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The Role and Future of Artificial Intelligence Supported Virtual Nurses in Diabetes Management

Yıl 2025, Sayı: 11, 80 - 87, 14.04.2025
https://doi.org/10.58252/artukluhealth.1577432

Öz

Diabetes is a chronic disease that requires continuous treatment processes such as regular monitoring of blood glucose levels, taking medications on time, ensuring proper nutrition and physical activity. In this challenging process of managing diabetes, the patient needs to be constantly provided with both information and support. Virtual nurses work through integrated artificial intelligence technology to provide personalised health support to patients. This support provides guidance in the daily health management processes of patients, guiding them in critical areas such as blood glucose monitoring, medication reminders and lifestyle recommendations. Virtual nurses not only support physical health, but also increase patients' psychological motivation and strengthen their commitment to treatment. These intelligence-supported systems offer 24/7 service with the capacity to instantly respond to any patient need and make recommendations based on personal health data. In this way, in the management of chronic diseases that require continuous follow-up, such as diabetes, the burden on patients is eased and access to healthcare services is facilitated. In addition, it supports the patient's education and information processes for a more informed management of personal health. This study emphasises that virtual nurses have a great potential in diabetes management and that these digital health technologies offer significant conveniences for both patients and healthcare professionals. In the long term, it is predicted that virtual nursing will be used much more widely in the treatment of chronic diseases such as diabetes, as it has the potential to improve the quality of life of patients and the efficiency of the healthcare system.

Etik Beyan

Ethics committee approval is not required for this study. It is declared that scientific and ethical principles were complied with during the preparation of this study and all the studies used in this study were cited in the bibliography.

