Leading medical and scientific experts will discuss the latest developments around the COVID-19 vaccines at our March 11th event.
EVENT INFORMATION
DATE:
Thursday, March 11th, 2021 at 12:30pm - 1:45pm EST
On the one-year anniversary of the global declaration of the pandemic, this virtual event will convene leading scientific and medical experts to discuss the most pressing questions around the COVID-19 vaccines. Planned topics include the effect of the new circulating variants on the vaccines, what we know so far about transmission dynamics post-vaccination, how individuals can behave post-vaccination, the myths of "good" and "bad" vaccines as more alternatives come on board, and more. A public Q&A will follow the expert discussion.
CONTACT:
kira@goodinc.com
LOCATION:
Zoom webinar
SPEAKERS:
Dr. Paul Offit speaking at Communicating Vaccine Science.
commons.wikimedia.org
Dr. Paul Offit, M.D., is the director of the Vaccine Education Center and an attending physician in infectious diseases at the Children's Hospital of Philadelphia. He is a co-inventor of the rotavirus vaccine for infants, and he has lent his expertise to the advisory committees that review data on new vaccines for the CDC and FDA.
Dr. Monica Gandhi
UCSF Health
Dr. Monica Gandhi, M.D., MPH, is Professor of Medicine and Associate Division Chief (Clinical Operations/ Education) of the Division of HIV, Infectious Diseases, and Global Medicine at UCSF/ San Francisco General Hospital.
Dr. Onyema Ogbuagu, MBBCh, FACP, FIDSA
Yale Medicine
Dr. Onyema Ogbuagu, MBBCh, is an infectious disease physician at Yale Medicine who treats COVID-19 patients and leads Yale's clinical studies around COVID-19. He ran Yale's trial of the Pfizer/BioNTech vaccine.
Dr. Eric Topol
Dr. Topol's Twitter
Dr. Eric Topol, M.D., is a cardiologist, scientist, professor of molecular medicine, and the director and founder of Scripps Research Translational Institute. He has led clinical trials in over 40 countries with over 200,000 patients and pioneered the development of many routinely used medications.
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This event is the first of a four-part series co-hosted by LeapsMag, the Aspen Institute Science & Society Program, and the Sabin–Aspen Vaccine Science & Policy Group, with generous support from the Gordon and Betty Moore Foundation and the Howard Hughes Medical Institute.
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.