Preface to the special issue on “Artificial Intelligence‐driven Decision Making in Health and Medicine”

We are pleased to present this special issue of International Transactions in Operations Research, titled “Artificial Intelligence-Driven Decision Making in Health and Medicine.” As guest editors, we have had the privilege of overseeing a collection of innovative research that highlights the transformative impact of artificial intelligence (AI) and decision making (DM) in the healthcare sector. Artificial intelligence is revolutionizing decision-making processes in health and medicine, offering new avenues for enhancing patient care, optimizing operational efficiencies, and improving health outcomes. This special issue seeks to highlight innovative methodologies and insights that illustrate the current landscape of AI applications in healthcare and medicine, with a particular emphasis on the integration of artificial intelligence and decision-making. We thank all contributors for their work and dedication. Each submission underwent a rigorous peer-review process, ensuring that only the highest quality research is presented here. After careful consideration, we are proud to include five articles that exemplify the diverse applications of AI in healthcare. The first article, “Digital health at Central Lisbon University Hospital Center: Strategic reflections and value proposition,” provides a comprehensive analysis of digital health initiatives and their strategic implications within a prominent healthcare institution. The second article, “Using interpretive structural modeling (ISM) to detect and define initiatives that facilitate hemodynamic laboratory management,” employs ISM to identify critical initiatives aimed at improving the management of hemodynamic laboratories, emphasizing a structured approach to decision-making. In the third article, “A multi-objective transportation model for COVID-19 patients: Lessons learned from France,” the authors present a novel transportation model designed to optimize the allocation and movement of COVID-19 patients, drawing valuable lessons from the French healthcare response. The fourth article, “Combining convolutional neural networks with long-short time memory layers to predict Parkinson's disease progression,” explores advanced machine learning techniques to forecast the progression of Parkinson's disease, showcasing the potential of AI in neurology. Finally, the article titled “Robust solutions via optimisation and predictive process monitoring for the scheduling of interventional radiology procedures” discusses the integration of optimization methods and predictive monitoring to enhance the scheduling efficiency of interventional radiology, highlighting the practical applications of AI in operational workflows. We believe that the contributions in this special issue will inspire further research and collaboration in the field of AI-driven decision-making in healthcare and medicine.