Kaynakça

  • Ali, H.M.A. (2023). Evaluation of awareness and attitude of paediatric nursing students, nurses, and adolescents regarding type one diabetes advanced devices and virtual nursing. Kontakt, 25(2), 100–108. https://doi.org/10.32725/kont.2023.013
  • Almalawi, A., Khan, A.I., Alsolami, F., Abushark, Y.B. and Alfakeeh, A. S. (2023). Managing security of healthcare data for a modern healthcare system. Sensors 2023, 23(7), 3612. https://doi.org/10.3390/S23073612
  • Anastasiadou, M., Alexiadis, A., Polychronidou, E., Votis, K. and Tzovaras, D. (2020). A prototype educational virtual assistant for diabetes management. Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020, 999–1004. https://doi.org/10.1109/BIBE50027.2020.00169
  • Balamurugan, A., Thiruppathi, D., Santhoshkumar, S., and Susithra, K. (2024). Artificial ıntelligence-based chatbot with voice assistance. International Conference on Trends in Quantum Computing and Emerging Business Technologies, 1-6. https://doi.org/10.1109/TQCEBT59414.2024.10545197
  • Bassi, G., Giuliano, C., Perinelli, A., Forti, S., Gabrielli, S., Mancinelli, E. and Salcuni, S. (2022). Motibot: the Virtual Coach for healthy coping intervention in diabetes. European Psychiatry, 65(1), 8-11. https://doi.org/10.1192/J.EURPSY.2022.453
  • Bickmore, T.W., Schulman, D. and Sidner, C. L. (2011). A reusable framework for health counseling dialogue systems based on a behavioral medicine ontology. Journal of Biomedical Informatics, 44(2), 183–197. https://doi.org/10.1016/J.JBI.2010.12.006
  • Buinhas, S., Cláudio, A.P., Carmo, M.B., Balsa, J., Cavaco, A., Mendes, A., Félix, I., Pimenta, N. and Guerreiro, M.P. (2019). Virtual assistant to improve self-care of older people with type 2 diabetes: First prototype. Communications in Computer and Information Science, 1016, 236–248. https://doi.org/10.1007/978-3-030-16028-9_21
  • Ceyhan Akça, N., Aslan Cobutoğlu, S., Özbek, Ö.Y. ve Akça, M.F. (2024). Yapay zekânın edebiyatta kullanım serüveni. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, 39, 283–306. https://doi.org/10.29000/RUMELIDE.1470139
  • Choi, J., Woo, S. and Ferrell, A. (2023). Artificial intelligence assisted telehealth for nursing: A scoping review. Journal of Telemedicine and Telecare. 31(1), 140-149. https://doi.org/10.1177/1357633X231167613
  • Contreras, I. and Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res, 20(5), e10775. https://doi.org/10.2196/10775
  • Dammavalam, S., Chandana, N., Rao, T., Lahari, A., and Aparna, B. (2022). AI based chatbot for hospital management system. 3rd International Conference on Computing, Analytics and Networks (ICAN), 1-5. https://doi.org/10.1109/ICAN56228.2022.10007105
  • Davies, M.J., D’Alessio, D.A., Fradkin, J., Kernan, W.N., Mathieu, C., Mingrone, G., Rossing, P., Tsapas, A., Wexler, D.J. and Buse, J.B. (2018). Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the european association for the study of diabetes (EASD). Diabetes Care, 41(12), 2669–2701. https://doi.org/10.2337/dci18-0033
  • Dihingia, H., Ahmed, S., Borah, D., Gupta, S., Phukan, K. and Muchahari, M.K. (2021). Chatbot implementation in customer service industry through deep neural networks. 2021 International Conference on Computational Performance Evaluation, ComPE 2021, 193–198. https://doi.org/10.1109/COMPE53109.2021.9752271
  • Doğu Yıldıran, Y. ve Erdem, Ş. (2023). Yapay zeka tabanlı chatbot hizmetinin kullanıcı alışkanlık ve davranışları üzerine etkileri ve bir uygulama. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 46(1), 20-43. https://doi.org/10.14780/MUIIBD.1381666
  • Fitzpatrick, K.K., Darcy, A. and Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2). https://doi.org/10.2196/mental.7785
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P. and Vayena, E. (2018). AI4People-An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  • Gal, R.L., Raghınaru, D., Aleppo, G., Olson, B.A., Kruger, D.F., Bergenstal, R.M., Weınstock, R.S., Bradshaw, A., Mcarthur, T.S., Oser, S., Oser, T., Hood, K.K., Cushman, T.L., Johnson, M.L., Kollman, C. and Beck, R. (2023). 149-LB: Telemedicine Intervention Impact on Diabetes Self-Management—Twelve-Month Virtual Diabetes Specialty Clinic (VDISC) Study Outcomes. Diabetes, 72(1). https://doi.org/10.2337/DB23-149-LB
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  • Goyal, S. and Cafazzo, J.A. (2013). Mobile phone health apps for diabetes management: Current evidence and future developments. QJM: An International Journal of Medicine, 106(12), 1067–1069. https://doi.org/10.1093/qjmed/hct203
  • Goyal, S., Morita, P., Lewis, G.F., Yu, C., Seto, E. and Cafazzo, J.A. (2016). The systematic design of a behavioural mobile health application for the self-management of type 2 diabetes. Canadian Journal of Diabetes, 40(1), 95–104. https://doi.org/10.1016/J.JCJD.2015.06.007
  • Holmen, H., Torbjørnsen, A., Wahl, A.K., Jenum, A.K., Småstuen, M.C., Årsand, E. and Ribu, L. (2014). A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: One-year results from the norwegian randomized controlled trial RENEWING HEALTH. JMIR MHealth and UHealth, 2(4). https://doi.org/10.2196/mhealth.3882
  • Huang, X., Baker, J. and Reddy, R. (2014). A historical perspective of speech recognition. Communications of the ACM, 57(1), 94–103. https://doi.org/10.1145/2500887
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  • Joshi, S., Shinde, S., Shinde, P., Sagar, N., and Rathod, S. (2023). Chatbot using deep learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 12(3), 228–232. https://doi.org/10.32628/CSEIT2390148
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Diyabet Yönetiminde Yapay Zeka Destekli Sanal Hemşirelerin Rolü ve Geleceği

