TY  - JOUR
T1  - SAĞLIK HİZMETLERİNDE YAPAY ZEKA ENTEGRASYONU: SOSYO-TEKNİK FAKTÖRLERİN SWARA VE AHP YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ
TT  - INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HEALTH SERVICES: EVALUATION OF SOCIO-TECHNICAL FACTORS USING SWARA AND AHP METHODS
AU  - Yılmaz, Emre
AU  - Uslu, Yeter
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.35379/cusosbil.1652007
JF  - Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi
PB  - Cukurova University
WT  - DergiPark
SN  - 1304-8899
SP  - 94
EP  - 108
VL  - 34
IS  - Uygarlığın Dönüşümü: Yapay Zekâ
LA  - tr
AB  - Bu çalışmada, sağlık hizmetlerinde yapay zekanın entegrasyonunu etkileyen sosyo-teknik faktörlerin belirlenerek SWARA ve AHP yöntemleriyle önceliklendirilmesi ve yapay zekanın uyumlaştırılmasına yönelik çözüm önerilerinin sunulması amaçlanmıştır. Betimsel ve kesitsel nitelikte olan çalışmada literatür taraması sonucunda sağlık hizmetlerinde yapay zekanın entegrasyonunu etkileyen sosyo-teknik faktörler 10 ana başlık altında toplanmıştır. SWARA ve AHP sonuçlarına göre; sağlık hizmetlerinde yapay zeka entegrasyonunu etkileyen sosyo-teknik faktörlerden en önemlileri sırasıyla veri kalitesi ve güvenliği, teknolojik altyapı uygunluğu ve beceri ve eğitim olarak tespit edilmiştir. Sonuçlar, her iki yöntemin de benzer önceliklendirme çıktıları sunduğunu göstermiştir. Sağlık hizmet sunucuları öncelikli olarak bu faktörler doğrultusunda strateji geliştirerek kaynaklarını bu yöne tahsis etmelidir. Veri kalitesi ve güvenliğinin artırılması için elektronik sağlık kayıtları ve diğer veri kaynaklarının entegrasyonu sağlanmalı, eksik ya da hatalı verilerin önüne geçmek için veri doğrulama ve temizleme mekanizmaları oluşturulmalıdır. Bulut tabanlı veri saklama ve işleme sistemleri, yüksek performanslı bilişim altyapıları ve hızlı veri akışını sağlayan ağ sistemleri gibi teknolojik çözümlerin geliştirilmesi önemlidir. Yapay zeka teknolojilerine yönelik farkındalığını artırmak, yapay zeka okuryazarlığını geliştirmek ve sistemleri etkin kullanmalarını sağlamak için sürekli gelişimi teşvik eden mesleki eğitim programları düzenlenmelidir.
KW  - Sağlık Hizmeti
KW  - Yapay Zeka
KW  - Sosyo-teknik Faktörler
KW  - SWARA
KW  - AHP
N2  - This study aims to determine the socio-technical factors affecting the integration of artificial intelligence (AI) in healthcare services, prioritize them with SWARA and AHP methods, and provide solution proposals for the harmonizing artificial intelligence. Designed as a descriptive and cross-sectional study, a literature review identified ten main categories of socio-technical factors influencing AI integration in healthcare. According to the results of the SWARA and AHP analyses; the most important socio-technical factors affecting the integration of artificial intelligence in healthcare services were determined to be data quality and security, suitability of technological infrastructure, and skills and education, respectively. The results showed that both methods offered similar prioritization outcomes. Healthcare service providers should primarily develop strategies in line with these factors and allocate their resources in this direction. In order to increase data quality and security, integration of electronic health records and other data sources should be ensured, and data verification and cleaning mechanisms should be established to prevent incomplete or incorrect data. It is important to develop technological solutions such as cloud-based data storage and processing systems, high-performance computing infrastructures, and network systems that provide fast data flow. Moreover, professional development programs that encourage continuous learning should be organized to increase awareness of artificial intelligence technologies, improve artificial intelligence literacy, and enable them to use the systems effectively.
