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King Khalid University faculty member Dr. Eman Yahya Abdu Al-Qaisi, from the 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 innovation, “A Convolutional Neural Network-Based System for Multimodal Medical Image Diagnosis,” 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.
Highlighting 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 different healthcare environments.
She noted that test results demonstrated excellent diagnostic performance across multimodal medical datasets, including cases of hemorrhagic and ischemic strokes, brain tumors, and chest infections, supporting 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 radiologists 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 overcrowded hospitals and remote areas.
Regarding future applications, Dr. Al-Qaisi explained that the system could be used in emergency departments, intensive care units, field clinics, and government hospitals, with the potential for integration into digital hospital systems and telehealth applications. Future development could also expand the system to cover additional types of medical images and diseases.
