Chiara Corti: Global disparities in artificial intelligence-based mammogram interpretation for breast cancer
Chiara Corti, Oncologist. Research Fellow at Dana-Farber Cancer Institute, shared a post on X:
“Global disparities in artificial intelligence-based mammogram interpretation for breast cancer: A scientometric analysis of representation, trends, and equity.
In our latest study, we identified 264 studies on AI-powered mammography interpretation for breastcancer detection out of 5774 in the two-year periods 2017-2018 and 2022-2023:
- Despite a ~300% increase in AI-related publications between these periods, we found that only 0-25% of studies reported race / ethnicity data, with the majority of patients identified as Caucasian.
- Nearly all patient cohorts originated from high- income countries, with no representation from low-income settings.
- We also observed gender imbalance among first and last authors (male predominance), with affiliations predominantly from high-income regions.
Model accuracy is limited in time and space. AI should be local, iterative, reflective and reflexive. The business model should be assisting health systems with creating, maintaining, curating, evaluating their data pipeline, and most importantly, their “learning pipeline”. A big thank you to LeoCeli and MIT Critical Data for their consistent support in our research on AI bias and disparities in breast cancer-related subfields. Especially proud of the talented MD candidate Isabele A. Miyawaki, first author of this work, and I have no doubt we’ll hear a lot about her in the near future.
Authors: Isabele A. Miyawaki et al.
-
Challenging the Status Quo in Colorectal Cancer 2024
December 6-8, 2024
-
ESMO 2024 Congress
September 13-17, 2024
-
ASCO Annual Meeting
May 30 - June 4, 2024
-
Yvonne Award 2024
May 31, 2024
-
OncoThon 2024, Online
Feb. 15, 2024
-
Global Summit on War & Cancer 2023, Online
Dec. 14-16, 2023