The Lunit solutions are expected to be installed at more than 170 medical sites across Saudi Arabia. Credit: Lunit.

Seha Virtual Hospital (SVH) in Riyadh, Saudi Arabia, has selected Lunit’s AI-powered cancer screening solutions for its AI evaluation and clinical integration project.

The collaboration aims to add to the ‘transformation’ of healthcare across Saudi Arabia in line with the Saudi Vision 2030 strategy.

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The AI cancer screening solutions, Lunit INSIGHT CXR for chest X-ray image analysis and Lunit INSIGHT MMG for mammography analysis, will undergo validation and integration at SVH.

Following evaluation, they are expected to be implemented at more than 170 medical sites across Saudi Arabia as part of a national cancer and tuberculosis screening programme.

SVH was established last year and is claimed to be the world’s largest virtual hospital.

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It supports 170 hospitals and provides medical services to 480,000 patients each year.

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The integration of Lunit’s solutions is intended to increase the effectiveness of cancer screening and improve patient care in Saudi Arabia.

Lunit CEO Brandon Suh said: “We are delighted to partner with Seha Virtual Hospital and contribute to the transformation of cancer diagnosis in Saudi Arabia.

“Given that Saudi Arabia boasts the largest medical market in the Gulf Cooperation Council, our highly promising partnership allows us to tap into this growing market.

“Through this collaboration, we aim to bring our innovative AI-powered solutions to healthcare providers throughout the Middle East, ultimately improving patient outcomes and making a significant impact on the fight against cancer.”

Lunit was founded in 2013 and is based in Seoul, South Korea.

The company’s collaboration with SVH follows previous partnerships with the BreastScreen New South Wales programme in Australia last year and the Capio St Göran Hospital in Sweden last month.

Earlier this year, it partnered with the National Cancer Center Hospital East (NCCHE) in Japan to evaluate and validate its AI pathology solution for the analysis of tissue data.