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.



Paris Olympics Expected to Face 4 Billion Cyber Incidents

A general view of the Olympic rings on the Eiffel Tower a day before the opening ceremony of the Paris 2024 Olympics, in Paris, France June 25, 2024. (Reuters)
A general view of the Olympic rings on the Eiffel Tower a day before the opening ceremony of the Paris 2024 Olympics, in Paris, France June 25, 2024. (Reuters)
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Paris Olympics Expected to Face 4 Billion Cyber Incidents

A general view of the Olympic rings on the Eiffel Tower a day before the opening ceremony of the Paris 2024 Olympics, in Paris, France June 25, 2024. (Reuters)
A general view of the Olympic rings on the Eiffel Tower a day before the opening ceremony of the Paris 2024 Olympics, in Paris, France June 25, 2024. (Reuters)

As the Paris 2024 Olympic Games approach, cybersecurity officials are bracing for over 4 billion cyber incidents. They are setting up a new centralized cybersecurity center for the Games, supported by advanced intelligence teams and artificial intelligence (AI) models.

Eric Greffier, the technical director for Paris 2024 at Cisco France, told Asharq Al-Awsat that the Tokyo 2020 Games saw around 450 million cyber incidents. He added that the number of incidents expected for Paris is at least ten times higher, requiring a more efficient response.

Greffier explained that a single cybersecurity center allows for better coordination and a faster response to incidents.

This approach has proven effective in other areas, such as banking and the NFL, where his company also handles cybersecurity, he added.

The Extended Detection and Response (XDR) system is central to the company’s security strategy.

Greffier described it as a “comprehensive dashboard” that gathers data from various sources, links events, and automates threat responses.

It offers a complete view of cybersecurity and helps manage threats proactively, he affirmed.

The system covers all aspects of the Olympic Games’ digital security, from network and cloud protection to application security and end-user safety.

In cybersecurity, AI is vital for managing large amounts of data and spotting potential threats. Greffier noted that with 4 billion expected incidents, filtering out irrelevant data is crucial.

The Olympic cybersecurity center uses AI and machine learning to automate threat responses, letting analysts focus on real issues, he explained.

One example is a network analytics tool that monitors traffic to find unusual patterns.

Greffier said that by creating models of normal behavior, the system can detect anomalies that might indicate a potential attack. While this might generate false alarms, it helps ensure that unusual activity is flagged for further review.