CR  - Alami, H., Lehoux, P., Denis, JL, Motulsky, A., Petitgand, C., Savoldelli, M., ... & Fortin, JP. (2020). Organizational readiness for artificial intelligence in health care: Insights for decision-making and practice. Journal of Health Organization and Management, 35(1), 106-114. https://doi.org/10.1108/JHOM-03-2020-0074
CR  - Aldwean, A. & Tenney, D. (2024). Artificial intelligence in healthcare sector: A literature review of the adoption challenges. Open Journal of Business and Management, 12(01), 129-147. https://doi.org/10.4236/ojbm.2024.121009
CR  - Alinezhad, A. & Khalili, J. (2019). SWARA method. New methods and applications in multiple attribute decision making (MADM). Springer. 99-102. https://doi.org/10.1007/978-3-030-15009-9_14
CR  - Amram, D., Cignoni, A., Banfi, T. & Ciuti, G. (2022). From P4 medicine to P5 medicine: Transitional times for a more human-centric approach to AI-based tools for hospitals of tomorrow. Open Research Europe, 2, 33. https://doi.org/10.12688/openreseurope.14524.1
CR  - Dai, T. & Tayur, S. (2022). Designing AI-augmented healthcare delivery systems for physician buy-in and patient acceptance. Production and Operations Management, 31(12), 4443-4451. https://doi.org/10.1111/poms.13850
CR  - Erdemir, N., Öztürk, F. & Kaya, GK. (2022). Kamu personeli performans değerlendirmesi için AHP ve bulanık TOPSIS ile bütünleşik karar destek modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37(4), 1809-1822. https://doi.org/10.17341/gazimmfd.933793
CR  - Fazakarley, CA., Breen, M., Leeson, P., Thompson, B. & Williamson, V. (2023). Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives. BMJ Open, 13(12), e076950. https://doi.org/10.1136/bmjopen-2023-076950
CR  - Gedikli, E. & Kocaman, E. (2025). Priorities for effective management of health expenditures in OECD countries: fuzzy AHP application. Sosyoekonomi, 33(63), 11-29.https://doi.org/10.17233/sosyoekonomi.2025.01.01
CR  - Gerlich, M. (2024). Public anxieties about AI: Implications for corporate strategy and societal impact. Administrative Sciences, 14(11), 288. http://dx.doi.org/10.2139/ssrn.4972893
CR  - Habib, MM, Hoodbhoy, Z. & Siddiqui, MAR. (2024). Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLOS Digital Health, 3(5), e0000443. https://doi.org/10.1371/journal.pdig.0000443
CR  - Hashemkhani Zolfani, S. & Bahrami, M. (2014). Investment prioritizing in high-tech industries based on SWARA-COPRAS approach. Technological and Economic Development of Economy, 20(3), 534-553. https://doi.org/10.3846/20294913.2014.881435
CR  - Hashemkhani Zolfani, S., Yazdani, M. & Zavadskas, EK. (2018). An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing, 22, 7399-7405. https://doi.org/10.1007/s00500-018-3092-2
CR  - Hoseini, M. (2023). Patient experiences with AI in healthcare settings. AI and Tech in Behavioral and Social Sciences, 1(3), 12-18. https://doi.org/10.61838/kman.aitech.1.3.3
CR  - İnce, Ö., Bedir, N. ve Eren, T. (2016). Hastane kuruluş yeri seçimi probleminin AHP ile modellenmesi: Tuzla ilçesi uygulaması. Gazi Sağlık Bilimleri Dergisi, 1(3), 8-21.
CR  - Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
CR  - Karalis, VD. (2024). The integration of artificial intelligence into clinical practice. Applied Biosciences, 3(1), 14-44. https://doi.org/10.3390/applbiosci3010002
CR  - Karami, S., Mousavi, SM & Antucheviciene, J. (2023). Enhancing contractor selection process by a new interval-valued fuzzy decision-making model based on SWARA and CoCoSo methods. Axioms, 12(8), 729. https://doi.org/10.3390/axioms12080729
CR  - Karamollaoğlu, H., Yücedağ, İ. & Doğru, İ. (2021). Risk assessment for electricity generation management process with SWARA-based Fuzzy TOPSIS method. Politeknik Dergisi, 27(1), 69-79. https://doi.org/10.2339/politeknik.917535
CR  - Kaye, J., Shah, N., Kogetsu, A., Coy, S., Katirai, A., Kuroda, M., ... ve Yamamoto, BA. (2024). Moving beyond technical issues to stakeholder involvement: Key areas for consideration in the development of human-centred and trusted AI in healthcare. Asian Bioethics Review, 1-11. https://doi.org/10.1007/s41649-024-00300-w
CR  - Keršuliene, V., Zavadskas, EK. & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258 https://doi.org/10.3846/jbem. 2010.12
CR  - Khalf, AG, Abdelhafez, K. & Khalab, S. (2022). Health care providers’ perception about artificial intelligence applications. Assiut Scientific Nursing Journal, 0(0), 0-0. https://doi.org/10.21608/asnj.2022.144712.1397