Yıl 2025, Sayı: 11, 80 - 87, 14.04.2025
https://doi.org/10.58252/artukluhealth.1577432

Öz

Diyabet; kan şekeri seviyelerinin düzenli olarak izlenmesi, ilaçların zamanında alınması, uygun beslenme ve fiziksel aktivitenin sağlanması gibi sürekli tedavi süreçlerini gerektiren kronik bir hastalıktır. Diyabetin yönetimi zorlu bir süreç olmakla birlikte hastaya sürekli olarak hem bilgi hem de destek verilmesi gerekmektedir. Sanal hemşireler, hastalara kişiselleştirilmiş sağlık desteği sunmak için entegre yapay zeka teknolojisi aracılığıyla çalışmaktadır. Bu destek, hastaların günlük sağlık yönetimi süreçlerinde rehberlik sağlamakta; kan şekeri takibi, ilaç hatırlatmaları ve yaşam tarzı önerileri gibi kritik alanlarda yol gösterici olmaktadır. Sanal hemşireler yalnızca fiziksel sağlığı desteklemekle kalmayıp, hastaların psikolojik motivasyonlarını da artırarak tedaviye olan bağlılıklarını güçlendirmektedir. Yapay zeka destekli bu sistemler, hastanın herhangi bir ihtiyacına anında yanıt verme ve kişisel sağlık verilerine dayalı tavsiyelerde bulunma kapasitesiyle 7/24 hizmet sunmaktadır. Diyabet gibi sürekli takip gerektiren kronik hastalıkların yönetiminde; hastaların üzerindeki yük hafifletilmekte ve sağlık hizmetlerine erişim kolaylaşmaktadır. Ayrıca kişisel sağlığın daha bilinçli bir şekilde yönetilmesi için hastanın eğitim ve bilgilendirme süreçleri desteklenebilmektedir. Bu çalışma; sanal hemşirelerin diyabet yönetiminde büyük bir potansiyel öneme sahip olduğunu ve bu dijital sağlık teknolojilerinin hem hastalar hem de sağlık profesyonelleri için önemli kolaylıklar sunduğunu vurgulamaktadır. Uzun vadede diyabet gibi kronik hastalıkların tedavisinde sanal hemşireliğin; hastaların yaşam kalitesini ve sağlık sisteminin verimliliğini artırma potansiyeline sahip olması nedeniyle çok daha yaygın bir şekilde kullanılacağı öngörülmektedir.

Etik Beyan

Bu çalışma için etik kurul onayına gerek yoktur. Bu çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulduğu ve yararlanılan tüm çalışmaların kaynakçada belirtildiği beyan olunur.

