AI-ASSISTED MULTI-CRITERIA MODEL FOR ASSESSING THE EFFECTIVENESS OF INFORMATION TECHNOLOGIES IN MEDICINE
Keywords:
Artificial intelligence, multi-criteria decision-making, medical informatics, healthcare assessment, information technologies, fuzzy logic, AHP, data analytics, machine learning, digital medicine.Abstract
In recent years, the integration of artificial intelligence (AI) and data analytics into medical information systems has redefined healthcare efficiency assessment frameworks. The present study develops an AI-assisted multi-criteria decision-making (MCDM) model designed to evaluate the effectiveness of information technologies (IT) in medical contexts. This model combines quantitative metrics (system performance, data accuracy, cost-efficiency) and qualitative indicators (user satisfaction, ethical compliance, adaptability) through AI-driven optimization. Using a hybrid approach that merges the Analytic Hierarchy Process (AHP) with fuzzy inference and machine learning-based sensitivity analysis, the study demonstrates how AI can minimize human bias and enhance reliability in complex decision environments.
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References
Alotaibi, Y., & Federer, L. (2020). Evaluating health information technology: Challenges and opportunities. Journal of Medical Systems, 44(3), 233–245.
Amann, J., Blasimme, A., Vayena, E., & Frey, D. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Ethics, 21(1), 241–247.
Carayon, P., & Hoonakker, P. (2021). Human factors and usability for health information technology: Old problems and new challenges. Yearbook of Medical Informatics, 30(1), 210–222.
Chen, J., Li, Z., & Zhou, W. (2023). A 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). A fuzzy multi-criteria group decision-making 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, 1122–1132.
European Commission. (2021). Ethics guidelines for trustworthy AI. Brussels: EC Publications.
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.
Garcia, F., Kim, Y., & Lopez, A. (2023). AI-enhanced multi-criteria assessment for hospital digital transformation. Health Informatics Journal, 29(1), 120–131.
Heeks, R. (2020). Health information systems: Failure, success, and improvisation. Information Technology for Development, 26(1), 96–109.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.
Kumar, S., & Zhang, M. (2021). AI in healthcare decision making: From support to autonomy. Artificial Intelligence in Medicine, 121, 102190.
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.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 134–143.
Raimo, N., Vitolla, F., & Rubino, M. (2021). Digital transformation and healthcare performance. Technovation, 110, 104365.
Shamsuzzaman, M., Hossain, A., & Park, J. (2022). Reducing bias in healthcare decision-making through AI-assisted MCDM. Computers & Industrial Engineering, 165, 164–172.
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, pp. 27–45.
Wang, L., Tian, S., & Zhao, D. (2022). AI-assisted performance assessment in hospital information systems. Health Care Management Science, 25, 85–92.
