Hospital Network – MIT Jameel Clinic

We believe that AI presents a powerful opportunity to tackle challenges faced by health care systems worldwide.

With the support of Wellcome Trust, we’re teaming up with hospitals around the globe to bring AI into mainstream healthcare.

In providing free access to an array of cutting-edge AI tools, we hope to contribute the unique expertise of Jameel Clinic researchers to empower healthcare systems by accelerating the mainstream usage of AI tools on a global scale.

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66 Hospitals

30 Countries

05 Continents

The effective adoption of clinical AI tools goes beyond creating them — it requires community engagement and the support of advocates within healthcare systems and patient organizations.

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Our Toolkit

SYBIL

Lung Cancer Risk Assessment Tool 2022

Uses a noninvasive deep learning system to assess lung cancer risk in low-dose CT scans up to 6 years in advance.

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MIRAI

Breast Cancer Risk Assessment Tool 2021

Uses noninvasive a deep learning system to assess breast cancer risk in mammograms up to 5 years in advance.

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Our Tools in Development

Closing the gap

While there have been significant advances in AI, there is still very little AI incorporated into routine patient care, even in technologically advanced nations.

Contrary to popular belief, technical limitations play a very small role in the adoption of clinical AI. Instead, it is unfamiliarity and lack of trust contributing to the gap between what clinical AI tools are capable of and what tools are currently being used in clinical care.

Developing trustworthy AI

Our work focuses on 1. transparency and interpretability and 2. patient data privacy. The inability of current deep learning models to consistently deliver on both accounts has resulted in serious failures that impact patient care and breach public trust.

To build trust, our machine learning researchers work with physicians to ensure that the AI models are understandable to their users and can readily incorporate feedback while they are being used in clinical settings while strengthening their data privacy.

The scale and computational power needed for bold ideas to work is not possible without the support of grants, foundations, and donors. Your support would enable MIT Jameel Clinic to make a better, healthier future for all.

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Since 2022, more than 6,000 women have had their exams passed through MIRAI and patients with increased breast cancer risk have been called for a differentiated screening program. Beyond the fact that these are important steps towards having a risk-based screening program implemented in Brazil's public health system, the opportunity for an early diagnosis is a life-changing factor for all these women! Dr. Grasiela Benini, MD
Head of Mastology & Mastology Residency Program Coordinator
Santa Marcelina Hospital

At IRST, we firmly believe that integrating deep learning technologies like SYBIL and MIRAI in clinical practice, will radically improve patient care. The use of AI algorithms by physicians and medical physicists can capture this opportunity and lead to the future of precision medicine because the utilization of imaging data is now only partially exploiting its full potential. Dr. Giacomo Feliciani, PhD
Medical Physics Unit
IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” IRST

Portrait of Dr. Luz Fernanda Sua Villegas

For patients with breast cancer, learning about the pathology can help them regain control of their lives, especially when they are shown the hopeful data of healing and recovery. Dr. Luz Fernanda Sua Villegas, MD, PhD
Pathologist
Fundación Valle del Lili

In Chile, there are no cancer screening policies as defined by the WHO, only recommendations and for some types of cancers, financial coverage for the population. Therefore, there are tremendous opportunities regarding cancer prevention and early detection, especially for lower income people. Dr. Arnaldo Marín
PhD Faculty of Medicine, Department of Basic and Clinical Oncology
University of Chile