Kaynakça

  • Ali, H.M.A. (2023). Evaluation of awareness and attitude of paediatric nursing students, nurses, and adolescents regarding type one diabetes advanced devices and virtual nursing. Kontakt, 25(2), 100–108. https://doi.org/10.32725/kont.2023.013
  • Almalawi, A., Khan, A.I., Alsolami, F., Abushark, Y.B. and Alfakeeh, A. S. (2023). Managing security of healthcare data for a modern healthcare system. Sensors 2023, 23(7), 3612. https://doi.org/10.3390/S23073612
  • Anastasiadou, M., Alexiadis, A., Polychronidou, E., Votis, K. and Tzovaras, D. (2020). A prototype educational virtual assistant for diabetes management. Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020, 999–1004. https://doi.org/10.1109/BIBE50027.2020.00169
  • Balamurugan, A., Thiruppathi, D., Santhoshkumar, S., and Susithra, K. (2024). Artificial ıntelligence-based chatbot with voice assistance. International Conference on Trends in Quantum Computing and Emerging Business Technologies, 1-6. https://doi.org/10.1109/TQCEBT59414.2024.10545197
  • Bassi, G., Giuliano, C., Perinelli, A., Forti, S., Gabrielli, S., Mancinelli, E. and Salcuni, S. (2022). Motibot: the Virtual Coach for healthy coping intervention in diabetes. European Psychiatry, 65(1), 8-11. https://doi.org/10.1192/J.EURPSY.2022.453
  • Bickmore, T.W., Schulman, D. and Sidner, C. L. (2011). A reusable framework for health counseling dialogue systems based on a behavioral medicine ontology. Journal of Biomedical Informatics, 44(2), 183–197. https://doi.org/10.1016/J.JBI.2010.12.006
  • Buinhas, S., Cláudio, A.P., Carmo, M.B., Balsa, J., Cavaco, A., Mendes, A., Félix, I., Pimenta, N. and Guerreiro, M.P. (2019). Virtual assistant to improve self-care of older people with type 2 diabetes: First prototype. Communications in Computer and Information Science, 1016, 236–248. https://doi.org/10.1007/978-3-030-16028-9_21
  • Ceyhan Akça, N., Aslan Cobutoğlu, S., Özbek, Ö.Y. ve Akça, M.F. (2024). Yapay zekânın edebiyatta kullanım serüveni. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, 39, 283–306. https://doi.org/10.29000/RUMELIDE.1470139
  • Choi, J., Woo, S. and Ferrell, A. (2023). Artificial intelligence assisted telehealth for nursing: A scoping review. Journal of Telemedicine and Telecare. 31(1), 140-149. https://doi.org/10.1177/1357633X231167613
  • Contreras, I. and Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res, 20(5), e10775. https://doi.org/10.2196/10775
  • Dammavalam, S., Chandana, N., Rao, T., Lahari, A., and Aparna, B. (2022). AI based chatbot for hospital management system. 3rd International Conference on Computing, Analytics and Networks (ICAN), 1-5. https://doi.org/10.1109/ICAN56228.2022.10007105
  • Davies, M.J., D’Alessio, D.A., Fradkin, J., Kernan, W.N., Mathieu, C., Mingrone, G., Rossing, P., Tsapas, A., Wexler, D.J. and Buse, J.B. (2018). Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the european association for the study of diabetes (EASD). Diabetes Care, 41(12), 2669–2701. https://doi.org/10.2337/dci18-0033
  • Dihingia, H., Ahmed, S., Borah, D., Gupta, S., Phukan, K. and Muchahari, M.K. (2021). Chatbot implementation in customer service industry through deep neural networks. 2021 International Conference on Computational Performance Evaluation, ComPE 2021, 193–198. https://doi.org/10.1109/COMPE53109.2021.9752271
  • Doğu Yıldıran, Y. ve Erdem, Ş. (2023). Yapay zeka tabanlı chatbot hizmetinin kullanıcı alışkanlık ve davranışları üzerine etkileri ve bir uygulama. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 46(1), 20-43. https://doi.org/10.14780/MUIIBD.1381666
  • Fitzpatrick, K.K., Darcy, A. and Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2). https://doi.org/10.2196/mental.7785
