The Use and Future of Artificial Intelligence (AI) in Public Health

Yıl 2024, Cilt: 1 Sayı: 1, 1 - 16, 27.12.2024

Ali Göde

Öz

Artificial intelligence (AI) is a rapidly developing technology with a wide range of applications in public health. Artificial intelligence enables faster detection and solution development of public health problems through big data analysis and epidemiological modeling methods. Artificial intelligence makes great contributions especially in areas such as early detection of epidemics, eliminating inequalities in access to health services and creating personalized treatment plans. Artificial intelligence-based systems are used to monitor the spread of epidemics, identify high-risk areas and take preventive measures against factors that threaten public health.
Artificial intelligence offers great advantages in planning health services and managing resources efficiently. While helping to develop health policies based on data, it also accelerates vaccine development processes, allowing effective solutions to be offered in a shorter time. It is also important to integrate it with applications such as telemedicine to provide easier access to healthcare services for individuals living in rural or disadvantaged areas. However, ethical issues and data privacy issues related to the use of AI are also critical points that need to be carefully addressed.
In the future, with the further integration of AI into public health, more effective, accessible and personalized healthcare services are expected. Artificial intelligence offers important opportunities to protect and improve public health.

Anahtar Kelimeler

Public Health, Health Management, New Technologies in Health, Artificial Intelligence (AI)

Halk Sağlığında Yapay Zekâ (YZ) Kullanımı ve Geleceği

Öz

Yapay zekâ (YZ), halk sağlığı alanında geniş uygulama alanına sahip, hızla gelişen bir teknolojidir. Yapay zekâ, büyük veri analizi ve epidemiyolojik modelleme yöntemleri ile halk sağlığı sorunlarının daha hızlı tespit edilmesine ve çözüm geliştirilmesine olanak tanımaktadır. Özellikle salgın hastalıkların erken tespiti, sağlık hizmetlerine erişimdeki eşitsizliklerin giderilmesi ve kişiselleştirilmiş tedavi planlarının oluşturulması gibi alanlarda yapay zekâ büyük katkılar sunmaktadır. Salgın hastalıkların yayılımını izlemek, yüksek riskli bölgeleri tespit etmek ve toplum sağlığını tehdit eden faktörlere karşı önleyici tedbirler almak için yapay zekâ tabanlı sistemler kullanılmaktadır.
Yapay zekâ, sağlık hizmetlerinin planlanmasında ve kaynakların verimli bir şekilde yönetilmesinde büyük avantajlar sunmaktadır. Sağlık politikalarının veriye dayalı olarak geliştirilmesine yardımcı olurken, aşı geliştirme süreçlerini de hızlandırarak daha kısa sürede etkili çözümler sunulmasına olanak tanımaktadır. Ayrıca kırsal veya dezavantajlı bölgelerde yaşayan bireylerin sağlık hizmetlerine daha kolay erişimini sağlamak için tele-tıp gibi uygulamalarla entegrasyonu önemlidir. Bununla birlikte, yapay zekânın kullanımıyla ilgili etik sorunlar ve veri gizliliği konuları da dikkatle ele alınması gereken kritik noktalardır.
Gelecekte, yapay zekânın halk sağlığına daha fazla entegre edilmesiyle birlikte, daha etkili, erişilebilir ve kişiselleştirilmiş sağlık hizmetleri sunulması beklenmektedir. Yapay zekâ, halk sağlığını koruma ve iyileştirme noktasında önemli fırsatlar sunmaktadır.

Anahtar Kelimeler

Halk Sağlığı, Sağlık Yönetimi, Sağlıkta Yeni Teknolojiler, Yapay Zeka (YZ)

