MiniGPT-Med Model
The head of the scientific team at SDAIA, who worked on creating the model, Dr. Ahmed bin Zaki Al-Sannan, the AI consultant at the National Center for Artificial Intelligence, said: “The MiniGPT-Med model was designed in a way that allows users to input medical images and perform a number of tasks, including generating medical reports, answering medical visual questions, detecting the location of the disease, recognizing diseases, and producing medically-based descriptions.”
X-rays, CT scans, MRIs
He added that the model has been trained on various medical images (X-rays, CT scans, and MRIs), noting that recent advancements in artificial intelligence have contributed to the development of the healthcare field. However, these advancements are often limited by specific functions. Therefore, the MiniGPT-Med model will address this issue. It is derived from large-scale language models and is specifically designed for medical applications, demonstrating remarkable versatility across different imaging methods. It also included X-rays, CT scans, and MRIs, which enhances its utility.
Al-Senan pointed out that the MiniGPT-Med model was developed by AI specialists from SDAIA and KAUST. It achieved advanced performance in generating medical reports that is 19% higher than the best previous model. It serves as a general interface for radiology diagnostics, enhancing diagnostic efficiency across a wide range of medical imaging applications.
KAUST, VQA
Professor Mohammed Al-Husseini, head of the research team at KAUST, stated that the model can generate medical reports, answer visual questions (VQA), and identify diseases in medical images.
The integrated processing of clinical imaging and textual data significantly enhances diagnostic accuracy, improving medical report generation and VQA standards. Furthermore, this advancement contributes to better radiology practices.
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