CR  - Koo, TH, Zakaria, AD, Ng, JSW & Leong, XB. (2024). Systematic review of the application of artificial intelligence in healthcare and nursing care. Malaysian Journal of Medical Sciences, 31(5), 135-142. https://doi.org/10.21315/mjms2024.31.5.9
CR  - Kumar, Y., Koul, A., Singla, R. & Ijaz, MF. (2023). Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459-8486. https://doi.org/10.1007/s12652-021-03612-z
CR  - Lacson, R., Cochón, L., Ip, IK, Desai, S., Kachalia, A., Dennerlein, JT … & Khorasani, R. (2019). Classifying safety events related to diagnostic imaging from a safety reporting system using a human factors framework. Journal of the American College of Radiology, 16(3), 282-288. https://doi.org/10.1016/j.jacr.2018.10.015
CR  - Long, P., Lu, L., Chen, Q., Chen, Y., Li, C. & Luo, X. (2023). Intelligent selection of healthcare supply chain mode – An applied research based on artificial intelligence. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1310016
CR  - Mamat, NJZ. & Daniel, JK. (2007). Statistical analyses on time complexity and rank consistency between singular value decomposition and the duality approach in AHP: A case study of faculty member selection. Mathematical and Computer Modelling, 46(7-8), 1099-1106. https://doi.org/10.1016/j.mcm.2007.03.025
CR  - Mashabab, MF, Sheniff, MSA, Alsharief, MS, Yami, MAAA, Matnah, HNM, Abbas, AMA, … & Kulayb, AHAA. (2024). The role of artificial intelligence in healthcare: A critical analysis of its implications for patient care. Journal of Ecohumanism, 3(7), 597-604. https://doi.org/10.62754/joe.v3i7.4228
CR  - Mathur, P., Arshad, H., Grasfield, R., Khatib, R., Aggarwal, A., Aurón, M., … & Khare, A. (2024). Navigating AI: A quick start guide for healthcare professionals. Cureus. https://doi.org/10.7759/cureus.72501
CR  - Memon, SS, Murad, S., Shah, SAR, Iqbal, Z., Saeed, R., & Abid, J. (2021). Perception about artificial intelligence in medical education. Pakistan Journal of Medical and Health Sciences, 17(3), 419-420. https://doi.org/10.53350/pjmhs2023173419
CR  - Mooghali, M., Stroud, AM, Yoo, DW, Barry, BA, Grimshaw, AA, Ross, JS … & Miller, JE. (2023). Barriers and facilitators to trustworthy and ethical AI-enabled medical care from patient’s and healthcare provider’s perspectives: A literature review. https://doi.org/10.1101/2023.10.02.23296447
CR  - Owoyemi, A., Osuchukwu, J., Salwei, ME, & Boyd, A. (2024). Checklist to support the development and implementation of AI in clinical settings. https://doi.org/10.1101/2024.08.08.24311701
CR  - Payel, SB, Ahmed, SF, Taseen, N., Siraj, MT & Shahadat, MRB. (2023). Challenges and opportunities for achieving operational sustainability of boilers in the context of industry 4.0. International Journal of Industrial Management, 17(3), 138-151. https://doi.org/10.15282/ijim.17.3.2023.9062
CR  - Pesapane, F., Bracchi, DA, Mulligan, JF, Linnikov, A., АС, Maslennikov, OV, Lanzavecchia, MB … & Carrafiello, G. (2021). Legal and regulatory framework for AI solutions in healthcare in EU, US, China, and Russia: New scenarios after a pandemic. Radiation, 1(4), 261-276. https://doi.org/10.3390/radiation1040022
CR  - Petersson, L., Larsson, I., Nygren, JM, Nilsén, P., Neher, M., Reed, J., … & Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: A qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08215-8
CR  - Rasouli, S., Alkurdi, D. & Jia, B. (2024). The role of artificial intelligence in modern medical education and practice: A systematic literature review. https://doi.org/10.1101/2024.07.25.24311022
CR  - Rathore, FA, & Rathore, MA. (2023). The emerging role of artificial intelligence in healthcare. J Pak Med Assoc, 73, 1368-1369. https://doi.org/10.47391/JPMA.23-48
CR  - Rehman, S., Rehman, N., Naz, M., Mumtaz, A. & Jianglin, Z. (2021). Application of Grey‐Based SWARA and COPRAS Techniques in Disease Mortality Risk Assessment. Journal of Healthcare Engineering, 2021(1), 7302157. https://doi.org/10.1155/2021/7302157
CR  - Rosiana, E., Garside, AK & Amallynda, I. (2021). Integration of rough SWARA and COPRAS in the performance evaluation of third-party logistics providers. Jurnal Teknik Industri, 22(1), 31-42. https://doi.org/10.22219/JTIUMM.Vol22.No1.31-42
CR  - Saaty, TL. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073-1076.
