Conceptual model for assessing the economic efficiency of AI solutions in healthcare

Aim. The work aimed to develop a conceptual model for assessing the economic efficiency of using artificial intelligence (hereinafter referred to as AI) technologies in healthcare, which includes taking into account not only direct costs, but also indirect, hidden, and transactional ones.
Objectives. The work seeks to consider the key fields of AI impact on the healthcare system with an emphasis on identifying the main effects and costs of its implementation; to develop a methodological approach for a multi-level and comprehensive economic assessment of AI solutions both at the level of individual medical institutions and on the scale of the state healthcare system.
Methods. The methodological basis was the analysis of scientific publications for 2020–2024 and a systemic analysis of the effects and costs of AI implementation. The study employed interdisciplinary and institutional approaches to integrate various aspects of the impact of AI and form a universal evaluation model, not just one that takes into account economic efficiency.
Results. A conceptual model was developed, comprising the short-term and long-term effects of AI implementation in the clinical, organizational, economic, social, scientific, and regulatory spheres. A classification of costs was proposed, which includes four groups (direct, indirect, hidden, and transactional). The model is adapted to the level of analysis and can be used for a comparable assessment of the economic efficiency of AI solutions.
Conclusions. Creating a sustainable and objective system for assessing the economic efficiency of AI in healthcare requires taking into account the entire range of effects and costs. Ignoring hidden and transaction costs can lead to distorted forecasts and a decrease in the assessment of effectiveness of the solutions being implemented. The developed model represents a universal tool for supporting strategic decisions at the level of institutions and government agencies, and it can also serve as a basis for further development of methods for assessing digital technologies in medicine.

1. Lapidus L.V., Tokareva O.M. Developing a taxonomy of decisions based on artificial intelligence technologies in health care practices. Ekonomika i upravlenie = Economics and Management. 2024;30(7):819-831. (In Russ.). https://doi.org/10.35854/1998-1627-2024-7-819-831

2. Gomez Rossi J., Rojas-Perilla N., Krois J., Schwendicke F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Network Open. 2022;5(3):e220269. https://doi.org/10.1001/jamanetworkopen.2022.0269

3. Kazemzadeh S., Yu J., Jamshy S., et al. Deep learning detection of active pulmonary tuberculosis at chest radiography matched the clinical performance of radiologists. Radiology. 2023;306(1):124-137. https://doi.org/10.1148/radiol.212213

4. Wang Y., Liu C., Hu W., et al. Economic evaluation for medical artificial intelligence: Accuracy vs. cost-effectiveness in a diabetic retinopathy screening case. NPJ Digital Medicine. 2024;7:43. https://doi.org/10.1038/s41746-024-01032-9

5. Wu H., Jin K., Yip C.C., Koh V., Ye J. A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality. Survey of Ophthalmology. 2024;69(4):499-507. https://doi.org/10.1016/j.survophthal.2024.03.008

6. Areia M., Mori Y., Correale L., et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: A modelling study. The Lancet Digital Health. 2022;4(6):e436–e444. https://doi.org/10.1016/S2589-7500(22)00042-5

7. Luz A., Gimah M. AI-driven early detection systems for chronic diseases. Preprints.org. 2025. https://doi.org/10.20944/preprints202501.2144.v1

8. Global health estimates: Leading causes of death. World Health Organization (WHO). URL: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death (accessed on 12.02.2025).

9. Khanna N.N., Maindarkar M.A., Viswanathan V., et al. Economics of artificial intelligence in healthcare: Diagnosis vs. treatment. Healthcare. 2022;10(12):2493. https://doi.org/10.3390/healthcare10122493

10. Sahni N., Stein G., Zemmel R., Cutler D.M. The potential impact of artificial intelligence on healthcare spending. NBER Working Paper. 2023;(30857). URL: https://www.nber.org/system/files/working_papers/w30857/w30857.pdf (accessed on 12.02.2025).

11. Vithlani J., Hawksworth C., Elvidge J., Ayiku L., Dawoud D. Economic evaluations of ar- tificial intelligence-based healthcare interventions: A systematic literature review of best practices in their conduct and reporting. Frontiers in Pharmacology. 2023;14:1220950. https://doi.org/10.3389/fphar.2023.1220950

12. van Leeuwen K.G., Meijer F.J.A., Schalekamp S., et al. Cost-effectiveness of artificial intel- ligence aided vessel occlusion detection in acute stroke: An early health technology assessment. Insights into Imaging. 2021;12(1):133. https://doi.org/10.1186/s13244-021-01077-4

13. Al Meslamani A.Z. Beyond implementation: The long-term economic impact of AI in healthcare. Journal of Medical Economics. 2023;26(1):1566-1569. https://doi.org/10.1080/13696998.2023.2285186

14. Lukichyov P.M., Chekmarev O.P. The economics of artificial intelligence: Opportunities and problems of its application in healthcare. Voprosy innovatsionnoi ekonomiki = Russian Journal of Innovation Economics. 2022;12(2):1111-1130. (In Russ.). https://doi.org/10.18334/vinec.12.2.114782

15. Merhi M.I. An evaluation of the critical success factors impacting artificial intelligence implementation. International Journal of Information Management. 2023;69:102545. https://doi.org/10.1016/j.ijinfomgt.2022.10254516. Bharadwaj P., Nicola L., Breau-Brunel M., et al. Unlocking the value: Quantifying the return on investment of hospital artificial intelligence. Journal of the American College of Radiology. 2024;21(10):1677-1685. https://doi.org/10.1016/j.jacr.2024.02.034

16. Hajri O. The hidden costs of AI: Decolonization from practice back to theory. In: Thiel S., Bernhardt J.C., eds. AI in museums: Reflections, perspectives and applications. Bielefeld: Transcript Publishing; 2023:57-64. https://doi.org/10.1515/9783839467107-006

17. Coase R.H. The nature of the firm. Economica. 1937;4(16):396-405. https://doi.org/10.1111/j.1468-0335.1937.tb00002.x

18. North D.C. Institutions, institutional change and economic performance. Cambridge: Cambridge University Press; 1990. 152 p.

19. Williamson O.E. The economic institutions of capitalism: Firms, markets, relational contracting. New York, NY: The Free Press; 1985. 450 p.

20. Liu C.-F., Huang C.-C., Wang J.-J., Kuo K.-M., Chen C.-J. The critical factors affecting the deployment and scaling of healthcare AI: Viewpoint from an experienced medical center. Healthcare. 2021;9(6):685. https://doi.org/10.3390/healthcare9060685

21. Alnasser B. A review of literature on the economic implications of implementing artificial intelligence in healthcare. E-Health Telecommunication Systems and Networks. 2023;12(3):35-48. https://doi.org/10.4236/etsn.2023.123003

22. Wolff J., Pauling J., Keck A., Baumbach J. The economic impact of artificial intelligence in health care: Systematic review. Journal of Medical Internet Research. 2020;22(2):e16866. https://doi.org/10.2196/16866