# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 4 Discussion and Framework  
The five key AIA applications reviewed were chosen due to their higher extent of application, but there are many more healthcare technologies and application opportunities. In healthcare, we noticed that AI is very focused on applying data mining algorithms on various problems such as diagnosis and treatment. That allows automated systems to make predictions and recommendations. Indeed, a data mining algorithm is used to learn a model to best predict the decision variable of a new example. It can also make associations between different frequent item sets and generate association rules with a certain confidence degree that can be used in prediction. Artificial intelligence raises many hopes in the healthcare sector even though its real implementation in the hospital environment and the daily lives of patients remains reluctant. Indeed, development of AI systems in health care is accompanied by ethical issues inherent to their use. Concerns about the protection of privacy, confidentiality, and even intimacy are worrisome. There are also challenges with respect to the free and informed consent of patients. Despite the great advantages of using big data in healthcare and artificial intelligence since the analysis of large volumes of data leads to better decisions, its use raises a few problems related in particular to security and privacy (often personal data, if not sensitive). These represent threats for smart homes adoption, with its extensive use of big data and data mining. Finally, it is not always possible to explain the reasons that lead AI systems to make recommendations and decisions, even if we know that it is data mining algorithms and ontologies that are used. This leads to considerations about the lack of transparency--the AI black box. AI applications can improve healthcare quality in hospitals on one hand, and on the other hand they can provide an efficient assistance to patients with difficulties in their daily life even outside of hospitals. AI in healthcare entails reducing medical errors and adverse events. Nevertheless, there is also what is called complacency in automation that threatens safety of AI in healthcare. In case of repeated use, physicians, especially those who are very busy, may accept AI systems guidance and transfer to them the responsibility even to make all decisions. These systems are given more weight than they deserve. Physicians may unconsciously learn to avoid available alternatives when an AI system repeatedly demonstrates that it is consistent with their diagnoses. We must balance the potential for improvement against the risk of negative results. AI also faces another major challenge in health: its results must be interpretable by humans in a consistent manner. For example, interpretation of imaging tests is a common cause of misdiagnosis especially in the diagnosis of pneumonia. Hence, AI has been applied in the settings of automated chest radiograph interpretation by using machine learning and deep learning algorithms, but many limitations were identified [138]. While our review is by no means exhaustive, their convergence may help us show the many more areas requiring further research, especially providing more ample empirical evidence for extensive field deployment. This requires applying AIA to serve patients within a complex environment, involving a degree of variability that is often overlooked or hard to reproduce in clinical informatics studies. To help future research, we propose an innovative architecture to integrate intelligent systems to help guide the delivery of primary healthcare. As presented in Figure 1, our framework can help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for complex cases (e.g., chronic care) who present comorbidities requiring coordination. This means that AIA technologies, features, and applications are linked to health outcomes directly, allowing a better representation of patient concerns in AIA design.