SAĞLIK EKONOMİSİ DEĞERLENDİRMELERİNDE YAPAY ZEKANIN ETKİSİ

Year 2025, Volume: 7 Issue: 1, 44 - 59, 30.06.2025

Tuba Saygın Avsar

Abstract

Yapay zeka (YZ), teşhislerin doğruluğunu artırarak, tedavileri kişiselleştirerek ve operasyonel verimliliği artırarak sağlık hizmetlerinde bir devrim yaratmaktadır. Ekonomik değerlendirmeler, sağlık hizmetleri müdahalelerini değerlendirmek için kritik olup, maliyetleri ve faydaları etkili bir şekilde dengelemek amacıyla giderek daha fazla yapay zeka tabanlı çözümlere odaklanmaktadır. Yüksek başlangıç maliyetleri, belirsiz uzun vadeli sonuçlar ve algoritmaların gelişen doğası bu teknolojilerin değerlendirilmesinde zorluklar ortaya çıkarmaktadır. YZ aynı zamanda büyük verilerin işlenmesine, ekonomik modelleme ve sistematik incelemelerin otomasyonu için dönüştürücü bir analitik araç olarak da hizmet veriyor. Ancak algoritmik önyargı, eşitlik endişeleri ve kaynak kısıtlamaları gibi zorluklar,YZ’yi yerel uzmanlıkla bütünleştiren yaklaşımlara olan ihtiyacı vurgulamaktadır. Çalışma, YZ’nin sağlık ekonomisi ve çıktıları yazınında hem bir müdahale hem de kolaylaştırıcı olarak kullanılmasını iceren ikili rolüne odaklanarak son gelişmelere kapsamlı bir genel bakış sunmaktadır. Etik analizler, sağlam raporlama ve kapasite geliştirme, YZ’nin etkili kaynak tahsisi, sağlık çıktılarını iyileştirme ve ileri teknolojilere adil erişim sağlama potansiyelinden yararlanmak için hayati öneme sahiptir.

Keywords

Sağlık ekonomisi , Yapay Zeka , Ekonomik Değerlendirme , Sağlık Teknolojisi Değerlendirmesi

IMPACT OF ARTIFICIAL INTELLIGENCE ON HEALTH ECONOMIC EVALUATIONS

Abstract

Artificial intelligence (AI) is revolutionising healthcare by enhancing diagnostic accuracy, personalising treatment, and improving operational efficiency. Economic evaluations, critical for assessing healthcare interventions, increasingly focus on AI-based solutions to balance costs and benefits effectively. High initial costs, uncertain longterm outcomes, and the evolving nature of adaptive algorithms challenge the economic evaluation of these technologies. AI also serves as a transformative analytical tool, enabling processing of large datasets, predictive modelling, and automation of systematic reviews. However, algorithmic bias, equity concerns, and resource constraints highlight the need for hybrid approaches, integrating AI with local expertise. The study provides an overview of the recent developments in health economics and outcomes research, focusing on the dual role of AI as both intervention and enabler. Ethical considerations, robust reporting, and capacity building are vital for leveraging AI’s potential to optimise resource allocation, improve health outcomes, and ensure equitable access to advanced technologies.

Keywords

Health Economics , Artificial Intelligence , Economic Evaluation , Health Technology Assessment

