A visualization of the virus that causes COVID-19.
12:30 p.m. - 1:45 p.m. EDT
REGISTER:
Dr. Amesh Adalja, M.D., FIDSA, Senior Scholar, Johns Hopkins Center for Health Security; Adjunct Assistant Professor, Johns Hopkins Bloomberg School of Public Health; Affiliate of the Johns Hopkins Center for Global Health. His work is focused on emerging infectious disease, pandemic preparedness, and biosecurity.
Dr. Nahid Bhadelia, M.D., MALD, Founding Director, Boston University Center for Emerging Infectious Diseases Policy and Research (CEID); Associate Director, National Emerging Infectious Diseases Laboratories (NEIDL), Boston University; Associate Professor, Section of Infectious Diseases, Boston University School of Medicine. She is an internationally recognized leader in highly communicable and emerging infectious diseases (EIDs) with clinical, field, academic, and policy experience in pandemic preparedness.
Dr. Akiko Iwasaki, Ph.D., Waldemar Von Zedtwitz Professor of Immunobiology and Molecular, Cellular and Developmental Biology and Professor of Epidemiology (Microbial Diseases), Yale School of Medicine; Investigator, Howard Hughes Medical Institute. Her laboratory researches how innate recognition of viral infections lead to the generation of adaptive immunity, and how adaptive immunity mediates protection against subsequent viral challenge.
Dr. Marion Pepper, Ph.D., Associate Professor, Department of Immunology, University of Washington. Her lab studies how cells of the adaptive immune system, called CD4+ T cells and B cells, form immunological memory by visualizing their differentiation, retention, and function.
This event is the third of a four-part series co-hosted by Leaps.org, 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.