5):e221–e223. doi.org/10.1016/S2589-7500(20)30065-0
27. Bresnick J (2018) Cost analysis of AI implementation in primary care. HealthITAnalytics. Available at: https://healthita nalytics.
com
28. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digit Med 1(1):5. doi.org/10.1038/s41746-017-
0012-2
29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2019) Deep learning for health informatics. IEEE
J Biomed Health Inform 21(1):4–21. doi.org/10.1109/JBHI.2016.2636665
30. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med
375(13):1216–1219. doi.org/10.1056/NEJMp1606181
31. Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. J Family Med Prim Care
8(7):2328–2332. doi.org/10.4103/jfmpc.jfmpc_440_19

Keywords
Primary health care , Artificial intelligence , E- Health , Machine Learning , Telemedicine , SWOT Analysis , Clinical Decision Support Systems
Для цитирования

Садырбеков К.К., Дуйшенов Д.А., Садырбекова А.К., Садырбеков А.К. SWOT-анализ внедрения искусственного интеллекта на уровне первичной медико-санитарной помощи.Научно-практический журнал «Здравоохранение Кыргызстана» 2025, № 2, с. 112-122. https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122 
For citation

Sadyrbekov K.K., Duishenov D.A., Sadyrbekova A.K., Sadyrbekov A.K. SWOT analysis of Artificial Intelligence Imp
lementation in Primary Health Care.Scientific practical journal “Health care of Kyrgyzstan” 2025, No.2, p. 112-122.  https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122
Цитата үчүн

Садырбеков К.К., Дуйшенов Д.А., Садырбекова А.К., Садырбеков А.К. Алгачкы медициналык жардамда жасалма интеллектти ишке ашыруунун SWOT анализи. Кыргызстандын саламаттык сактоо илимий-практикалык журналы
2025, № 2, б. 112-122. https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122 
Authors Sadyrbekov K.K., Duishenov D.A., Sadyrbekova A.K., Sadyrbekov A.K.
Link doi.org https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122
Pages 112-122
Keywords Primary health care , Artificial intelligence , E- Health, Machine Learning, Telemedicine , SWOT Analysis, Clinical Decision Support Systems
Russian
Об авторах

Садырбеков Кубатбек Каныбекович, кандидат медицинских наук, и.о. доцента, заведующий кафедрой «Общественное здравоохранение» Международной школы медицины, Международного университета Кыргызстана, Бишкек, Кыргызская Республика

Дуйшенов Дамир Арыпбекович, ректор Университета Южной Азии, Бишкек, Кыргызская Республика

Садырбекова Алтынай Кубатбековна, студент факультета глобального законодательства Университета Турина, Турин, Италия

Садырбеков Анвар Кубатбекович, студент лечебного факультета Кыргызской государственной медицинской академии им. И.К. Ахунбаева, Бишкек, Кыргызская Республика
Полный текст

PDF (RUS)
Список литературы

1. Sadyrbekov KK (2024) PHC as a key component of the Global Public Health architecture. BIO Web Conf: XIII Int Sci Pract
Conf “Medico-biological and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001014
2. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Wang Y (2017) Artificial intelligence in healthcare: past, present, and
future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
3. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
4. Chen JH, Asch SM (2020) Machine learning and prediction in medicine — beyond the peak of inflated expectations. N Engl J
Med 376(26):2507–2509. doi.org/10.1056/NEJMp1702071
5. Sadyrbekov KK (2024) Strategic management of public health. In: BIO Web Conf: XIII Int Sci Pract Conf “Medico-biological
and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001013
6. World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. World Health Or
ganization. Available at: https://www.who.int. Accessed: 28.06.2021
7. Ministry of Health of the Kyrgyz Republic (2017) Order №23 dated 01/12/2017 “On the establishment of the institution of the
Electronic Health Center under the Ministry of Health of the Kyrgyz Republic”. Bishkek
8. President of the Kyrgyz Republic (2024) The concept of digital transformation of the Kyrgyz Republic for 2024–2028. Decree
№90, Bishkek.
9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer
with deep neural networks. Nature 542(7639):115–118. doi.org/10.1038/nature21056
10. Wang G, Liu X, Shen J et al (2021) A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-
19 pneumonia from chest X-ray images. Nat Biomed Eng. 5:509–521. doi.org/10.1038/s41551-021-00704-1
11. Liu X et al (2021) A comparison of deep learning performance against healthcare professionals. Lancet Digit Health 3(4):e215–
e224
12. Ardila D, Kiraly AP, Bharadwaj S et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-
dose chest computed tomography. Nat Med 25(6):954–961. doi.org/10.1038/s41591-019-0447-x
13. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. doi.org/10.1001/jama.2016.17216
14. Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep
learning on whole slide images. Nat Med 25(8):1301–1309. doi.org/10.1038/s41591-019-0508-1
15. Somashekhar SP, Sepúlveda MJ, Puglielli S et al (2017) Early experience with IBM Watson for Oncology (WFO) in breast
cancer treatment. Ann Oncol 28(Suppl 5):v605–v649. doi.org/10.1093/annonc/mdx440
16. Accenture (2023) Accenture invests $150 million in AI to help clients reinvent business through generative AI [Press release].
Available at: https://newsroom.accenture.com/news/accenture-invests-150-million-in-ai-to-help-clients-reinvent-business-throu
gh-generative-ai
17. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in
medicine. Nat Med 25(1):30–36. doi.org/10.1038/s41591-018-0307-0
18. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
19. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2021) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol. 6(1). doi.org/10.1136/svn-2020-000483

20. Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, Smith AC (2020) The role of telehealth in reducing the
mental health burden from COVID-19. Telemed e-Health 26(4):377–379. doi.org/10.1089/tmj.2020.0068
21. Rajkomar A, Dean J, Kohane I (2018) Machine learning in medicine. N Engl J Med 378(14):1347–1358. doi.org/10.1056/NE
JMra1814259
22. Oviedo S, Vehi J, Calm R, Armengol J (2019) A review of personalized blood glucose prediction strategies for T1DM patients.
Int J Environ Res Public Health 16(14):2533. doi.org/10.3390/ijerph16142533
23. Price WN, Gerke S, Cohen IG (2019) Potential liability for physicians using artificial intelligence. JAMA 322(18):1765–1766.
doi.org/10.1001/jama.2019.15064
24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
25. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273.
doi.org/10.1016/S1470-2045(19)30149-4
26. McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care ma
chine learning. Lancet Digit Health 2(5):e221–e223. doi.org/10.1016/S2589-7500(20)30065-0
27. Bresnick J (2018) Cost analysis of AI implementation in primary care. HealthITAnalytics. Available at: https://healthita nalytics.
com
28. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digit Med 1(1):5. doi.org/10.1038/s41746-017-
0012-2
29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2019) Deep learning for health informatics. IEEE
J Biomed Health Inform 21(1):4–21. doi.org/10.1109/JBHI.2016.2636665
30. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med
375(13):1216–1219. doi.org/10.1056/NEJMp1606181
31. Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. J Family Med Prim Care
8(7):2328–2332. doi.org/10.4103/jfmpc.jfmpc_440_19
Для цитирования

Садырбеков К.К., Дуйшенов Д.А., Садырбекова А.К., Садырбеков А.К. SWOT-анализ внедрения искусственного интеллекта на уровне первичной медико-санитарной помощи.Научно-практический журнал «Здравоохранение Кыргызстана» 2025, № 2, с. 112-122. https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122 
English
About authors

Sadyrbekov Kubatbek Kanybekovich, Candidate of Medical Sciences, Acting Associate Professor, Head of the Department of Public Health, International School of Medicine, International University of Kyrgyzstan, Bishkek, Kyrgyz Republic

Duishenov Damir Arypbekovich, Rector of the University of South Asia, Bishkek, Kyrgyz Republic

Sadyrbekova Altynay Kubatbekovna, student of the Faculty of Global Legislation of the University of Turin, Turin, Italy

Sadyrbekov Anvar Kubatbekovich, student of the medical faculty of the Kyrgyz State Medical Academy named after I.K. Akhunbaev, Bishkek, Kyrgyz Republic
Full text

