Artificial Intelligence for Automatic and Optimized Generation of Healthcare Planning

The management of the planning in a service is a major stake for the good functioning of the health establishment, but it is also extremely complex due to a large number of constraints, specific rules, and the large size of the planning. In the literature, different solutions have been suggested, and most of them are based on algorithms from optimization and operational research. This paper aims to discuss the application of artificial intelligence (AI), particularly artificial learning, to overcome the limitations of existing work, namely the very limited number of constraints, static planning without auto-updating, reduced size, and application to very specific cases without generalization of the model, and thus improve the performance of existing models and algorithms. For the various use cases, the majority of previous works have only taken into account less difficult restrictions that are specific to certain study instances, but in practice, the situation is different because there are additional more complex constraints and objectives.

I. Introduction

The healthcare sector is changing as a result of advances in artificial intelligence (AI) and machine learning [1]. Huge data sets in the form of clinical trial findings, demographics, medical records, photographs, and claims have been amassed by healthcare companies. Artificial intelligence (AI) technologies are ideally equipped to examine this information and determine patterns and indicators that people would not be able to recognize on their own. Healthcare institutions may utilize deep learning [2] and reinforcement learning algorithms [3] [4] to help them make better business and clinical choices and raise the caliber of the experiences they offer.