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AIMI Journal Club: Underdiagnosis Bias of Artificial Intelligence Algorithms Applied to Chest Radiographs in Under-Served Patient Populations - Laleh Seyyed-Kalantari, PhD

Event Details:

Thursday, February 10, 2022
3:00pm - 4:00pm PST


Laleh Seyyed-Kalantari, PhD

Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic.


Laleh is an associate scientist at Lunenfeld Tanenbaum Research Institute in Toronto, Canada. She received a PhD in electrical engineering from McMaster University and she was an NSERC postdoctoral fellow at the Vector Institute and the University of Toronto. Her research interests are developing AI-based medical image diagnostic tools with novel contributions in theory and application with a focus on their fairness. Her ultimate goal is to remove barriers toward the trustable deployment of AI-based medical image diagnostic tools in clinics, such that they benefit the patients, provide fairness, and reduce the workload of clinical staff. She has received a number of highly competitive national, provincial, and institutional awards such as NSERC Postdoctoral Fellowship (2018), Research in Motion Ontario Graduate Scholarship (2015), Ontario Graduate Scholarship and Queen Elizabeth II Graduate Scholarship in Science and Technology (2014), and Ontario Graduate Scholarship (2013).

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