# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications
### Clinical Decision Support Systems  
The medical informatics community is very interested in developing methods and tools to support clinical processes, varying from reminders, recommendations, and messaging systems, to decision support, treatment planning, and clinical workflow services. Clinical decision support systems (CDSS) that aim to help healthcare providers to make better decisions and improve patient care were proposed more than a half-century ago. This help concerns both diagnosis and treatment [62]. According to several studies, if used effectively, this aid could improve the quality, safety, and high performance of health care [63; 64; 65; 66]. The lynchpin of CDSS success is well defined in [67] \"The goal of CDSS is to provide the right information, to the right person, in the right format, through the right channel, at the right point in workflow to improve health and health care decisions and outcomes\". An interesting article [68] discusses the use of fuzzy decision support systems for diabetes diagnosis. The authors presented an ontology-based system and implemented a new semantically interpretable FRBS (Fuzzy Rule-Based Systems) framework to diagnose diabetes. To provide a more intuitive and accurate design, the authors used in their framework multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP). The proposed system offers numerous unique and essential enhancements to the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. In the fuzzy rules evaluation process the ontology semantic similarity of diabetes complications are considered, as well as symptoms concepts. The proposed system helps physicians and patients to accurately diagnose diabetes mellitus as it was shown in the testing of the framework using a real data set. The authors worked on a hospital in Egypt, they combined the proposed FRBS using JAVA APIs and produced the hierarchical FRBS (H-FRBS). In an actual hospital environment, although all features are required, certain features are more critical than others. For example, diabetes symptoms are more important than lipid profile. Moreover, in some cases, not all features are known for a given patient, and physicians must decide with the available information. The resulting system can make decisions with different combinations of data sources and with different confidence levels. According to the authors the framework is smart and can provide more accurate results than existing studies. Further improvements can be made to extend its functionality as it was built within an open architecture. The system can even be applied to any other medical field in a simple and easy way. Its current limitations are that it does not yet allow full interoperability with the HER (Electronic Health Records) system and does not consider data regarding patients from social media. The paper [69] presents a type-2 fuzzy ontology-aided recommendation system. The system monitors the patient's body by recommending diets with specific foods and drugs. The system begins first by extracting the values of patient risk factors. Then, it determines the patient's health condition via wearable sensors. At last, it delivers recommendations consisting of diabetes-specific prescriptions for a smart medicine box and food for a smart refrigerator. Protege Web Ontology Language (OWL)-2 tools are used in this ontology, the aim is to deliver decision-making knowledge based on patient information. The recommendation process uses Semantic Web Rule Language (SWRL) rules and fuzzy logic. The performance of the proposed ontology was evaluated after the development of each phase to measure the improvement level. This system can effectively overcome the load of chronic patients on hospitals, help physicians to automatically retrieve patient physiological information for diagnoses, and can assist old patients with chronic diseases for long-term care without continuous visits to physicians.