Kaynakça

• Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023). Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy and Technology, 12(1), 100702. https://doi.org/10.1016/j.hlpt.2022.100702
• Al-Antari, M. A. (2023). Artificial intelligence for medical diagnostics—existing and future AI technology! Diagnostics, 13(4), 688. https://doi.org/10.3390/DIAGNOSTICS13040688
• Alıcılar, H. E., & Çöl, M. (2021). Halk sağlığında yapay zekanın kullanımı. Uludağ Üniversitesi Tıp Fakültesi Dergisi, 47(1), 151–158. https://doi.org/10.32708/uutfd.891274
• Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 1–15. https://doi.org/10.1186/s12909-023-04698-z
• Anjaria, P., Asediya, V., Bhavsar, P., Pathak, A., Desai, D., & Patil, V. (2023). Artificial intelligence in public health: Revolutionizing epidemiological surveillance for pandemic preparedness and equitable vaccine access. Vaccines, 11(7), 1–4. https://doi.org/10.3390/vaccines11071154
• Barragán-Montero, A., Javaid, U., Valdés, G., Nguyen, D., Desbordes, P., Macq, B., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., Michiels, S., Souris, K., Sterpin, E., & Lee, J. A. (2021). Artificial intelligence and machine learning for medical imaging: a technology review. Physica Medica : PM : An International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB), 83, 242. https://doi.org/10.1016/J.EJMP.2021.04.016
• Bravi, B. (2024). Development and use of machine learning algorithms in vaccine target selection. Npj Vaccines, 9(1), 1–14. https://doi.org/10.1038/s41541-023-00795-8
• Celi, L. A., Cellini, J., Charpignon, M.-L., Dee, E. C., Dernoncourt, F., Eber, R., Mitchell, W. G., Moukheiber, L., Schirmer, J., Situ, J., Paguio, J., Park, J., Wawira, J. G., & Yao, S. (2022). Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digital Health, 1(3), 1–19. https://doi.org/10.1371/journal.pdig.0000022
• Chen, J., & See, K. C. (2020). Artificial Intelligence for COVID-19: Rapid Review. Journal of Medical Internet Research, 22(10), e21476. https://doi.org/10.2196/21476
• d’Elia, A., Gabbay, M., Rodgers, S., Kierans, C., Jones, E., Durrani, I., Thomas, A., & Frith, L. (2022). Artificial intelligence and health inequities in primary care: a systematic scoping review and framework. Family Medicine and Community Health, 10(Suppl 1), 1–10. https://doi.org/10.1136/fmch-2022-001670
• Elhaddad, M., & Hamam, S. (2024). AI-driven clinical decision support systems: an ongoing pursuit of potential. Cureus, 16(4), 1–9. https://doi.org/10.7759/cureus.57728
• Farhud, D. D., & Zokaei, S. (2021). Ethical issues of artificial intelligence in medicine and healthcare. Iranian Journal of Public Health, 50(11), i–v. https://doi.org/10.18502/IJPH.V50I11.7600
• Federspiel, F., Mitchell, R., Asokan, A., Umana, C., & McCoy, D. (2023). Threats by artificial intelligence to human health and human existence. BMJ Global Health, 8(5), 1–6. https://doi.org/10.1136/bmjgh-2022-010435
• Fisher, S., & Rosella, L. C. (2022). Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health, 22(1), 1–14. https://doi.org/10.1186/s12889-022-14422-z
• Ghosh, A., Larrondo-Petrie, M. M., & Pavlovic, M. (2023). Revolutionizing vaccine development for COVID-19: A review of AI-based approaches. Information, 14(12), 1–24. https://doi.org/10.3390/info14120665
• Green, B. L., Murphy, A., & Robinson, E. (2024). Accelerating health disparities research with artificial intelligence. Frontiers in Digital Health, 6, 1–4. https://doi.org/10.3389/fdgth.2024.1330160
• Hoşgör, H., & Güngördü, H. (2022). Sağlıkta yapay zekanın kullanım alanları üzerine nitel bir araştırma. Avrupa Bilim ve Teknoloji Dergisi, 35, 395–407. https://doi.org/10.31590/EJOSAT.1052614
• Jeste, D. V., Graham, S. A., Nguyen, T. T., Depp, C. A., Lee, E. E., & Kim, H. C. (2020). Beyond artificial intelligence (AI): Exploring artificial wisdom (AW). International Psychogeriatrics, 32(8), 993. https://doi.org/10.1017/S1041610220000927