References

• Adamson, A. S., & Smith, A. (2018). Machine learning and health care disparities in dermatology. JAMA Dermatology, 154(11), 1247-248.https://doi.org/10.1001/jamadermatol.2018.2347.
• Aung, Y. Y., Wong, D. C., & Ting, D. S. (2021). The promise of artificial intelligence: A review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4–15. https://doi.org/10.1093/bmb/ldaa043.
• Bongurala, A. R., Save, D., Virmani, A., & Kashyap, R. (2024). Transforming health care with artificial intelligence (AI): Redefining medical documentation. Mayo Clinic Proceedings: Digital Health. https://doi.org/10.1016/j.mcpdh.2024.100045.
• Chhatwal, J., Yildirim, I. F., Samur, S., Bayraktar, E., Ermis, T., & Ayer, T. (2024). P28 Development of de novo health economic models using generative AI. Value in Health, 27(12), S7. https://doi.org/10.1016/j.jval.2024.08.013.
• De Vos, J., Visser, L. A., De Beer, A. A., Fornasa, M., Thoral, P. J., & Elbers, P. W. G. (2022). The potential cost-effectiveness of a machine learning tool that can prevent untimely intensive care unit discharge. Value in Health, 25(3), 359–67.
• Dolin, O., Lim, V., Chalmers, K., Hepworth, T., Gonçalves-Bradley, D., Langford, B., & Rinciog, C. (2024). Large Language Models for Health Economics and Health Technology Assessment: A Targeted Review. Value in Health, 27(12), S462.
• Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the economic evaluation of health care programmes (4th ed.). Oxford University Press.
• Elvidge, J., Hawksworth, C., Avşar, T. S., Zemplenyi, A., Chalkidou, A., Petrou, S., & Wilson, E. (2024). Consolidated health economic evaluation reporting standards for interventions that use artificial intelligence (CHEERS-AI). Value in Health. https://doi.org/10.1016/j.jval.2024.09.007
• Fleurence, R., Wang, X., Bian, J., Higashi, M. K., Ayer, T., Xu, H., & Chhatwal, J. (2024a). Generative AI in health economics and outcomes research: A taxonomy of key definitions and emerging applications—An ISPOR working group report. arXiv Preprint. https://doi.org/10.48550/arXiv.2410.20204
• Fleurence, R. L., Bian, J., Wang, X., Xu, H., Dawoud, D., Higashi, M., & Chhatwal, J. (2024b). Generative AI for health technology assessment: Opportunities, challenges, and policy considerations — An ISPOR working group report. Value in Health. https://doi.org/10.1016/j.jval.2024.08.012
• Gandhi, Z., Gurram, P., Amgai, B., Lekkala, S. P., Lokhandwala, A., Manne, S., & Surani, S. (2023). Artificial intelligence and lung cancer: Impact on improving patient outcomes. Cancers, 15(21), 5236. https://doi.org/10.3390/cancers15215236
• Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence- driven healthcare. In T. Cohen & G. Cohen (Eds.), Artificial intelligence in healthcare (pp. 295– 336). Academic Press. https://doi.org/10.1016/B978-0-12-818438-7.00016-4
• Giessen, A., Wilcher, B., Peters, J., Hyde, C., Moons, K. G., de Wit, G. A., & Koffijberg, H. (2014). Health economic evaluation of diagnostic and prognostic prediction models: A systematic review. Value in Health, 17(7), A560. https://doi.org/10.1016/j.jval.2014.08.888
• Gottinger, H. W. (1991). Artificial intelligence and economic modelling. Expert Systems, 8(2), 99– 105. https://doi.org/10.1111/j.1468-0394.1991.tb00435.x
• Hassan, C., Povero, M., Pradelli, L., Spadaccini, M., & Repici, A. (2023). Cost-utility analysis of real-time artificial intelligent-assisted colonoscopy in Italy. Endoscopy International Open. https://doi.org/10.1055/a-2136-3428 Heinz, S., Bondal, E., Kumari, C., and Castellano, G. (2024). HTA141 Artificial Intelligence: Is It a New Era in Payer Decision Making? Value in Health, 27(12), S381.
• Hill, N. R., Sandler, B., Mokgokong, R., Lister, S., Ward, T., & Boyce, R. (2020). Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm. Journal of Medical Economics, 23(4), 386–93. https://doi.org/10.1080/13696998.2019.1706543
• Holmes, T., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022). Artificial intelligence for strengthening healthcare systems in low-and middle-income countries: A systematic scoping review. NPJ Digital Medicine, 5(1), 162. https://doi.org/10.1038/s41746-022-00665-w
