ABSTRACT. The huge amount of information that needs to be assimilated in order to keep pace with the continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications. This article provides an overview of the underlying design and functionality of radiological decision support systems with examples tracing the development and evolution of such systems over the past 40 years. More importantly, we discuss the specific design, performance and usage characteristics that previous systems have highlighted as being necessary for clinical uptake and routine use. Additionally, we have identified particular failings in our current methodologies for data dissemination within the medical domain that must be overcome if the next generation of decision support systems is to be implemented successfully. What is a decision support system? Decision support systems (DSS) are a set of manual or computer-based tools that assist in some decision-making activity. In today's information-driven environment, DSS are commonly understood to be a variety of computerised information management systems, designed to help resolve complicated problems and/or questions by supporting the decision-making process. DSS are gaining increasing popularity in various domains including business, engineering, the military and medicine. These systems are especially valuable in situations where the amount of available information is prohibitive for the intuition of a human decision maker and where precision and optimal performance are of importance [1]. Medical decision support systemsThe huge amount of information that needs to be assimilated in order to keep pace with continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. In medical applications decision quality is of crucial importance, whilst human decision-making performance can be suboptimal and deteriorate as the complexity of the problem increases.For these reasons, the development of medical DSS is becoming increasingly important [1] and the routine uptake of these ''intelligent'' systems is becoming more common [2]. One of the earliest rule-based expert systems, DENDRAL [3], was implemented in the 1960s and was designed to provide support to organic chemists. This was further developed over the early 1970s by the same team at Stanford University into arguably the first rule-based medical DSS, MYCIN [4]. This system attempted to identify bacteria causing severe infections and recommend appropriate antibiotics. From these early DSS and the subsequent development of knowledge engineering, we now have DSS based on established architectures. Specific radiological cons...