Araştırma Makalesi
BibTex RIS Kaynak Göster

Ethical Dimensions of Artificial Intelligence Use in Healthcare: An Analytical Approach Based on DEMATEL

Yıl 2025, Cilt: 6 Sayı: Global Academic Perspective in Social Sciences, 125 - 146, 28.10.2025

Öz

This study aims to identify the ethical risks of using artificial intelligence in healthcare and analyze cause-and- effect relationships using the DEMATEL method. According to the literature review, ethical risks in the use of artificial intelligence in healthcare services are grouped under 10 headings: consensus risk in the decision-making process, unemployment risk, autonomously controlled risk, risk of violating ethical norms, risk of ecological imbalance, data risk, privacy breach risk, algorithmic discrimination and bias risk, uncertain liability risk and decision-making judgment deficiency risk. The DEMATEL method was used to determine the levels of interaction between ethical risks and to prioritize them. According to the DEMATEL results, the most effective and prioritized ethical risks arising in the use of artificial intelligence in healthcare services were determined as “Risk of Algorithmic Discrimination and Bias” with a weight score of 10.85%; “Privacy Breach Risk” with a weight score of 10.63%; and “Autonomous Control Risk” with a weight score of 10.55%, respectively. The least effective and insignificant ethical risk was found to be “Risk of Ecological Imbalance” with a weight score of 8.64%. Findings highlight the importance of using inclusive, fair data sets and careful data processing and algorithm design in AI development. It emphasizes that current ethical regulations cannot fully capture rapid technological developments, that regulatory frameworks need to be updated, and the importance of cooperation between policy makers, developers and health professionals.

