This work proposes integrating and advancing LLM and optimization modeling to empower organizational decision-makers to model and solve such complex problems without requiring deep expertise in optimization, thereby enabling much more widespread improved decision-making and increasing by orders of magnitude the benefits AI and OR can bring to enterprises and society.
This review examines current applications of LLMs to health economic modeling in terms of reproducibility, validation, adaptability, and technology readiness and concludes that near-term gains are augmenting human modelers on decomposed, verifiable sub-tasks rather than pursuing autonomous end-to-end modeling.
Attila Imre, B. Németh, Á. Jóźwiak et al.· Expert review of pharmacoeco...· 0 citations
An integrated framework that connects DSS evolution, architecture, organisational conditions, risks, and future development is contributed, to explain how multiple analytical perspectives can be generated, integrated, assessed, implemented, and refined through feedback.
Emeka Eze, F. Bello, Olusegun Akinyemi· Data Intelligence and Inform...· 0 citations
A systematic literature review analyzing 111 papers from scientific databases on integrating AI with MCDM/A methods offers both a conceptual consolidation for scholars and a structured foundation for the design of next-generation, human-centered intelligent decision support systems (IDSS).
Bruno Cicciú, E. Frej, A. D. de Almeida· IMA Journal of Management Ma...· 1 citation
The study suggests that explainable AI is a crucial factor in building intelligent, reliable, accountable, transparent, and effective AI-powered systems that can be deployed in realistic environments for decision making.
Kirankumar Pundlik Mohurle, S. Sahare, Yugant Rupesh Dhoke et al.· International Journal of Eng...· 0 citations
Uncertainty reasoning and quantification play a critical role in decision-making across various domains, prompting increased attention from both academia and industry. As real-world applications become more complex and data-driven, effectively handling uncertainty becomes paramount for accurate and reliable decision-ma...
Xujiang Zhao, Chen Zhao, Feng Chen et al.· Proceedings of the 32nd ACM...· 0 citations
Deploying artificial intelligence systems for medical image analysis in clinical settings involves considerations that go beyond model accuracy: infrastructure constraints, integration with existing workflows, and generalization across patient populations all determine whether a system works outside a research lab. Thi...
Humberto J. Navarro, M. S. González, Nubia Y. Piñeros et al.· Journal of Intelligent Decis...· 0 citations
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