Recognizing the vital role of innovation, industrial products, and technological solutions in showcasing the impact and achievements of research chairs, as well as contributing to the objectives of Saudi Arabia's Vision 2030, the Artificial Intelligence Research Chair in Healthcare has, since its establishment, been dedicated to transforming its members' research and intellectual contributions into innovative, marketable products and applications.
In collaboration with King Abdullah bin Abdulaziz University Hospital, the Chair is advancing this vision through the development and upcoming launch of AI-powered healthcare applications that have been designed and validated using real-world hospital data. These applications include:
1. Mareen, an AI-powered rehabilitation system that has been successfully developed and tested to support pediatric patients aged 5–12 years who have sustained hand injuries. The system integrates therapeutic exercises into interactive games designed across three difficulty levels—easy, moderate, and advanced—based on the child's motor performance.
Mareen generates detailed progress reports that enable physical therapists to remotely monitor the child's improvement and adherence to the rehabilitation program. The platform features a fully Arabic user interface, specifically designed to facilitate independent engagement by children without the need for continuous parental supervision.
Project Team: Shahad Al-Hajri, Rahaf Maslami, Abrar Al-Ghamdi, Deem Al-Mutairi
2. Chest X-rays are gaining increasing importance as a common diagnostic tool, as recognized by the World Health Organization.
However, interpreting chest X-rays can be complex and time-consuming — even for experienced radiologists — which may lead to misinterpretations and delays in treatment. This is where RADAI comes in. This application aims to develop an AI-based model for reading chest X-rays, along with a companion application. The model can accurately detect four abnormalities in chest X-ray images and generate a report for each image. The application is also designed with ease-of-use in mind, featuring a simple interface for radiologists and a well-structured backend model, enabling smooth interaction and collaboration.
Project team: Hanan Aljuaid , Hessa Albalahad, Walaa Alshuaibi, Shahad Almutairiz, Rawan Bin Rkhyes ،Nazar Hussain
3. Brain Tumor Detection and Intensity Classification Using Deep Convolutional Neural Networks
Brain images are a vital tool for diagnosing brain tumors, but they require high accuracy and specialized expertise to interpret. This is where this revolutionary technical project comes into play! Using Deep Convolutional Neural Networks (CNNs), a model has been developed capable of analyzing brain images with exceptional intelligence to identify the type of tumor and classify its severity (ranging from low to high risk).
The model effectively supports doctors in making accurate and rapid decisions, reducing diagnosis time and increasing early treatment opportunities. The system is designed with a user-friendly interface and relies on real medical data to ensure the highest levels of accuracy and efficiency.
4. Detection & Prediction of Epileptic Seizures Using Machine Learning Model
Seizures pose a sudden and frightening risk to many patients, but with artificial intelligence... we can now be "ahead of the event." In this project, an intelligent system has been developed based on machine learning models to analyze neural signals and predict seizures before they occur. The model works by detecting neural patterns that precede a seizure, allowing for early preparation and risk mitigation for the patient. The system is supported by analytical interfaces and real-time reports, helping healthcare providers make faster and more accurate decisions