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References

1. Sadyrbekov KK (2024) PHC as a key component of the Global Public Health architecture. BIO Web Conf: XIII Int Sci Pract
Conf “Medico-biological and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001014
2. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Wang Y (2017) Artificial intelligence in healthcare: past, present, and
future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
3. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
4. Chen JH, Asch SM (2020) Machine learning and prediction in medicine — beyond the peak of inflated expectations. N Engl J
Med 376(26):2507–2509. doi.org/10.1056/NEJMp1702071
5. Sadyrbekov KK (2024) Strategic management of public health. In: BIO Web Conf: XIII Int Sci Pract Conf “Medico-biological
and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001013
6. World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. World Health Or
ganization. Available at: https://www.who.int. Accessed: 28.06.2021
7. Ministry of Health of the Kyrgyz Republic (2017) Order №23 dated 01/12/2017 “On the establishment of the institution of the
Electronic Health Center under the Ministry of Health of the Kyrgyz Republic”. Bishkek
8. President of the Kyrgyz Republic (2024) The concept of digital transformation of the Kyrgyz Republic for 2024–2028. Decree
№90, Bishkek.
9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer
with deep neural networks. Nature 542(7639):115–118. doi.org/10.1038/nature21056
10. Wang G, Liu X, Shen J et al (2021) A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-
19 pneumonia from chest X-ray images. Nat Biomed Eng. 5:509–521. doi.org/10.1038/s41551-021-00704-1
11. Liu X et al (2021) A comparison of deep learning performance against healthcare professionals. Lancet Digit Health 3(4):e215–
e224
12. Ardila D, Kiraly AP, Bharadwaj S et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-
dose chest computed tomography. Nat Med 25(6):954–961. doi.org/10.1038/s41591-019-0447-x
13. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. doi.org/10.1001/jama.2016.17216
14. Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep
learning on whole slide images. Nat Med 25(8):1301–1309. doi.org/10.1038/s41591-019-0508-1
15. Somashekhar SP, Sepúlveda MJ, Puglielli S et al (2017) Early experience with IBM Watson for Oncology (WFO) in breast
cancer treatment. Ann Oncol 28(Suppl 5):v605–v649. doi.org/10.1093/annonc/mdx440
16. Accenture (2023) Accenture invests $150 million in AI to help clients reinvent business through generative AI [Press release].
Available at: https://newsroom.accenture.com/news/accenture-invests-150-million-in-ai-to-help-clients-reinvent-business-throu
gh-generative-ai
17. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in
medicine. Nat Med 25(1):30–36. doi.org/10.1038/s41591-018-0307-0
18. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
19. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2021) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol. 6(1). doi.org/10.1136/svn-2020-000483

20. Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, Smith AC (2020) The role of telehealth in reducing the
mental health burden from COVID-19. Telemed e-Health 26(4):377–379. doi.org/10.1089/tmj.2020.0068
21. Rajkomar A, Dean J, Kohane I (2018) Machine learning in medicine. N Engl J Med 378(14):1347–1358. doi.org/10.1056/NE
JMra1814259
22. Oviedo S, Vehi J, Calm R, Armengol J (2019) A review of personalized blood glucose prediction strategies for T1DM patients.
Int J Environ Res Public Health 16(14):2533. doi.org/10.3390/ijerph16142533
23. Price WN, Gerke S, Cohen IG (2019) Potential liability for physicians using artificial intelligence. JAMA 322(18):1765–1766.
doi.org/10.1001/jama.2019.15064
24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
25. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273.
doi.org/10.1016/S1470-2045(19)30149-4
26. McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care ma
chine learning. Lancet Digit Health 2(5):e221–e223. doi.org/10.1016/S2589-7500(20)30065-0
27. Bresnick J (2018) Cost analysis of AI implementation in primary care. HealthITAnalytics. Available at: https://healthita nalytics.
com
28. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digit Med 1(1):5. doi.org/10.1038/s41746-017-
0012-2
29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2019) Deep learning for health informatics. IEEE
J Biomed Health Inform 21(1):4–21. doi.org/10.1109/JBHI.2016.2636665
30. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med
375(13):1216–1219. doi.org/10.1056/NEJMp1606181
31. Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. J Family Med Prim Care
8(7):2328–2332. doi.org/10.4103/jfmpc.jfmpc_440_19
For citation

Sadyrbekov K.K., Duishenov D.A., Sadyrbekova A.K., Sadyrbekov A.K. SWOT analysis of Artificial Intelligence Imp
lementation in Primary Health Care.Scientific practical journal “Health care of Kyrgyzstan” 2025, No.2, p. 112-122.  https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122
Kyrgyz
Авторлор жөнүндө

Садырбеков Кубатбек Каныбекович, медицина илимдеринин кандидаты, доценттин милдетин аткаруучу, Кыргызстан Эл аралык университетинин Эл аралык медицина мектебинин коомдук саламаттыкты сактоо кафедрасынын башчысы, Бишкек, Кыргыз Республикасы

Дүйшөнов Дамир Арыпбекович, Түштүк Азия университетинин ректору, Бишкек, Кыргыз Республикасы

Садырбекова Алтынай Кубатбековна, Турин университетинин глобалдык мыйзамдуулук факультетинин студенти, Турин, Италия

Садырбеков Анвар Кубатбекович, И.К. атындагы Кыргыз мамлекеттик медициналык академиясынын дарылоо факультетинин студенти. Ахунбаев, Бишкек, Кыргыз Республикасы
Шилтемелер