Kaynakça

  • Adigwe, O. P., Onavbavba, G., & Sanyaolu, S. E. (2024). Exploring the matrix: Knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Frontiers in Artificial Intelligence, 6, 1293297. https://doi.org/10.3389/frai.2023.1293297
  • Bogani, R., Theodorou, A., Arnaboldi, L., & Wortham, R. H. (2022). Garbage in, toxic data out: A proposal for ethical artificial intelligence sustainability impact statements. AI and Ethics, 3(4), 1135-1142. https://doi.org/10.1007/s43681-022-00221-0
  • Bonnefon, J. F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573-1576. https://doi.org/10.1126/science.aaf2654
  • Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011. https://doi.org/10.1016/j.eswa.2011.08.162
  • Caro-Burnett, J., & Kaneko, S. (2022). Is society ready for AI ethical decision making? Lessons from a study on autonomous cars. Journal of Behavioral and Experimental Economics, 98, 101881. https://doi.org/10.1016/j.socec.2022.101881
  • Chen, Z., Lu, M., Ming, X., Zhang, X., & Zhou, T. (2020). Explore and evaluate innovative value propositions for smart product-service system: A novel graphics-based rough-fuzzy DEMATEL method. Journal of Cleaner Production, 243, 118672. https://doi.org/10.1016/j.jclepro.2019.118672
  • Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024). Bias in medical AI: Implications for clinical decision-making. PLOS Digital Health, 3(11), e0000651. https://doi.org/10.1371/journal.pdig.0000651
  • Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V., & Zou, J. (2021). Lack of transparency and potential bias in artificial intelligence data sets and algorithms: A scoping review. JAMA Dermatology, 157(11), 1362-1369. https://doi.org/10.1001/jamadermatol.2021.3129
  • Debnath, A., Roy, J., Kar, S., Zavadskas, E. K., & Antuchevičienė, J. (2017). A hybrid MCDM approach for strategic project portfolio selection of agro by-products. Sustainability, 9(8), 1302. https://doi.org/10.3390/su9081302
  • Delgado, J., de Manuel, A., Jounou, Parra, I., Moyano, C., Rueda, J., Guersenzvaig, A., Ausin, T., Cruz, M., Casacuberta, D., & Puyol, A. (2022). Bias in algorithms of AI systems developed for covid-19: A scoping review. Journal of Bioethical Inquiry, 19(3), 407-419. https://doi.org/10.1007/s11673-022-10200-z
  • Dhurandhar, D., Dhamande, M., Shivaleela, C., Bhadoria, P., Chandrakar, T., & Agrawal, J. (2025). Exploring medical artificial intelligence readiness among future physicians: Insights from a medical college in central India. Cureus, 17(1), e76835. https://doi.org/10.7759/cureus.76835
  • Elkholy, S. M., Ageiz, M. H., & Elshrief, H. A. (2024). Artificial intelligence and its relation to nurses' innovative behavior: Moderating role of job control. Assiut Scientific Nursing Journal, 12(43), 53-63. https://doi.org/10.21608/asnj.2024.268620.1785
  • Estiri, H., Strasser, Z. H., Rashidian, S., Klann, J. G., Wagholikar, K. B., McCoy Jr, T. H., & Murphy, S. N. (2022). An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes. Journal of the American Medical Informatics Association, 29(8), 1334-1341. https://doi.org/10.1093/jamia/ocac070
  • European Commission. (2021). Proposal for a Regulation of the European Parliament and the Council: Laying Down Harmonised Rules on Artificial Intelligence. European Commission. http://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence (Accessed Date: 26 May 2025).
  • Fazil, A. W., Hakimi, M., & Shahidzay, A. K. (2024). A comprehensive review of bias in AI algorithms. Nusantara Hasana Journal, 3(8), 1-11. https://doi.org/10.59003/nhj.v3i8.1052
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & 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
  • Ghanem, S., Moraleja, M., Gravesande, D., & Rooney, J. (2025). Integrating health equity in artificial intelligence for public health in Canada: A rapid narrative review. Frontiers in Public Health, 13, 1524616. https://doi.org/10.3389/fpubh.2025.1524616
  • Goktas, P., & Grzybowski, A. (2024). Assessing the impact of ChatGPT in dermatology: A comprehensive rapid review. Journal of Clinical Medicine, 13(19), 5909. https://doi.org/10.3390/jcm13195909
  • Guan, H., Dong, L., & Zhao, A. (2022). Ethical risk factors and mechanisms in artificial intelligence decision making. Behavioral Sciences, 12(9), 343. https://doi.org/10.3390/bs12090343
  • Gupta, S., Kamboj, S., & Bag, S. (2023). Role of risks in the development of responsible artificial intelligence in the digital healthcare domain. Information Systems Frontiers, 25(6), 2257-2274. https://doi.org/10.1007/s10796-021-10174-0