• Howell, M. D., Corrado, G. S., & DeSalvo, K. B. (2024). Three epochs of artificial intelligence in healthcare. JAMA, 331(3), 242–244. https://doi.org/10.1001/jama.2023.25902
• Hua, D., Petrina, N., Young, N., Cho, J. G., & Poon, S. K. (2024). Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artificial Intelligence in Medicine, 147, 102698. https://doi.org/10.1016/j.artmed.2023.102698
• Jardim, P. S. J., Rose, C. J., Ames, H. M., Echavez, J. F. M., Van de Velde, S., & Muller, A. E. (2022). Automating risk of bias assessment in systematic reviews: A real-time mixed methods comparison of human researchers to a machine learning system. BMC Medical Research Methodology, 22(1), 167. https://doi.org/10.1186/s12874-022-01620-z
• Jarke, J. & Heuer, H. (2024). Reassembling the black box of machine learning: Of monsters and the reversibility of foldings. In J. Jarke, B. Prietl, S. Egbert, Y. Boeva, H. Heuer, M. Arnold (Eds.). Algorithmic regimes. Methods, interactions, politics. University Press.
• Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., & Oermann, E. K. (2023). Health system-scale language models are all-purpose prediction engines. Nature, 619(7969), 357–362. https://doi.org/10.1038/s41586-023-06498-1
• Kastrup, N., Holst-Kristensen, A. W., & Valentin, J. B. (2024). Landscape and challenges in economic evaluations of artificial intelligence in healthcare: A systematic review of methodology. BMC Digital Health, 2(1), 39. https://doi.org/10.1186/s44229-024-00068-7
• Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, 1–9. https://doi.org/10.1186/s12916-019-1426-2
• Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., & Suri, J. S. (2022). Economics of artificial intelligence in healthcare: Diagnosis vs. treatment. In Healthcare (Vol. 10, No. 12, p. 2493). MDPI. https://doi.org/10.3390/healthcare10122493
• Landschaft, A., Antweiler, D., Mackay, S., Kugler, S., Rüping, S., Wrobel, S., & Allende-Cid, H. (2024). Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews. International Journal of Medical Informatics, 189, 105531. https://doi.org/10.1016/j.ijmedinf.2023.105531
• Liu, M., Li, S., Yuan, H., Ong, M. E. H., Ning, Y., Xie, F., & Liu, N. (2023). Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine, 142, 102587. https://doi.org/10.1016/j.artmed.2023.102587
• López, D. M., Rico-Olarte, C., Blobel, B., & Hullin, C. (2022). Challenges and solutions for transforming health ecosystems in low-and middle-income countries through artificial intelligence. Frontiers in Medicine, 9, 958097. https://doi.org/10.3389/fmed.2022.958097
• Luo, X., Chen, F., Zhu, D., Wang, L., Wang, Z., Liu, H., & Chen, Y. (2024). Potential roles of large language models in the production of systematic reviews and meta-analyses. Journal of Medical Internet Research, 26, e56780. https://doi.org/10.2196/56780
• Mainz, J., Munch, L., & Bjerring, J. C. (2024). Cost-effectiveness and algorithmic decision-making. AI and Ethics, 1–13. https://doi.org/10.1007/s43681-024-00223-0
• Marey, A., Arjmand, P., Alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024). Explainability, transparency and black box challenges of AI in radiology: Impact on patient care in cardiovascular radiology. Egyptian Journal of Radiology and Nuclear Medicine, 55(1), 183.
• Mittermaier, M., Raza, M., & Kvedar, J. C. (2023). Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digital Medicine, 6(1), 113.
• Morgenstern, J. D., Buajitti, E., O'Neill, M., Piggott, T., Goel, V., Fridman, D., & Rosella, L. C. (2020). Predicting population health with machine learning: A scoping review. BMJ Open, 10(10), e037860. https://doi.org/10.1136/bmjopen-2020-037860
• National Institute for Health and Care Excellence [NICE]. (2024). Use of AI in evidence generation: NICE position statement v.1. Retrieved from https://www.nice.org.uk/about/what-we-do/our- research-work/use-of-ai-in-evidence-generation--nice-position-statement