CR  - Saaty, TL. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process (Vol. 6). RWS publications.
CR  - Shah, NJ & Chen, H. (2024). Optimizing healthcare delivery: Investigating key areas for AI integration and impact in clinical settings. https://doi.org/10.20944/preprints202402.1694.v1
CR  - Sığrı, Ü. (2018). Nitel araştırma yöntemleri. İstanbul: Beta Yayıncılık.
CR  - Štefanišinová, N., Muthová, NJ, Štrangfeldová, J. & Šulajová, K. (2021). Implementation and application of artificial intelligence in selected public services. Hrvatska i Komparativna Javna Uprava, 21(4), 601-622. https://doi.org/10.31297/hkju.21.4.2
CR  - Tepe, S., Eti, S. & Kantaş Yılmaz, F. (2023). Sağlık sektöründe görevde yükselme performans kriterlerinin bulanık çok kriterli karar verme ile belirlenmesi. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 10(4), 272-287.
CR  - Tsopra, R., Luchinat, C., Alberghina, L., Lehrach, H., Vanoni, M., Dreher, F., … & Burgun, A. (2021). A framework for validating AI in precision medicine: Considerations from the European ITFOC consortium. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01634-3
CR  - Udegbe, FC, Ebulue, OR, Ebulue, CC & Ekesiobi, CS. (2024). The role of artificial intelligence in healthcare: A systematic review of applications and challenges. International Medical Science Research Journal, 4(4), 500-508. https://doi.org/10.51594/imsrj.v4i4.1052
CR  - Uslu, Y., Hancıoğlu, Y., Yılmaz, E. & Gedikli, E. (2022). Sağlık yöneticilerinin bakış açısıyla sağlık kuruluşlarında risk yönetimi olgusunun analitik hiyerarşi süreci (AHS) yöntemi ile değerlendirilmesi. Üçüncü Sektör Sosyal Ekonomi Dergisi, 57(3), 1508-1513. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.22.07.1805
CR  - Veritti, D., Rubinato, L., Sarao, V., De Nardin, A., Foresti, GL & Lanzetta, P. (2023). Behind the mask: A critical perspective on the ethical, moral, and legal implications of AI in ophthalmology. Graefe's Archive for Clinical and Experimental Ophthalmology, 262(3), 975-982. https://doi.org/10.1007/s00417-023-06245-4
CR  - Vojinović, N., Stević, Ž. & Tanackov, I. (2022). A novel IMF SWARA-FDWGA-PESTEL analysis for assessment of healthcare system. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 139-151. https://doi.org/10.31181/oresta070422211v
CR  - Wang, G., Obrenovic, B., Gu, X., & Godinic, D. (2025). Fear of the new technology: Investigating the factors that influence individual attitudes toward generative Artificial Intelligence (AI). Current Psychology, 1-18. https://doi.org/10.1007/s12144-025-07357-2
CR  - Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, VX., Doshi-Velez, F., ... & Goldenberg, A. (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340. https://doi.org/10.1038/s41591-019-0548-6
CR  - Wu, C., Xu, H., Bai, D., Chen, X., Gao, J. & Jiang, X. (2023). Public perceptions on the application of artificial intelligence in healthcare: A qualitative meta-synthesis. BMJ Open, 13(1), e066322. https://doi.org/10.1136/bmjopen-2022-066322
CR  - Yang, CC. (2022). Explainable artificial intelligence for predictive modeling in healthcare. Journal of Healthcare Informatics Research, 6(2), 228-239. https://doi.org/10.1007/s41666-022-00114-1
CR  - Yıldırım, BF. & Önder, E. (2015). Çok kriterli karar verme yöntemleri. Bursa: Dora Basım-Yayın Dağıtım.
CR  - Yücenur, G. & Yazıcı, M. (2023). Global location selection for an oral health center with a sequential method by SWARA&TODIM. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 476-492. https://doi.org/10.47495/okufbed.1070698
UR  - https://doi.org/10.35379/cusosbil.1652007
L1  - https://dergipark.org.tr/en/download/article-file/4664255
ER  -