• Jiao, Z., Ji, H., Yan, J., & Qi, X. (2023). Application of big data and artificial intelligence in epidemic surveillance and containment. Intelligent Medicine, 3(1), 36–43. https://doi.org/10.1016/j.imed.2022.10.003
• Kaushik, R., Kant, R., & Christodoulides, M. (2023). Artificial intelligence in accelerating vaccine development - current and future perspectives. Frontiers in Bacteriology, 2, 1–8. https://doi.org/10.3389/fbrio.2023.1258159
• Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024). Artificial intelligence and decision-making in healthcare: A thematic analysis of a systematic review of reviews. Health Services Research and Managerial Epidemiology, 11, 1–15. https://doi.org/10.1177/23333928241234863
• Khoury, M. J., Armstrong, G. L., Bunnell, R. E., Cyril, J., & Iademarco, M. F. (2020). The intersection of genomics and big data with public health: Opportunities for precision public health. PLOS Medicine, 17(10), 1–14. https://doi.org/10.1371/journal.pmed.1003373
• Malik, Y. S., Sircar, S., Bhat, S., Ansari, M. I., Pande, T., Kumar, P., Mathapati, B., Balasubramanian, G., Kaushik, R., Natesan, S., Ezzikouri, S., El Zowalaty, M. E., & Dhama, K. (2021). How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future. Reviews in Medical Virology, 31(5), 1–11. https://doi.org/10.1002/rmv.2205
• Messova, A., Pivina, L., Ygiyeva, D., Batenova, G., Dyussupov, A., Jamedinova, U., Syzdykbayev, M., Adilgozhina, S., & Bayanbaev, A. (2024). Lessons of the COVID-19 pandemic for ambulance service in Kazakhstan. Healthcare, 12(16), 1–10. https://doi.org/10.3390/healthcare12161568
• Mooney, S. J., & Pejaver, V. (2024). Big data in public health: Terminology, machine learning, and privacy. Annual Review of Public Health, 39(1), 95–112. https://doi.org/10.1146/annurev-publhealth-040617-014208
• Müller, V. C. (2023). Ethics of artificial ıntelligence and robotics. In E. N. Zalta & U. Nodelman (Eds.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University.
• Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021). Artificial intelligence for good health: a scoping review of the ethics literature. BMC Medical Ethics, 22(1), 1–17. https://doi.org/10.1186/S12910-021-00577-8/FIGURES/4
• Olawade, D. B., Wada, O. J., David-Olawade, A. C., Kunonga, E., Abaire, O., & Ling, J. (2023). Using artificial intelligence to improve public health: A narrative review. Frontiers in Public Health, 11, 1–9. https://doi.org/10.3389/fpubh.2023.1196397
• Oniani, D., Hilsman, J., Peng, Y., Poropatich, R. K., Pamplin, J. C., Legault, G. L., & Wang, Y. (2023). Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. Npj Digital Medicine, 6(1), 1–10. https://doi.org/10.1038/s41746-023-00965-x
• Ramezani, M., Takian, A., Bakhtiari, A., Rabiee, H. R., Ghazanfari, S., & Mostafavi, H. (2023). The application of artificial intelligence in health policy: a scoping review. BMC Health Services Research, 23(1), 1–11. https://doi.org/10.1186/s12913-023-10462-2
• Singh, R., Wu, W., Wang, G., & Kalra, M. K. (2020). Artificial intelligence in image reconstruction: The change is here. Physica Medica : PM : An International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB), 79, 113–125. https://doi.org/10.1016/J.EJMP.2020.11.012
• Wang, C., Zhu, X., Hong, J. C., & Zheng, D. (2019). Artificial intelligence in radiotherapy treatment planning: Present and future. Technology in Cancer Research & Treatment, 18. https://doi.org/10.1177/1533033819873922
• World Health Organization. (2024). Artificial intelligence public health - Q&A on artificial intelligence for supporting public health. Pan American Health Organization.
• Zeng, D., Cao, Z., & Neill, D. B. (2021). Artificial intelligence–enabled public health surveillance—from local detection to global epidemic monitoring and control. In Artificial Intelligence in Medicine (pp. 437–453). Elsevier. https://doi.org/10.1016/B978-0-12-821259-2.00022-3