Recommendation Systems in Healthcare: Challenges, Limitations, and Its Applications

Ahstract-The rapid advancement of healthcare technologies has led to the increasing integration of recommendation systems in various aspects of medical practice. This survey aims to provide a comprehensive overview of the current state-of-the-art recommendation systems in healthcare, examining their applications, underlying techniques, and the challenges they face. This paper highlights their role in personalized treatment plans, drug recommendations, diagnostic support, and lifestyle man-agement by categorizing and analyzing diverse recommendation methods, including content-based filtering, collaborative filtering, and hybrid approaches. Furthermore, we explore the types of data utilized, ranging from electronic health records to wearable device outputs, and discuss the significance of machine learning, deep learning, and hybrid models in enhancing recommendation accuracy and efficacy. Through detailed case studies and com-parative analyses, we illustrate successful implementations and extract lessons from less effective attempts. Finally, we outline emerging trends and future directions, emphasizing the potential of technologies like blockchain, federated learning, and real-time data analysis from IoT devices.