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Dr. Eman Yahya Abdu Al-Qaisi, a faculty member at King Khalid University’s College of Computer Science (Department of Informatics and Computer Systems), has been granted a patent registered with the Saudi Authority for Intellectual Property for her invention, “A Convolutional Neural Network-Based System for Multimodal Medical Image Diagnosis.” It is the first medical artificial intelligence system of its kind designed to operate in both large hospitals and remote areas.
The invention involves the development of a medical artificial intelligence system capable of diagnosing medical images within seconds by automatically identifying the type of input image and diagnosing a number of critical diseases, thereby supporting immediate medical decision-making without requiring an internet connection.
Regarding the invention’s key features, Dr. Al-Qaisi explained that the system can automatically identify and diagnose the type of medical image within a single lightweight model, rather than relying on separate models for each type of image. It can also operate entirely on mobile devices without an internet connection and in real time, providing flexibility for use across various healthcare environments.
She noted that test results demonstrated excellent diagnostic performance across multimodal medical data, including cases of hemorrhagic and ischemic strokes, brain tumors, and chest infections, enhancing the potential for further development of the system and expansion of its medical applications.
The significance of the invention lies in addressing a diagnostic gap in certain healthcare environments that may face shortages of radiology specialists or limited internet connectivity. The system provides physicians with immediate diagnostic support, helping accelerate medical decision-making, reduce diagnostic errors, and improve patient outcomes in both crowded hospitals and remote areas.
Regarding future applications, Dr. Al-Qaisi explained that the system could be extended to emergency departments, intensive care units, field clinics, and government hospitals, with the possibility of integrating it into digital hospital systems and telehealth applications. In the future, it could also be expanded to include additional types of medical images and diseases.