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  • Koo, T. H., Zakaria, A. D., Ng, J. K., & Leong, X. B. (2024). Systematic review of the application of artificial intelligence in healthcare and nursing care. The Malaysian Journal of Medical Sciences: MJMS, 31(5), 135-142. https://doi.org/10.21315/mjms2024.31.5.9
  • Kooli, C., & Al Muftah, H. (2022). Artificial intelligence in healthcare: A comprehensive review of its ethical concerns. Technological Sustainability, 1(2), 121-131. https://doi.org/10.1108/TECHS-12-2021-0029
  • Li, F., Wang, S., Gao, Z., Qing, M., Pan, S., Liu, Y., & Hu, C. (2025). Harnessing artificial intelligence in sepsis care: Advances in early detection, personalized treatment, and real-time monitoring. Frontiers in Medicine, 11, 1510792. https://doi.org/10.3389/fmed.2024.1510792
  • Lin, C. L., & Tzeng, G. H. (2009). A value-created system of science (technology) park by using DEMATEL. Expert Systems with Applications, 36(6), 9683-9697. https://doi.org/10.1016/j.eswa.2008.11.040
  • Marabelli, M., Newell, S., & Handunge, V. (2021). The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges. The Journal of Strategic Information Systems, 30(3), 101683. https://doi.org/10.1016/j.jsis.2021.101683
  • Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501-507. https://doi.org/10.1038/s42256-019-0114-4
  • Mohammad Amini, M., Jesus, M., Fanaei Sheikholeslami, D., Alves, P., Hassanzadeh Benam, A., & Hariri, F. (2023). Artificial intelligence ethics and challenges in healthcare applications: A comprehensive review in the context of the European GDPR mandate. Machine Learning and Knowledge Extraction, 5(3), 1023-1035. https://doi.org/10.3390/make5030053
  • Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141-2168. https://doi.org/10.1007/s11948-019-00165-5
  • Murphy, K., Ruggiero, E. D., 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), 14. https://doi.org/10.1186/s12910-021-00577-8
  • Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022). Ethical conundrums in the application of artificial intelligence (AI) in healthcare—A scoping review of reviews. Journal of Personalized Medicine, 12(11), 1914. https://doi.org/10.3390/jpm12111914
  • Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21, 125. https://doi.org/10.1186/s12911-021-01488-9
  • Tang, L., Li, J., & Fantus, S. (2023). Medical artificial intelligence ethics: A systematic review of empirical studies. Digital Health, 9, 20552076231186064. https://doi.org/10.1177/20552076231186064
  • 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.
  • Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., La, V. P., Ho, M. T., Nghiem, K. C. P., Nguyen, H. L. T., Latkin, C. A., Tam, W. W. S., Cheung, N. M., Nguyen, H. K. T., Ho, C. S. H., & Ho, R. C. M. (2019). Global evolution of research in artificial intelligence in health and medicine: A bibliometric study. Journal of Clinical Medicine, 8(3), 360. https://doi.org/10.3390/jcm8030360
  • Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert Systems with Applications, 36(2), 1444-1458. https://doi.org/10.1016/j.eswa.2007.11.058
  • Tzeng, G. H., & Huang, C. Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190. https://doi.org/10.1007/s10479-010-0829-4
  • Vicente, L. G., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13(1), 15737. https://doi.org/10.1038/s41598-023-42384-8
  • Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digital Health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810
  • Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. https://doi.org/10.1016/j.eswa.2005.12.005
  • Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112. https://doi.org/10.1177/0739456X17723971
  • Zavaleta-Monestel, E., Anchía-Alfaro, A., Rojas-Chinchilla, C., Quesada-Loria, D. F., & Arguedas-Chacón, S. (2025). Ethical and Practical Dimensions of Artificial Intelligence (AI) in Healthcare: A Comprehensive Study of Professional Perceptions. Cureus, 17(2), e78416. https://doi.org/10.7759/cureus.78416
  • Zhang, J., & Zhang, Z. M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. https://doi.org/10.1186/s12911-023-02103-9
  • Zhang, L., Liu, R., Jiang, S., Luo, G., & Liu, H. C. (2020). Identification of key performance indicators for hospital management using an extended hesitant linguistic DEMATEL approach. Healthcare, 8(1), 7. https://doi.org/10.3390/healthcare8010007
  • Zhou, J., Wu, Y., Liu, F., Tao, Y., & Gao, J. (2021). Prospects and obstacles analysis of applying blockchain technology to power trading using a deeply improved model based on the DEMATEL approach. Sustainable Cities and Society, 70, 102910. https://doi.org/10.1016/j.scs.2021.102910

