AI-ASSISTED MULTI-CRITERIA MODEL FOR ASSESSING THE EFFECTIVENESS OF INFORMATION TECHNOLOGIES IN MEDICINE

• Vaxidov Inomjon Ilxamovich

  Author

• Maxsudov Moxirbek Tolibjonovich

  Author

The rapid integration of artificial intelligence (AI) into healthcare has transformed the design and evaluation of medical information technologies (IT). Yet, systematic and objective methods for assessing the effectiveness of these technologies remain limited. This study introduces a multi-criteria decision-making (MCDM) model enhanced by AI to evaluate IT effectiveness in medicine. By merging analytic hierarchy process (AHP), fuzzy logic, and machine-learning optimization, the model quantifies both tangible (cost, accuracy, interoperability) and intangible (user satisfaction, ethical compliance, transparency) dimensions.

Amann, J., Blasimme, A., Vayena, E., & Frey, D. (2020). Explainability for AI in healthcare: A multidisciplinary perspective. BMC Medical Ethics, 21(1), 241–247.

Basak, S., & Gomez, E. (2022). Investment assessment in digital health technologies. Health Policy and Technology, 11(4), 441–450.

Chen, J., Li, Z., & Zhou, W. (2023). Hybrid decision model for medical IT evaluation using fuzzy AHP and deep learning. Computers in Biology and Medicine, 154, 105458.

Dursun, M., & Karsak, E. (2020). Fuzzy multi-criteria group decision approach for healthcare system selection. Expert Systems with Applications, 144, 113127.

Esteva, A., et al. (2021). Deep learning-enabled medical computer vision. Nature Medicine, 27, 1122–1133.

Floridi, L., Cowls, J., Beltrametti, M., et al. (2022). AI4People—An ethical framework for a good AI society. Minds and Machines, 32(2), 87–98.

Haider, R., Javed, M., & Khan, A. (2022). Measuring effectiveness of health information systems in hospitals. International Journal of Medical Informatics, 159, 605–613.

Horgan, D., et al. (2023). Digital health transformation in European medicine. Frontiers in Digital Health, 5, 118.

Jobin, A., Ienca, M., & Vayena, E. (2019). Global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.

Lee, S., Choi, Y., & Park, J. (2021). Socio-technical evaluation framework for hospital information systems. Health Informatics Journal, 27(2), 210–220.

Liu, T., Wang, Y., & Zhao, Q. (2023). Dynamic evaluation frameworks for medical information systems using adaptive AI. IEEE Access, 11, 60–73.

Nasiri, M., Zolfani, S., & Lee, K. (2022). Hybrid MCDM models for assessing digital healthcare technologies. Technological Forecasting and Social Change, 183, 303–310.

Singh, R., & Malik, P. (2023). Integrating fuzzy logic and AI for healthcare technology evaluation. Expert Systems with Applications, 213, 119352.

Triantaphyllou, E. (2021). Multi-criteria decision-making methodologies: A comparative study. Springer.

Wang, L., Tian, S., & Zhao, D. (2022). AI-assisted performance assessment in hospital information systems. Health Care Management Science, 25, 85–92.

Zhang, H., & Pinto, A. (2023). Evaluating the success of medical IT projects in emerging markets. Information Systems Frontiers, 25(1), 70–81.

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