Economic efficiency of diagnostics using artificial intelligence-assisted patient routing: an evaluation method

Objective: To develop and validate a method for evaluating the economic efficiency of target disease (TD) diagnostics performed via artificial intelligence (AI)-assisted multi-stage patient routing.

Material and methods. The evaluation method was developed through a simulation of two diagnostic routing scenarios (with and without AI program output) based on data from 381 patients with malignant and benign skin neoplasms. This approach was validated using output of the Derma Onko Check AI program, employing previously proposed diagnostic algorithms for melanocytic skin tumors (n=230) at a 62% routing threshold. Formulas were derived to calculate the financial cost (FC) ratio, the cost of identifying one TD case, and coefficients for avoidable and potential avoidable costs to enable mapping within a quadrant matrix. The evaluation method factors in not only the avoidable costs of medical interventions but also the potential avoidable costs (losses) resulting from delayed TD detection.

Results. The implementation of diagnostic algorithms based on the output of the Derma Onko Check AI program demonstrated high economic efficiency. The FC ratio of 0.49 indicates a 51% reduction in the total FCs for melanocytic skin tumors compared to conventional diagnostics. The analysis of avoidable and potential avoidable costs revealed a 59.0% decrease in avoidable costs (R_TC_AC=0.41) and a 51.0% decrease in potential avoidable costs (R_TC_PAC=0.49). These results fall within the optimal efficiency zone of the quadrant matrix.

Conclusion. The obtained results validate factoring missed-case treatment costs into both parts of the R_TC formula. Thisensures accurate comparability of diagnostic approaches with different FC structures and clinical outcomes.

1. Wu W.T., Chao Y.W., Lin T.K., et al. Economic evaluation of AI-assisted technologies in healthcare: a systematic review. J Food Drug Anal. 2025; 33 (4): 487–500. https://doi.org/10.38212/2224-6614.3570.

2. Omelyanovskiy V.V., Gorkavenko F.V., Ryagina V.A., et al. Basic approaches to assessment of digital health products and services in the Russian Federation. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2025; 18 (4): 473–82 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.349.

3. Korabelnikov D.I., Lamotkin A.I. A method for evaluating diagnostic effectiveness using algorithms based on opinion obtained from artificial intelligence models. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2026; 19 (1): 79–91 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2026.355.

4. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Preliminary differential diagnosis of benign and malignant tumors from epidermal skin tissue using an artificial intelligence program “Derma Onko Check”. Current Problems of Health Care and Medical Statistics. 2025; 2: 223–42 (in Russ.). https://doi.org/10.24412/2312-2935-2025-2-223-242.

5. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A., et al. Melanoma check the accuracy of the preliminary diagnosis of malignant melanocytic skin tumors using the artificial intelligence program “Melanoma Check”. Medical Bulletin of the Main Military Cinical Hospital named after N.N. Burdenko. 2025; 1: 42–51 (in Russ.). https://doi.org/10.53652/2782-1730-2025-6-1-42-51.

6. De Bedout V., Williams N.M., Muñoz A.M., et al. Skin cancer and dermoscopy training for primary care physicians: a pilot study. Dermatol Pract Concept. 2021; 11 (1): e2021145. https://doi.org/10.5826/dpc.1101a145.

7. Neretin E.Yu., Titov K.S., Zapirov G.M. Primary early diagnosis of skin melanoma after individual training of doctors. Russian Journal of Clinical Dermatology and Venereology. 2023; 22 (1): 99–105 (in Russ.). https://doi.org/10.17116/klinderma20232201199.

8. Chen J.Y., Fernandez K., Fadadu R.P., et al. Skin cancer diagnosis by lesion, physician, and examination type: a systematic review and meta-analysis. JAMA Dermatol. 2025; 161 (2): 135–46. https://doi.org/10.1001/jamadermatol.2024.4382.

9. Lamotkin A.I., Korabelnikov D.I., Olisova O.Yu., Lamotkin I.A. Patient routing algorithm when using the artificial intelligence program “Derma Onko Check” for differential diagnosis of skin neoplasms. Current Problems of Health Care and Medical Statistics. 2025; 5: 139–59 (in Russ.). https://doi.org/10.24412/2312-2935-2025-5-139-159.

10. Korabelnikov D.I., Lamotkin A.I. Patient routing algorithm in differential diagnosis of cutaneous neoplasms with the combined use of Derma Onko Check and Melanoma Check artificial intelligence software tools. Clinical Review for General Practice. 2025; 6 (12): 71–9 (in Russ.). https://doi.org/10.47407/kr2025.6.11.00715.

11. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Patient routing algorithm for differential diagnosis of skin neoplasms using the artificial intelligence program “Melanoma Check”. Medical Bulletin of the Main Military Cinical Hospital named after N.N. Burdenko. 2025; 4: 6–13 (in Russ.). https://doi.org/10.53652/2782-1730-2025-6-4-06-13.

12. Kaprin A.D., Starinskiy V.V., Shakhzadova A.O. (Eds.) The state of oncological care for the population of Russia in 2024. Мoscow: Herzen Moscow Research Institute of Oncology – branch of National Medical Research Center for Radiology; 2025: 275 pp. (in Russ.).

13. Babenko A.I., Pushkarev O.V. Methodological basis of the complex estimation of medical-economic efficiency of public health services. Bulletin of the Siberian Branch of the Russian Academy of Medical Sciences. 2014; 34 (2): 89–94 (in Russ.).

14. Krasnova L.S., Arkova E.S., Luchinin E.A., Kholovnya-Voloskova M.A. Methodological recommendations for organizing and conducting clinical and economic analysis of medical devices. Мoscow: Research Institute of Healthcare Organization and Medical Management; 2022: 60 pp. (in Russ.).

15. Marisov L.V., Kobyatskaya E.E., Butareva M.M., Metsger A.V. Medical and economic analysis of the effectiveness of the structural units of a medical organization. Problems of Social Hygiene, Public Health and History of Medicine. 2022; 30 (5): 853–8 (in Russ.). https://doi.org/10.32687/0869-866X-2022-30-5-853-858.