## 2 Fundamental Concepts and Background  
### Clinical Decision Support Systems  
Clinical Decision Support Systems are computer systems that \"provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered, or presented at appropriate times, to enhance health and health care\" [22]. CDSSs are designed for a variety of purposes such as diagnosis, treatment response prediction, treatment recommendation (personalization), prognosis, and the prioritization of patient care according to their level of risk. They can be helpful in clinical practice as a \"second set of eyes\" for clinicians, combining their human knowledge with the \"knowledge\" that is embedded in the system. CDSS can help to improve patients' safety, quality of care, and healthcare efficiency [23; 24; 25], as well as reducing the costs of healthcare [26]. They can improve patient safety not only by reducing medical errors but also through reminders for medications or other medical events for patients or clinicians [25]. Additionally, CDSS can be useful in low-resource settings where the number of medical institutions, equipment, and qualified clinicians is limited. CDSS can be classified as knowledge-based and non-knowledge-based [27]. CDSS that are knowledge-based depend on medical guidelines and knowledge while non-knowledge-based CDSS typically use ML. ML-based CDSS find patterns in historical clinical data and develop predictive models that are able to predict clinical outcomes based on new inputs. These outcomes can then be used as recommendations for clinicians to help them in their practice. ML-based CDSS have great potential in clinical practice. They can help to enhance the accuracy of clinical decisions and minimize medical errors because they are objective, depending only on the input data, and the inner decision-making logic. However, they rely on the quality and quantity of data provided [28]. When the data used to train an ML model are biased, this bias is captured by the model and consequently can make biased or incorrect predictions. This can ultimately lead to a biased or incorrect human decision. Companies such as IBM, Elsevier, Intermediate, and Microsoft have developed or are currently developing such systems. IBM's \"Watson Health\" [29] aims to help in treatment-related decisions for patients. However, significant challenges still remain as Watson Health does not perform as well in the clinical world as it did in the game show Jeopardy! [30]. Elsevier's \"Via Pathways\", rebranded \"ClinicalPath\" [31] provides evidence-based care maps for the treatment of patients with cancer, and ClinicalKey [32] is a search engine that provides clinical decision support using research-based recommendations. Informedica has developed a mobile application called Symptate [33] which is a popular symptom checker, recently updated to perform a COVID-19 checkup. Finally, Microsoft is developingthe \"Hanover Project\" [34] which aims to identify the most relevant pieces of information that experts will need to make the best possible decisions regarding treatment plans for patients with cancer. CDSS have been developed and applied across a wide range of pathologies including rare diseases [35], oncology [36] and specifically, breast cancer [37], chronic obstructive pulmonary disease [38], the prevention of venous thromboembolism [39], the prediction of chronic kidney disease [40] and Alzheimer's disease [41], for diabetes care [42], and risk-level prediction of heart disease [43]. In constructing CDSS, developers are confronted with \"unknown, incomplete, imbalanced, heterogeneous, noisy, dirty, erroneous, inaccurate, and missing datasets in arbitrarily high-dimensional spaces\" [3]. Additionally, systems such as CDSS do not work in isolation, but within other systems, institutions, and with human actors whose efforts must be coordinated for AI in medicine to have the most beneficial impact.