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
Decision Support Systems (DSS) have evolved from stand-alone model-based tools into integrated, data-intensive, and increasingly intelligent systems that assist decision makers in healthcare, finance, supply chains, public administration, and other complex settings. This review article presents a structured integrative synthesis of foundational and contemporary DSS literature. The review examines conceptual origins, major taxonomies, architectural components, domain applications, implementation constraints, and emerging research directions. The synthesis shows that modern DSS combine data management, analytical models, knowledge representation, collaborative functions, and artificial intelligence, but their effectiveness remains contingent on data quality, organisational fit, explainability, interoperability, cybersecurity, and meaningful human oversight. Rather than comparing heterogeneous studies through unsupported aggregate performance statistics, this review evaluates evidence according to study context, DSS function, reported outcome, and methodological limitations. A divergent-convergent process model is presented to explain how multiple analytical perspectives can be generated, integrated, assessed, implemented, and refined through feedback. The review identifies priority directions in real-time adaptive support, interoperable architectures, explainable and accountable AI, longitudinal human-factor evaluation, and governance of increasingly autonomous decision systems. The article contributes an integrated framework that connects DSS evolution, architecture, organisational conditions, risks, and future development.
Emeka J. Eze, F. Bello, Olusegun Akinyemi· Data Intelligence and Inform...· 0 citations
Artificial Intelligence (AI) has advanced significantly in the 21st century, evolving into a crucial tool for decision-making. A prominent trend is its integration with Multi-Criteria Decision-Making/Aiding (MCDM/A) methods to support complex decisions across diverse engineering domains. This paper presents a systematic literature review analyzing 111 papers from scientific databases on integrating AI with MCDM/A methods. Unlike prior reviews that primarily catalogue methods or hybrid techniques, this study introduces a socio-technical analytical framework comprising three layers—technical configurations, functional mechanisms, and human–AI collaboration patterns—to explain how and why AI reshapes multicriteria decision processes. The findings reveal recurrent architectural patterns, identify dominant functional roles of AI across decision-process phases, and uncover an emerging shift from automation-oriented systems toward augmentation-based decision support. Rather than providing a purely descriptive mapping of the literature, this review undertakes an investigative task guided by a socio-technical framework. By examining how structural configurations of AI–MCDM/A integration reshape the stages of the decision process and redistribute roles between humans and AI, the study moves beyond cataloguing techniques to uncover underlying integration logics, structural tensions, and developmental trajectories. In doing so, it 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...· 0 citations
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-making. This workshop focuses on the critical topics of uncertainty reasoning and quantification in decision making. It provides a platform for experts and researchers from diverse backgrounds to exchange ideas on cutting-edge techniques and challenges in this field. The interdisciplinary nature of uncertainty reasoning and quantification, spanning artificial intelligence, machine learning, statistics, risk analysis, and decision science, will be explored. The workshop aims to address the need for robust and interpretable methods for modeling and quantifying uncertainty, fostering reasoned decision-making in various domains. Participants will have the opportunity to share research findings and practical experiences, promoting collaboration and advancing decision-making practices under uncertainty.
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. This paper examines how computer vision architectures and large language models can be combined into a unified decision-support system for early detection of diseases through medical image analysis. Through a systematic review of 25 recent studies published between 2022–2025, the work develops an engineering-oriented taxonomy of architectures such as VGG16, DeiT, GPT-4 and LLaMA2, evaluating models against criteria including computational complexity, scalability, dataset dependency and deployment feasibility in low-resource environments. The evidence indicates that combining automated image analysis with LLM-based decision support can improve diagnostic accuracy and lower screening costs, with classification metrics exceeding 90% in controlled settings, contributing to the fulfillment of the Sustainable Development Goals. However, real-world deployment remains constrained by hardware requirements, interoperability gaps and dataset bias that limit generalization across diverse populations. This analysis provides concrete engineering guidelines for the design, validation and scalable implementation of AI systems in medical image analysis clinical workflows.
Humberto J. Navarro, M. S. González, Nubia Y. Piñeros et al.· Journal of Intelligent Decis...· 0 citations
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