One year ago today, the Massachusetts Institute of Technology (MIT) and Community Jameel co-founded the Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), with the aim of revolutionizing disease prevention, detection, and treatment of ailments including cancer, epilepsy, Alzheimer’s, and congestive heart failure by harnessing the power of machine learning and AI technologies.

J-Clinic utilizes a holistic approach that draws on MIT’s strong expertise in cellular and medical biology, computer science, engineering, and the social sciences as it focuses on developing machine learning technologies to transform the treatment of disease in future, with three main areas of focus:

  1. Preventative medicine methods and technologies with the potential to change the course of the noninfectious disease by stopping it in its tracks.
  2. Cost-effective diagnostic tests that may be able to both detect and alleviate health problems.
  3. Drug discovery and development to enable faster and cheaper discovery, development, and manufacture of new pharmaceuticals, particularly those targeted for individually customized therapies.

This marriage of machine learning with clinical and biological insights aspires to spur a global transformation in the healthcare and medical fields with the aim to save the lives of millions of people, spawn new technologies, and improve the entire healthcare industry around the globe.

Since its launch one year ago, J-Clinic has announced more than US$ 2.3 million in funding for 18 research projects, involving principal investigators from departments and labs within engineering, architecture and planning, science, and management. 

Some projects will focus on AI technologies to optimize early detection and prevention of disease, whilst others will concentrate on repurposing existing drugs and improving electronic health records. In addition, a $50,000 grant funded by J-Clinic in collaboration with the MIT Deshpande Center for Technological Innovation will support AI-focused research into the rapid diagnosis of bacterial infection.

The resulting technologies and solutions will be applied to numerous health care systems and clinical settings around the globe, transforming medical outcomes for people everywhere.

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