Sağlık Hizmetlerinde Yapay Zekâ Kullanımının Etik Boyutu: DEMATEL Tabanlı Analitik Bir Yaklaşım

Yıl 2025, Cilt: 6 Sayı: Global Academic Perspective in Social Sciences, 125 - 146, 28.10.2025

Öz

Bu çalışmada, yapay zekâ teknolojilerinin kullanımında ortaya çıkabilecek etik riskleri belirlemek ve bu riskler arasındaki neden-sonuç ilişkilerini DEMATEL yöntemiyle analiz etmek amaçlanmıştır. Literatür taramasına göre sağlık hizmetlerinde yapay zekâ kullanımındaki etik riskler; karar alma sürecinde mutabakat riski, işsizlik riski, otonom kontrollü risk, etik normlara aykırılık riski, ekolojik dengesizlik riski, veri riski, gizlilik ihlali riski, algoritmik ayrımcılık ve önyargı riski, belirsiz sorumluluk riski ve karar yargısı eksikliği riski olmak üzere 10 başlık altında toplanmıştır. Etik risklerin birbirlerine olan etki düzeylerini belirlemek ve öncelikli olarak sıralayabilmek için DEMATEL yöntemi kullanılmıştır. DEMATEL sonuçlarına göre, sağlık hizmetlerinde yapay zekâ kullanımında ortaya çıkan en etkili ve öncelikli etik riskler sırasıyla %10,85 ağırlık skoru ile “Algoritmik Ayrımcılık ve Önyargı Riski”; %10,63 ağırlık skoru ile “Gizlilik İhlali Riski”; %10,55 ağırlık skoru ile “Otonom Kontrollü Risk” olarak tespit edilmiştir. En etkisiz ve önemsiz etik risk ise %8,64 ağırlık skoru ile “Ekolojik Dengesizlik Riski” olarak bulunmuştur. Sonuçlar, yapay zekâ geliştirme süreçlerinde kapsayıcı ve adil veri setlerinin kullanılmasının yanı sıra algoritma tasarımının yanında veri işleme süreçlerine de dikkat edilmesi gerektiğinin altını çizmektedir. Mevcut etik düzenlemelerin hızlı teknolojik gelişmeleri tam olarak yakalayamadığını ve düzenleyici çerçevelerin güncellenmesi gerektiğini, ayrıca politika yapıcılar, geliştiriciler ve sağlık profesyonelleri arasında iş birliğinin önemini vurgulamaktadır.

