Today’s podcast guest is Rosalind Picard, a researcher, inventor named on over 100 patents, entrepreneur, author, professor and engineer. When it comes to the science related to endowing computer software with emotional intelligence, she wrote the book. It’s published by MIT Press and called Affective Computing.
Dr. Picard is founder and director of the MIT Media Lab’s Affective Computing Research Group. Her research and engineering contributions have been recognized internationally. For example, she received the 2022 International Lombardy Prize for Computer Science Research, considered by many to be the Nobel prize in computer science.
Through her research and companies, Dr. Picard has developed wearable sensors, algorithms and systems for sensing, recognizing and responding to information about human emotion. Her products are focused on using fitness trackers to advance clinical quality treatments for a range of conditions.
Meanwhile, in just the past few years, numerous fitness tracking companies have released products with their own stress sensors and systems. You may have heard about Fitbit’s Stress Management Score, or Whoop’s Stress Monitor – these features and apps measure things like your heart rhythm and a certain type of invisible sweat to identify stress. They’re designed to raise awareness about forms of stress such as anxieties and anger, and suggest strategies like meditation to relax in real time when stress occurs.
But how well do these off-the-shelf gadgets work? There’s no one more knowledgeable and experienced than Rosalind Picard to explain the science behind these stress features, what they do exactly, how they might be able to help us, and their current shortcomings.
Dr. Picard is a member of the National Academy of Engineering and a Fellow of the National Academy of Inventors, and a popular speaker who’s given over a hundred invited keynote talks and a TED talk with over 2 million views. She holds a Bachelors in Electrical Engineering from Georgia Tech, and Masters and Doctorate degrees in Electrical Engineering and Computer Science from MIT. She lives in Newton, Massachusetts with her husband, where they’ve raised three sons.
In our conversation, we discuss stress scores on fitness trackers to improve well-being. She describes the difference between commercial products that might help people become more mindful of their health and products that are FDA approved and really capable of advancing the science. We also talk about several fascinating findings and concepts discovered in Dr. Picard’s lab including the multiple arousal theory, a phenomenon you’ll want to hear about. And we explore the complexity of stress, one reason it’s so tough to measure. For example, many forms of stress are actually good for us. Can fitness trackers tell the difference between stress that’s healthy and unhealthy?
Show links:
- Dr. Picard’s book, Affective Computing
- Dr. Picard’s bio
- Dr. Picard on Twitter
- Dr. Picard’s company, Empatica - https://www.empatica.com/ - The FDA-cleared Empatica Health Monitoring Platform provides accurate, continuous health insights for researchers and clinicians, collected in the real world
- Empatica Twitter
- Dr. Picard and her team have published hundreds of peer-reviewed articles across AI, Machine Learning, Affective Computing, Digital Health, and Human-computer interaction.
- Dr. Picard’s TED talk
Rosalind Picard
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.