# Exploring Computational Methods to Advance Clinical Decision Making
###### Abstract  
The papers comprising our Special Issue on advanced decision making in clinical medicine (which is part of the series in applied bioscience and bioengineering) were conceived and written during a most challenging time for medical science and its delivery--the COVID-19 pandemic. Particularly in the pre-vaccine period, terms and concepts which are normally reserved for specialist journals were, for the first time, the parlance of the daily news. Public health officials and practitioners were called on to explain their technical frameworks and jargon to a wide audience. For most, it was a revelation to learn of the highly mathematical nature of modern medical practice. For many, this was reassuring, and for others, the message was foreign and fueled distrust. Recent developments in artificial intelligence, data science, and statistics, however, have the potential to enhance the quality of medical decision making and bolster public confidence in the value of the offered solutions. There is a communication gap that is as much the responsibility of scientists and practitioners as it is of the media and politicians. The landscape of clinical decision making is transforming, driven by the convergence of data science, systems engineering, cognitive analytics, and machine learning. As the complexity and volume of patient data expand thanks to genomic, imaging, sensor-derived, and electronic health record sources, the demand for robust, explicable, and adaptive decision-support tools has intensified. The challenge to combine, for the best effect, the knowledge of healthcare professionals (both frontline and their scientist and technical colleagues) with patient preferences, values and risk tolerance, has never been more pressing. The role of decision theory and probabilistic reasoning has re-emerged in clinical contexts. Shared decision making frameworks, supported by Bayesian models and utilities elicited from patients, are facilitating more personalized and ethically grounded treatment choices [1]. Simulation-based methods have also gained traction. Studies apply agent-based models, system dynamics, and discrete-event simulations to optimize clinical pathways and resource allocation, especially in critical care and pandemic response scenarios [2]. Decision support also requires understanding causality. Advances in causal inference from observational data--through frameworks such as counterfactual reasoning [3], targeted maximum likelihood estimation (TMLE), and instrumental variable methods--enable clinicians to assess treatment effects more robustly in the absence of randomized trials [4]. Recent research has progressed to embed artificial intelligence (AI) in diagnostic and prognostic workflows. For example, ref. [5] demonstrated the potential of deep learning to match or exceed dermatologist-level accuracy in skin cancer classification. The study [6]advocated for a human-centered AI approach that enhances clinician judgment rather than replacing it. At the same time, explainable AI (XAI) approaches, as outlined in [7], have emerged as critical tools to improve transparency and trust in clinical environments, particularly in high-stakes decisions. Another trend is the use of reinforcement learning (RL) as a decision-theoretic framework for sequential treatment planning, especially in chronic and critical care. The study referenced in [8] demonstrated how off-policy RL could recommend vasopressor and fluid management strategies for septic patients in the ICU, often aligning with expert decisions. Building on this, ref. [9] highlighted the challenges of implementing RL in healthcare, considering aspects of safety, fairness, and interpretability. In this Special Issue we present state-of-the-art contributions that examine how artificial intelligence, data science, and statistics can improve and enhance effective medical decision making and evidence-based medicine.