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P. and Vayena, E. (2018). AI4People-An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  • Gal, R.L., Raghınaru, D., Aleppo, G., Olson, B.A., Kruger, D.F., Bergenstal, R.M., Weınstock, R.S., Bradshaw, A., Mcarthur, T.S., Oser, S., Oser, T., Hood, K.K., Cushman, T.L., Johnson, M.L., Kollman, C. and Beck, R. (2023). 149-LB: Telemedicine Intervention Impact on Diabetes Self-Management—Twelve-Month Virtual Diabetes Specialty Clinic (VDISC) Study Outcomes. Diabetes, 72(1). https://doi.org/10.2337/DB23-149-LB
  • Garg, S.K., Almurashi, A.M. and Rodriguez, E. (2023). Virtual clinics for diabetes care. Diabetes Technology & Therapeutics, 25(1), 2–14. https://doi.org/10.1089/DIA.2023.2501
  • Goyal, S. and Cafazzo, J.A. (2013). Mobile phone health apps for diabetes management: Current evidence and future developments. QJM: An International Journal of Medicine, 106(12), 1067–1069. https://doi.org/10.1093/qjmed/hct203
  • Goyal, S., Morita, P., Lewis, G.F., Yu, C., Seto, E. and Cafazzo, J.A. (2016). The systematic design of a behavioural mobile health application for the self-management of type 2 diabetes. Canadian Journal of Diabetes, 40(1), 95–104. https://doi.org/10.1016/J.JCJD.2015.06.007
  • Holmen, H., Torbjørnsen, A., Wahl, A.K., Jenum, A.K., Småstuen, M.C., Årsand, E. and Ribu, L. (2014). A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: One-year results from the norwegian randomized controlled trial RENEWING HEALTH. JMIR MHealth and UHealth, 2(4). https://doi.org/10.2196/mhealth.3882
  • Huang, X., Baker, J. and Reddy, R. (2014). A historical perspective of speech recognition. Communications of the ACM, 57(1), 94–103. https://doi.org/10.1145/2500887
  • Ibuki, T., Ibuki, A. and Nakazawa, E. (2023). Possibilities and ethical issues of entrusting nursing tasks to robots and artificial intelligence. Nursing Ethics, 31(6), 1010-1020. https://doi.org/10.1177/09697330221149094
  • Joshi, S., Shinde, S., Shinde, P., Sagar, N., and Rathod, S. (2023). Chatbot using deep learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 12(3), 228–232. https://doi.org/10.32628/CSEIT2390148
  • Khodve, G.B. and Banerjee, S. (2022). Artificial intelligence in efficient diabetes care. Current Diabetes Reviews, 19(9), 45–54. https://doi.org/10.2174/1573399819666220905163940
  • Lecun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  • Lei, W., Fuster-Barceló, C., Reder, G., Muñoz-Barrutia, A. and Ouyang, W. (2023). BioImage.IO Chatbot: A Community-Driven AI Assistant for Integrative Computational Bioimaging. https://doi.org/10.5281/zenodo.10032227
  • Luo, J., Wu, M., Gopukumar, D. and Zhao, Y. (2016). Big data application in biomedical research and health care: a literature review. Biomedical Informatics Insights, 8, BII.S31559. https://doi.org/10.4137/BII.S31559
  • Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., Hinz, O., Morana, S., and Söllner, M. (2019). AI-Based digital assistants. Business & Information Systems Engineering, 61, 535 - 544. https://doi.org/10.1007/s12599-019-00600-8
  • McCoy, M.A. and Theeke, L.A. (2019). A systematic review of the relationships among psychosocial factors and coping in adults with type 2 diabetes mellitus. International Journal of Nursing Sciences, 6(4), 468–477. https://doi.org/10.1016/j.ijnss.2019.09.003
  • Mumford, B., Oldham, V., Lee, D., Jones, J. and Das, G. (2021). The effectiveness of running virtual clinics as part of insulin pump services for patients with type 1 diabetes. Endocrine and Metabolic Science, 3, 100083. https://doi.org/10.1016/J.ENDMTS.2021.100083
  • Nerpin, E., Toft, E., Fischier, J., Lindholm-Olinder, A. and Leksell, J. (2020). A virtual clinic for the management of diabetes-type 1: Study protocol for a randomised wait-list controlled clinical trial. BMC Endocrine Disorders, 20(1), 1–7. https://doi.org/10.1186/S12902-020-00615-3/PEER-REVIEW