Kaynakça

  • Adigwe, O. P., Onavbavba, G., & Sanyaolu, S. E. (2024). Exploring the matrix: Knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Frontiers in Artificial Intelligence, 6, 1293297. https://doi.org/10.3389/frai.2023.1293297
  • Bogani, R., Theodorou, A., Arnaboldi, L., & Wortham, R. H. (2022). Garbage in, toxic data out: A proposal for ethical artificial intelligence sustainability impact statements. AI and Ethics, 3(4), 1135-1142. https://doi.org/10.1007/s43681-022-00221-0
  • Bonnefon, J. F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573-1576. https://doi.org/10.1126/science.aaf2654
  • Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011. https://doi.org/10.1016/j.eswa.2011.08.162
  • Caro-Burnett, J., & Kaneko, S. (2022). Is society ready for AI ethical decision making? Lessons from a study on autonomous cars. Journal of Behavioral and Experimental Economics, 98, 101881. https://doi.org/10.1016/j.socec.2022.101881
  • Chen, Z., Lu, M., Ming, X., Zhang, X., & Zhou, T. (2020). Explore and evaluate innovative value propositions for smart product-service system: A novel graphics-based rough-fuzzy DEMATEL method. Journal of Cleaner Production, 243, 118672. https://doi.org/10.1016/j.jclepro.2019.118672
  • Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024). Bias in medical AI: Implications for clinical decision-making. PLOS Digital Health, 3(11), e0000651. https://doi.org/10.1371/journal.pdig.0000651
  • Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V., & Zou, J. (2021). Lack of transparency and potential bias in artificial intelligence data sets and algorithms: A scoping review. JAMA Dermatology, 157(11), 1362-1369. https://doi.org/10.1001/jamadermatol.2021.3129
  • Debnath, A., Roy, J., Kar, S., Zavadskas, E. K., & Antuchevičienė, J. (2017). A hybrid MCDM approach for strategic project portfolio selection of agro by-products. Sustainability, 9(8), 1302. https://doi.org/10.3390/su9081302
  • Delgado, J., de Manuel, A., Jounou, Parra, I., Moyano, C., Rueda, J., Guersenzvaig, A., Ausin, T., Cruz, M., Casacuberta, D., & Puyol, A. (2022). Bias in algorithms of AI systems developed for covid-19: A scoping review. Journal of Bioethical Inquiry, 19(3), 407-419. https://doi.org/10.1007/s11673-022-10200-z
  • Dhurandhar, D., Dhamande, M., Shivaleela, C., Bhadoria, P., Chandrakar, T., & Agrawal, J. (2025). Exploring medical artificial intelligence readiness among future physicians: Insights from a medical college in central India. Cureus, 17(1), e76835. https://doi.org/10.7759/cureus.76835
  • Elkholy, S. M., Ageiz, M. H., & Elshrief, H. A. (2024). Artificial intelligence and its relation to nurses' innovative behavior: Moderating role of job control. Assiut Scientific Nursing Journal, 12(43), 53-63. https://doi.org/10.21608/asnj.2024.268620.1785
  • Estiri, H., Strasser, Z. H., Rashidian, S., Klann, J. G., Wagholikar, K. B., McCoy Jr, T. H., & Murphy, S. N. (2022). An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes. Journal of the American Medical Informatics Association, 29(8), 1334-1341. https://doi.org/10.1093/jamia/ocac070
  • European Commission. (2021). Proposal for a Regulation of the European Parliament and the Council: Laying Down Harmonised Rules on Artificial Intelligence. European Commission. http://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence (Accessed Date: 26 May 2025).
  • Fazil, A. W., Hakimi, M., & Shahidzay, A. K. (2024). A comprehensive review of bias in AI algorithms. Nusantara Hasana Journal, 3(8), 1-11. https://doi.org/10.59003/nhj.v3i8.1052
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & 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
  • Ghanem, S., Moraleja, M., Gravesande, D., & Rooney, J. (2025). Integrating health equity in artificial intelligence for public health in Canada: A rapid narrative review. Frontiers in Public Health, 13, 1524616. https://doi.org/10.3389/fpubh.2025.1524616
  • Goktas, P., & Grzybowski, A. (2024). Assessing the impact of ChatGPT in dermatology: A comprehensive rapid review. Journal of Clinical Medicine, 13(19), 5909. https://doi.org/10.3390/jcm13195909
  • Guan, H., Dong, L., & Zhao, A. (2022). Ethical risk factors and mechanisms in artificial intelligence decision making. Behavioral Sciences, 12(9), 343. https://doi.org/10.3390/bs12090343
  • Gupta, S., Kamboj, S., & Bag, S. (2023). Role of risks in the development of responsible artificial intelligence in the digital healthcare domain. Information Systems Frontiers, 25(6), 2257-2274. https://doi.org/10.1007/s10796-021-10174-0
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  • Koo, T. H., Zakaria, A. D., Ng, J. K., & Leong, X. B. (2024). Systematic review of the application of artificial intelligence in healthcare and nursing care. The Malaysian Journal of Medical Sciences: MJMS, 31(5), 135-142. https://doi.org/10.21315/mjms2024.31.5.9
