Integrated fuzzy MCDM framework for evaluating ai-assisted healthcare delivery models for aging populations

The rapid development of AI offers us an unprecedented opportunity to revolutionize healthcare delivery for older populations but the sustainability implications of such progress are largely unclear. The present paper uses an integrated fuzzy multi-criteria decision-making framework to assess seven AI-driven healthcare delivery models against 14 sustainability criteria from environmental, social, economic and technological. A hybrid weighting model that combines ANP and entropy methods, with two approaches for quantifying subjective expert judgments and objective data variability, is proposed to generate robust criterion weights. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied in a fuzzy environment to rank the alternatives relative to an ideal sustainability performance. This study used a real-life case study from a hospital in Lagos, Nigeria, to demonstrate the framework's applicability. Four experts in AI implementation in healthcare systems provided information for the framework evaluation. The results obtained showed that energy consumption was the least important criterion for the current selection problem. Patient privacy and security were considered the most important criterion for the AI-assisted healthcare delivery models. The results of this study showed that telemedicine with AI chatbot was the most suitable alternative for the selection problem under the environmental and technical criteria. Robotic assistance with AI companions and AI-powered community health hub were the most suitable alternatives for this problem in terms of economic and social criteria, respectively. The findings of this study show that MCDM frameworks are robust models for addressing decision-making problems in healthcare systems.