  • Nundy, S., Dick, J.J., Chou, C.H., Nocon, R.S., Chin, M.H. and Peek, M.E. (2017). Mobile phone diabetes project led to improved glycemic control and net savings for chicago plan participants. Health Affairs, 33(2), 265–272. https://doi.org/10.1377/HLTHAFF.2013.0589
  • Polonsky, W.H., Fisher, L., Hessler, D. and Edelman, S.V. (2017). Investigating hypoglycemic confidence in type 1 and type 2 diabetes. Diabetes Technology and Therapeutics, 19(2), 131–136. https://doi.org/10.1089/dia.2016.0366
  • Powers, M.A., Bardsley, J., Cypress, M., Duker, P., Funnell, M.M., Fischl, A.H., Maryniuk, M.D., Siminerio, L. and Vivian, E. (2016). Diabetes self-management education and support in type 2 diabetes: A joint position statement of the American Diabetes Association, the American Association of diabetes educators, and the Academy of nutrition and dietetics. Clinical Diabetes, 34(2), 70–80. https://doi.org/10.2337/diaclin.34.2.70
  • Powers, M.A., Bardsley, J., Cypress, M., Duker, P., Funnell, M.M., Fischl, A.H., Maryniuk, M.D., Siminerio, L. and Vivian, E. (2017). Diabetes self-management education and support in type 2 diabetes: a joint position statement of the american diabetes association, the american association of diabetes educators, and the academy of nutrition and dietetics. Diabetes Educator, 43(1), 40–53. https://doi.org/10.1177/0145721716689694
  • Rachdaoui, N. (2020). Insulin: The friend and the foe in the development of type 2 diabetes mellitus. International Journal of Molecular Sciences, 21(5), 1–21. https://doi.org/10.3390/IJMS21051770
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  • Schroeder, J., Wilks, C., Rowan, K., Toledo, A., Paradiso, A., Czerwinski, M., Mark, G. and Linehan, M.M. (2018). Pocket skills: a conversational mobile web app to support dialectical behavioral therapy. International Conference on Human Factors in Computing Systems, 398, 1- 15. https://doi.org/10.1145/3173574.3173972
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  • Shabbir, M., Shabbir, A., Iwendi, C., Javed, A.R., Rizwan, M., Herencsar, N. and Lin, J.C.W. (2021). Enhancing security of health ınformation using modular encryption standard in mobile cloud computing. IEEE Access, 9, 8820–8834. https://doi.org/10.1109/ACCESS.2021.3049564
  • Shum, Hy., He, Xd. and Li, D. (2018). From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology and Electronic Engineering, 19(1), 10–26. https://doi.org/10.1631/FITEE.1700826
  • Surendran, A., Murali, R. and Babu, R. (2020). Conversational AI - A Retrieval Based Chatbot.
  • Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
  • Weizenbaum, J. (1966). ELIZA-A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
  • Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J., Li, L., Sun, X., Tian, H. and Li, S. (2017). Mobile app-based interventions to support diabetes self-management: A systematic review of randomized controlled trials to identify functions associated with glycemic efficacy. JMIR MHealth and UHealth, 5(3), e6522. https://doi.org/10.2196/mhealth.6522
  • Yıldız, B. ve Dayı, F. (2024). Finans uygulamalarında yapay zekâ destekli chatbot kullanımı üzerine nicel bir araştırma. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(2), 215–231. https://doi.org/10.25287/OHUIIBF.1384420
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Dahili Hastalıklar Hemşireliği
Bölüm Derlemeler
Yazarlar

Ahmet Ceviz 0009-0004-3536-2113

Gürkan Özden 0000-0002-2775-3163

Yayımlanma Tarihi 14 Nisan 2025
Gönderilme Tarihi 1 Kasım 2024
Kabul Tarihi 20 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 11

Kaynak Göster

APA Ceviz, A., & Özden, G. (2025). Diyabet Yönetiminde Yapay Zeka Destekli Sanal Hemşirelerin Rolü ve Geleceği. Artuklu Health(11), 80-87. https://doi.org/10.58252/artukluhealth.1577432

  Artuklu Health dergisinde yayımlanan tüm makaleler Creative Commons Atıf-Gayri Ticari 4.0 Uluslararası Lisansı ile lisanslanmıştır.