Saudi Arabia’s SDAIA Signs MoU with Int’l Software Developer at LEAP 23

Saudi Arabia’s SDAIA Signs MoU with Int’l Software Developer at LEAP 23
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Saudi Arabia’s SDAIA Signs MoU with Int’l Software Developer at LEAP 23

Saudi Arabia’s SDAIA Signs MoU with Int’l Software Developer at LEAP 23

Saudi Data and Artificial Intelligence Authority (SDAIA) has signed a memorandum of understanding with NetApp global software company to provide high-quality and fast-growing Cloud services.

This comes as part of SDAIA’s work on empowering government institutions with digital services to achieve the objectives of the Saudi Vision 2030.

The memorandum was signed during SDAIA’s participation in the LEAP Conference, held on February 6-9.

Under the MoU, the two sides will build a comprehensive strategy for services, exchange expertise in commercial and technical service operations, and cooperate to decrease the environmental impact of Cloud services.



SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI
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SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

The Center of Excellence for Data Science and Artificial Intelligence at the Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have introduced the MiniGPT-Med model.

The large multi-modal language model is designed to help doctors quickly and accurately diagnose medical radiology using artificial intelligence techniques.

Dr. Ahmed Alsinan, the Artificial Intelligence Advisor at the National Center for Artificial Intelligence and head of the scientific team at SDAIA, explained that the MiniGPT-Med model is capable of performing various tasks such as generating medical reports, answering medical visual questions, describing diseases, locating diseases, identifying diseases, and documenting medical descriptions based on entered medical images.

The model was trained on different medical images, including X-rays, CT scans, and MRIs.

The MiniGPT-Med model, derived from large-scale language models, is specifically tailored for medical applications and demonstrates significant versatility across different imaging methods, including X-rays, CT scans, and MRI. This enhances its utility in medical diagnosis.

Dr. Alsinan highlighted that the MiniGPT-Med model was developed collaboratively by artificial intelligence specialists from SDAIA and KAUST.

The model exhibits advanced performance in generating medical reports, achieving 19% higher efficiency than previous models. It serves as a general interface for radiology diagnosis, enhancing diagnostic efficiency across various medical imaging applications.