AI Cures: data-driven clinical solutions for Covid-19
Panelists discuss open problems in Covid-19 patient care and new opportunities for AI solutions. Left to right and top to bottom: Regina Barzilay, MIT; Gabriella Antici, Protea Institute; Rajesh Gandhi, MGH; Karen Wong, CDC; and Guillermo Torre, TEC Salud.
Modern health care has been reinvigorated by the widespread adoption of artificial intelligence. From speeding image analysis for radiology to advancing precision medicine for personalized care, AI has countless applications, but can it rise to the challenge in the fight against Covid-19?
Researchers from the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), now housed within the MIT Stephen A. Schwarzman College of Computing, say the ongoing public health crisis provides ample opportunities for leveraging AI technologies, such as accelerating the search for effective therapeutics and drugs that can treat the disease, and are actively working to translate this potential to success.
When Covid-19 began to spread worldwide, Jameel Clinic’s community of machine learning and life science researchers redirected their work and began exploring how they can collaborate on the search for solutions by tapping into their collective knowledge and expertise. The ensuing discussions led to the launch of AI Cures, an initiative dedicated to developing machine learning methods for finding promising antiviral molecules for Covid-19 and other emerging pathogens, and to lower the barrier for people from varied backgrounds to get involved by inviting them to contribute to the effort.
As part of the mission of AI Cures to have broad impact and engagement, Jameel Clinic brought together researchers, clinicians, and public health specialists for a conference focused on the development of AI algorithms for the clinical management of Covid-19 patients, early detection and monitoring of the disease, preventing future outbreaks, and ways in which these technologies have been utilized in patient care.
Data-driven clinical solutions
On Sept. 29, over 650 people representing 50 countries and 70 organizations logged on from around the globe for the virtual AI Cures Conference: Data-driven Clinical Solutions for Covid-19.
In welcoming the audience, Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing, remarked that “AI in health care is moving beyond the use of computing as just simple tools, to capabilities that really aid in the processes of discovery, diagnosis, and care. The potential for AI-accelerated discovery is particularly relevant in times such as these.”
Attendees heard from 14 other speakers, including MIT researchers, on technologies they developed over the past six months in response to the pandemic — from epidemiological models created using clinical data to predict the risk of both infection and death for individual patients, to a wireless device that allows doctors to monitor Covid-19 patients from a distance, to a machine learning model that pinpoints patients at risk for intubation before they crash.
James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and faculty co-lead of life sciences for Jameel Clinic, gave the first talk of the day on harnessing synthetic biology to develop diagnostics to address Covid-19 and how his lab is using deep learning to enhance the design of such systems. Collins and his team are utilizing AI techniques to create a set of algorithms to effectively predict the efficacy of RNA-based sensors. The sensors, first developed in 2014 to detect the Ebola virus and later tailored for the Zika virus in 2016, were designed and optimized for a Covid-19 diagnostic, and related CRISPR-based biosensors are being used in a mask developed in Collins’ lab that produces a detectable signal when a person with the virus breathes, coughs, or sneezes.
While AI has proven to be an effective tool in health care, a model requires good data for it to be valuable and useful. With Covid-19 being a new disease, limited amounts of information are available to researchers, and in order to advance even more efforts to combat the virus, Collins notes that “we need to put in place and secure the resources to generate and collect large amounts of well-characterized data to train deep learning models. At present we generally don’t have such large datasets. In the system we developed, our dataset consists of about 91,000 RNA elements, which is currently the largest available for RNA synthetic biology, but it should be larger and expanded to many more different sensors.”
Offering perspective from the clinical side, Constance Lehman, a professor at Harvard Medical School (HMS), discussed the ways in which she’s implementing AI tools in her work as director of breast imaging at Massachusetts General Hospital (MGH). In collaboration with Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science and faculty co-lead of AI for Jameel Clinic, Lehman designs machine learning models to aid in breast cancer detection, which became a critical tool when mammography screenings were put on hold during the emergency stay-at-home-order issued in Massachusetts last March. By the time screenings reopened in May, around 15,000 mammograms had been cancelled. MGH is gradually rescheduling patients using a model developed by Lehman and Barzilay to help ease the process. “We took those women that had been diverted from screening and ranked them by their AI risk models and we reached out to them, inviting them back in.”
However, according to Lehman, many are choosing to opt out of screening and, in particular, fewer women of color are returning. “There are many determinants of who returns for screening. Social determinants can swamp all of our best, most scientific evidence-based approaches to effective and equitable health care. We’re delighted that our risk model is equally predictive across races, but I am dismayed to see that we are screening more white women than women of color during these times. Those are social determinants, which we are working very hard on.”
The conference culminated in a panel discussion with those who are at the front line of the pandemic. The panelists — Gabriella Antici, founder of the Protea Institute in Brazil; Rajesh Gandhi, a professor at HMS and an infectious disease physician at MGH; Guillermo Torre, a professor of cardiology and president of TEC Salud in Mexico; and Karen Wong, data science unit lead for the Covid-19 clinical team at the U.S. Centers for Disease Control and Prevention — shared their experiences in handling the crisis and had an open conversation with Barzilay, the panel’s moderator, on the limitations of AI and what is currently not being addressed.
“Those from the AI community like myself are always asking ourselves if we are solving the right problems,” says Barzilay. “We hope to come up with new ideas for AI solutions and what we can do in the future to help.”
Gandhi offered that “we need more refined and sophisticated approaches to deciding when to use different drugs and how to use them in combination.” He also suggested that integrating physiologic data could be useful in considering how to treat individual patients from different age ranges exhibiting a variety of Covid-19 symptoms, from mild to severe.
In her closing remarks, Barzilay expressed hope that the conference “illustrates the types of problems that we need to be addressing on the AI side” and notes that Jameel Clinic will widely share any new data they obtain so that everyone can benefit to help patients suffering from Covid-19.
The event was the first in a pair of conferences that took place as part of the AI Cures initiative. The next event, AI Cures Drug Discovery Conference, which will focus on cutting-edge AI approaches in this area developed by MIT researchers and their collaborators, will be held virtually on Oct. 30.
AI Cures: Data-driven Clinical Solutions was organized by Jameel Clinic, MIT Schwarzman College of Computing, and Institute for Medical Engineering and Sciences. Additional support was provided by the Patrick J. McGovern Foundation.
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