  • Kooli, C., & Al Muftah, H. (2022). Artificial intelligence in healthcare: A comprehensive review of its ethical concerns. Technological Sustainability, 1(2), 121-131. https://doi.org/10.1108/TECHS-12-2021-0029
  • Li, F., Wang, S., Gao, Z., Qing, M., Pan, S., Liu, Y., & Hu, C. (2025). Harnessing artificial intelligence in sepsis care: Advances in early detection, personalized treatment, and real-time monitoring. Frontiers in Medicine, 11, 1510792. https://doi.org/10.3389/fmed.2024.1510792
  • Lin, C. L., & Tzeng, G. H. (2009). A value-created system of science (technology) park by using DEMATEL. Expert Systems with Applications, 36(6), 9683-9697. https://doi.org/10.1016/j.eswa.2008.11.040
  • Marabelli, M., Newell, S., & Handunge, V. (2021). The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges. The Journal of Strategic Information Systems, 30(3), 101683. https://doi.org/10.1016/j.jsis.2021.101683
  • Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501-507. https://doi.org/10.1038/s42256-019-0114-4
  • Mohammad Amini, M., Jesus, M., Fanaei Sheikholeslami, D., Alves, P., Hassanzadeh Benam, A., & Hariri, F. (2023). Artificial intelligence ethics and challenges in healthcare applications: A comprehensive review in the context of the European GDPR mandate. Machine Learning and Knowledge Extraction, 5(3), 1023-1035. https://doi.org/10.3390/make5030053
  • Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141-2168. https://doi.org/10.1007/s11948-019-00165-5
  • Murphy, K., Ruggiero, E. D., 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), 14. https://doi.org/10.1186/s12910-021-00577-8
  • Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022). Ethical conundrums in the application of artificial intelligence (AI) in healthcare—A scoping review of reviews. Journal of Personalized Medicine, 12(11), 1914. https://doi.org/10.3390/jpm12111914
  • Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21, 125. https://doi.org/10.1186/s12911-021-01488-9
  • Tang, L., Li, J., & Fantus, S. (2023). Medical artificial intelligence ethics: A systematic review of empirical studies. Digital Health, 9, 20552076231186064. https://doi.org/10.1177/20552076231186064
  • 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.
  • Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., La, V. P., Ho, M. T., Nghiem, K. C. P., Nguyen, H. L. T., Latkin, C. A., Tam, W. W. S., Cheung, N. M., Nguyen, H. K. T., Ho, C. S. H., & Ho, R. C. M. (2019). Global evolution of research in artificial intelligence in health and medicine: A bibliometric study. Journal of Clinical Medicine, 8(3), 360. https://doi.org/10.3390/jcm8030360
  • Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert Systems with Applications, 36(2), 1444-1458. https://doi.org/10.1016/j.eswa.2007.11.058
  • Tzeng, G. H., & Huang, C. Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190. https://doi.org/10.1007/s10479-010-0829-4
  • Vicente, L. G., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13(1), 15737. https://doi.org/10.1038/s41598-023-42384-8
  • Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digital Health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810
  • Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. https://doi.org/10.1016/j.eswa.2005.12.005
  • Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112. https://doi.org/10.1177/0739456X17723971
  • Zavaleta-Monestel, E., Anchía-Alfaro, A., Rojas-Chinchilla, C., Quesada-Loria, D. F., & Arguedas-Chacón, S. (2025). Ethical and Practical Dimensions of Artificial Intelligence (AI) in Healthcare: A Comprehensive Study of Professional Perceptions. Cureus, 17(2), e78416. https://doi.org/10.7759/cureus.78416
  • Zhang, J., & Zhang, Z. M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. https://doi.org/10.1186/s12911-023-02103-9
  • Zhang, L., Liu, R., Jiang, S., Luo, G., & Liu, H. C. (2020). Identification of key performance indicators for hospital management using an extended hesitant linguistic DEMATEL approach. Healthcare, 8(1), 7. https://doi.org/10.3390/healthcare8010007
  • Zhou, J., Wu, Y., Liu, F., Tao, Y., & Gao, J. (2021). Prospects and obstacles analysis of applying blockchain technology to power trading using a deeply improved model based on the DEMATEL approach. Sustainable Cities and Society, 70, 102910. https://doi.org/10.1016/j.scs.2021.102910
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makaleleri
Yazarlar

Emre Yılmaz 0000-0003-4502-9846

Yayımlanma Tarihi 28 Ekim 2025
Gönderilme Tarihi 16 Temmuz 2025
Kabul Tarihi 10 Ekim 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: Global Academic Perspective in Social Sciences

Kaynak Göster

APA Yılmaz, E. (2025). Sağlık Hizmetlerinde Yapay Zekâ Kullanımının Etik Boyutu: DEMATEL Tabanlı Analitik Bir Yaklaşım. Sosyal Mucit Academic Review, 6(Global Academic Perspective in Social Sciences), 125-146. https://doi.org/10.54733/smar.1743307