MIT Jameel Clinic will tomorrow host first conference in Saudi Arabia exploring the role of artificial intelligence in healthcare
The MIT Jameel Clinic, the epicentre of artificial intelligence (AI) in healthcare at the Massachusetts Institute of Technology (MIT), is hosting a conference in Saudi Arabia exploring the role of artificial intelligence in healthcare. The conference – called 'AI Cures • MENASA' – aims to discuss mechanisms for integrating artificial intelligence technologies into the healthcare sector in the Middle East, North Africa and South Asia (MENASA) regions.
A number of Saudi government entities will participate in the conference, including the Ministry of Health, SEHA Virtual Hospital, King Abdulaziz City for Science and Technology and King Faisal Specialist Hospital and Research Centre, as well as leaders from several public and private hospitals and medical experts. A number of panel discussions will be held at the conference, showcasing recent developments in AI-powered health technology, as well as AI's potential to reshape the future of healthcare by providing effective and accurate diagnosis and treatment.
This year's conference coincides with the fifth anniversary of the founding of the MIT Jameel Clinic in 2018 by Community Jameel and MIT, which has more than 60 years' experience in the field of artificial intelligence. The conference will also serve as a platform for knowledge exchange, networking and the expansion of the MIT Jameel Clinic AI Hospital Network, supported by Community Jameel and Wellcome, in line with the MIT Jameel Clinic's mission to democratise access to AI-based healthcare solutions worldwide. The network was launched by MIT Jameel Clinic in 2022.
The MIT Jameel Clinic is in the process of developing several leading AI tools that use deep learning models, and recently unveiled several AI-powered tools, including Mirai, a pioneering technology that can predict breast cancer risk up to five years ahead and more accurately than current screening techniques, and Sybil, which can predict the risk of lung cancer up to six years ahead.