• O'Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., & Ananiadou, S. (2015). Using text mining for study identification in systematic reviews: a systematic review of current approaches. Systematic Reviews, 4, 1-22. https://doi.org/10.1186/2046-4053-4-5
• Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024). Global regulatory frameworks for the use of artificial intelligence (AI) in the healthcare services sector. Healthcare, 12(5), 562.
• Pandey, S., Kaur, R., Teitsson, S., Malcolm, B., Rai, P., Singh, B., & Klijn, S. (2024). EE494 AI- Driven Virtual Assistance Interface for Excel-Based Economic Model. Value in Health, 27(12), S153.
• Panch, T., Mattie, H., & Atun, R. (2019). Artificial intelligence and algorithmic bias: implications for health systems. Journal of Global Health, 9(2).
• Ratwani, R. M., Sutton, K., & Galarraga, J. E. (2024). Addressing AI algorithmic bias in health care. JAMA, 332(13), 1051-1052.
• Rawlinson, W., Klijn, S., Teitsson, S., Malcolm, B., Gimblett, A., & Reason, T. (2024). P48 Automating Economic Modelling: Potential of Generative AI for Updating Excel-Based Cost- Effectiveness Models. Value in Health, 27(6), S11.
• Reason, T., Rawlinson, W., Langham, J., Gimblett, A., Malcolm, B., & Klijn, S. (2024). Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models. PharmacoEconomics-Open, 8(2), 191-203.
• Robinson, A., Thorne, W., & Wu, B. P. (2023). Bio-sieve: Exploring instruction tuning large language models for systematic review automation. arXiv preprint arXiv:230806610.
• Schwendicke, F., Rossi, J. G., Göstemeyer, G., Elhennawy, K., Cantu, A., & Gaudin, R. (2021). Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection. Journal of Dental Research, 100, 369–76.
• Sele, D. & Chugunova, M. (2024). Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making. PLoS One, 19(2), e0298037.
• Srivastava, T., Swami, S., & Tong, T. (2024). EE504 Leveraging Large Language Models for Conceptualizing Health Economic Models: A Feasibility Study in Oncology. Value in Health, 27(12), S155.
• Sundaram, G. & Berleant, D. (2023). Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review, in Proceedings of the Eighth International Congress on Information and Communication Technology (ICICT 2023), 1:73-92, Springer, https://doi.org/10.1007/978-981-99-3243-6
• Swami, S. & Srivastava, T. (2024). Can Gen-AI Assist in Interpreting the Health Economic Model Results as Per Target Audience? Value in Health, 27(12), S483.
• Tufail, A., Kapetanakis, V. V., & Salas-Vega, S. (2016). An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technology Assessment, 20(92), 1–72.
• Velichkovska, B., Gjoreski, H., Denkovski, D., Kalendar, M., Mullan, I. D., Gichoya, J. W., & Osmani, V. (2023). AI learns racial information from the values of vital signs. medRxiv, 2023- 12.
• Vithlani, J., Hawksworth, C., Elvidge, J., Ayiku, L., & Dawoud, D. (2023). Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting. Frontiers in Pharmacology, 14, 1220950.
• Voets, M. M., Veltman, J., Slump, C. H., Siesling, S., & Koffijberg, H. (2022). Systematic review of health economic evaluations focused on artificial intelligence in healthcare: the tortoise and the cheetah. Value in Health, 25(3), 340-349.
• Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2020). The economic impact of artificial intelligence in health care: systematic review. Journal of Medical Internet Research, 22(2), e16866.
• Xie, Y., Nguyen, Q. D., Hamzah, H., Lim, G., Bellemo, V., & Gunasekeran, D. V. (2020). Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. The Lancet Digital Health, 2(5), e240–9.
• Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of artificial intelligence applications in real-life clinical practice: systematic review. Journal of Medical Internet Research, 23(4), e25759.
• Zemplényi, A., Tachkov, K., Balkanyi, L., Németh, B., Petykó, Z. I., Petrova, G., Czech, M., Dawoud, D., Goettsch, W., Gutierrez Ibarluzea, I., Hren, R., Knies, S., Lorenzovici, L., Maravic, Z., Piniazhko, O., Savova, A., Manova, M., Tesar, T., Zerovnik, S., & Kaló, Z. (2023). Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment. Frontiers in Public Health, 11, 1088121. https://doi.org/10.3389/fpubh.2023.1088121.