1. Sadyrbekov KK (2024) PHC as a key component of the Global Public Health architecture. BIO Web Conf: XIII Int Sci Pract
Conf “Medico-biological and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001014
2. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Wang Y (2017) Artificial intelligence in healthcare: past, present, and
future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
3. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
4. Chen JH, Asch SM (2020) Machine learning and prediction in medicine — beyond the peak of inflated expectations. N Engl J
Med 376(26):2507–2509. doi.org/10.1056/NEJMp1702071
5. Sadyrbekov KK (2024) Strategic management of public health. In: BIO Web Conf: XIII Int Sci Pract Conf “Medico-biological
and Pedagogical Foundations of Adaptation, Sports Activities and a Healthy Lifestyle” (MBFA 2024).
doi.org/10.1051/bioconf/202412001013
6. World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. World Health Or
ganization. Available at: https://www.who.int. Accessed: 28.06.2021
7. Ministry of Health of the Kyrgyz Republic (2017) Order №23 dated 01/12/2017 “On the establishment of the institution of the
Electronic Health Center under the Ministry of Health of the Kyrgyz Republic”. Bishkek
8. President of the Kyrgyz Republic (2024) The concept of digital transformation of the Kyrgyz Republic for 2024–2028. Decree
№90, Bishkek.
9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer
with deep neural networks. Nature 542(7639):115–118. doi.org/10.1038/nature21056
10. Wang G, Liu X, Shen J et al (2021) A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-
19 pneumonia from chest X-ray images. Nat Biomed Eng. 5:509–521. doi.org/10.1038/s41551-021-00704-1
11. Liu X et al (2021) A comparison of deep learning performance against healthcare professionals. Lancet Digit Health 3(4):e215–
e224
12. Ardila D, Kiraly AP, Bharadwaj S et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-
dose chest computed tomography. Nat Med 25(6):954–961. doi.org/10.1038/s41591-019-0447-x
13. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. doi.org/10.1001/jama.2016.17216
14. Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep
learning on whole slide images. Nat Med 25(8):1301–1309. doi.org/10.1038/s41591-019-0508-1
15. Somashekhar SP, Sepúlveda MJ, Puglielli S et al (2017) Early experience with IBM Watson for Oncology (WFO) in breast
cancer treatment. Ann Oncol 28(Suppl 5):v605–v649. doi.org/10.1093/annonc/mdx440
16. Accenture (2023) Accenture invests $150 million in AI to help clients reinvent business through generative AI [Press release].
Available at: https://newsroom.accenture.com/news/accenture-invests-150-million-in-ai-to-help-clients-reinvent-business-throu
gh-generative-ai
17. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in
medicine. Nat Med 25(1):30–36. doi.org/10.1038/s41591-018-0307-0
18. Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
19. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2021) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol. 6(1). doi.org/10.1136/svn-2020-000483

20. Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, Smith AC (2020) The role of telehealth in reducing the
mental health burden from COVID-19. Telemed e-Health 26(4):377–379. doi.org/10.1089/tmj.2020.0068
21. Rajkomar A, Dean J, Kohane I (2018) Machine learning in medicine. N Engl J Med 378(14):1347–1358. doi.org/10.1056/NE
JMra1814259
22. Oviedo S, Vehi J, Calm R, Armengol J (2019) A review of personalized blood glucose prediction strategies for T1DM patients.
Int J Environ Res Public Health 16(14):2533. doi.org/10.3390/ijerph16142533
23. Price WN, Gerke S, Cohen IG (2019) Potential liability for physicians using artificial intelligence. JAMA 322(18):1765–1766.
doi.org/10.1001/jama.2019.15064
24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past,
present and future. Stroke Vasc Neurol 2(4):230–243. doi.org/10.1136/svn-2017-000101
25. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273.
doi.org/10.1016/S1470-2045(19)30149-4
26. McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care ma
chine learning. Lancet Digit Health 2(5):e221–e223. doi.org/10.1016/S2589-7500(20)30065-0
27. Bresnick J (2018) Cost analysis of AI implementation in primary care. HealthITAnalytics. Available at: https://healthita nalytics.
com
28. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digit Med 1(1):5. doi.org/10.1038/s41746-017-
0012-2
29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2019) Deep learning for health informatics. IEEE
J Biomed Health Inform 21(1):4–21. doi.org/10.1109/JBHI.2016.2636665
30. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med
375(13):1216–1219. doi.org/10.1056/NEJMp1606181
31. Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. J Family Med Prim Care
8(7):2328–2332. doi.org/10.4103/jfmpc.jfmpc_440_19
Цитата үчүн

Садырбеков К.К., Дуйшенов Д.А., Садырбекова А.К., Садырбеков А.К. Алгачкы медициналык жардамда жасалма интеллектти ишке ашыруунун SWOT анализи. Кыргызстандын саламаттык сактоо илимий-практикалык журналы
2025, № 2, б. 112-122. https://dx.doi.org/10.51350/zdravkg2025.2.6.13.112.122 
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