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Three Saudi Teams Win in Saudi Space Apps Challenge in Jeddah

University of Business and Technology in Jeddah (UBT official site)
University of Business and Technology in Jeddah (UBT official site)
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Three Saudi Teams Win in Saudi Space Apps Challenge in Jeddah

University of Business and Technology in Jeddah (UBT official site)
University of Business and Technology in Jeddah (UBT official site)

Three teams in Jeddah won the Space Apps Challenge, participating against 17 projects that competed over three days to provide the best scientific and engineering innovations in space, organized by the Technical Valley Department at the University of Business and Technology in Jeddah, under the supervision of NASA Space Agency.

The judging committee announced the winning projects, scoring the first three places in seven tracks, which included space exploration, planets and moons, open science, the sun, climate, Earth and astrophysics, according to SPA.

More than 497 competitors participated from Jeddah, competing to build innovative solutions to challenges on Earth and in space using NASA's 48-hour open data.

The Communications, Space & Technology Commission (CST) organized the largest space and science Hackathon in the solar system, stating that the Saudi Space Apps Challenge marks a milestone in the Kingdom's journey toward space exploration and technological progress.

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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
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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.