# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications
### Clinical Decision Support Systems
Here we are in the presence of a technical paper in which the authors conducted field studies to calibrate their model. CDSSs are applied to single patients, therefore conclusions are better described by the membership of patient's characteristics to different classes, which can be encoded by possibility measures. Moreover, DSSs mostly give results in the form of a single, most reliable class. Selection and implementation of a CDSS for imaging was a subject study proposed in [74]. The tool presented as a procedure is intended to help radiologists ensure that they can understand and contribute to the process of implementing the CDSS for imaging. The efficacy of imaging CDSS was discussed in detail, the authors gave insightful discussion on the legislation and evidence of the system. They also showed number of considerations for selecting a CDSS vendor, tips for configuring CDSS in a fashion consistent with departmental goals, and pointers for implementation and change management. It was shown that number of studies have demonstrated a reduction in imaging utilization growth and improved appropriateness of advanced imaging requests after the implementation of imaging CDSS. The need for new CDSS is driven by different changes in legislation regarding healthcare. The study showed evidence that careful implementation of CDSS can have a significant impact on inappropriate imaging utilization. Careful planning is required when using CDSS, it can become an administrative obstacle to providing high-quality patient care. Radiologists should pay careful attention that CDSS is configured in a way that facilitates lasting, collaborative relationships with referring physicians and highlights the value of the radiologist as a clinical consultant rather than a clinical commodity. Patient privacy preservation in clinical decision support systems was the focus of [73]. Indeed, the protection of patient privacy when using clinical decision systems became an important subject with the exponentially increasing data used. The authors proposed a new privacy-preserving patient-orientedCDSS. The system helps clinician to complementarily diagnose the risk of patients' disease by preserving their privacy. The past patients' historical data are stored in cloud and can be used to train a Bayesian classifier algorithm. Information is preserved without leaking any patient medical data. The algorithm uses training data to classify and compute the disease risk for new patients. The authors of [75] were interested in how a CDSS can help in using Traumatic Brain Injury (TBI) Prediction Rules. The importance of prediction rules and guidelines lies in their ability to safely impact care. The primary aim of the authors was to determine whether implementing the two age based PECARN TBI (Pediatric Emergency Care Applied Research Network) clinical prediction rules by using a multifaceted intervention, centered around computerized CDSS, would decrease the rate of cranial CT (Computed Tomography) use for children with minor blunt head trauma at very low risk of TBIs, without increasing the rate of missed TBIs. In a second stage the aim of the authors was to determine whether CDSS that provided risk data for TBI for all children with minor blunt head trauma would decrease CT use. According to the authors, the implementation of TBI prediction rules and provision of risks of TBI by using computerized CDSS was associated with modest but variable decreases in rates of CT use for children at very low risk of TBI and for all children with minor blunt head trauma, without increasing the rate of missed injuries. However, decreased CT rates were inconsistent. The overall goal for the study presented in [76] is to identify decision complexity and its constituents in combination with cognitive strategies to inform the design of health information technology. It was important to provide valuable cognitive support to clinicians. The authors focused on the following research questions:  
1. What factors are associated with decision-making complexity experienced by ID experts?