Interest in AI for medical image analysis is exploding, with exponential growth in publications and dataset availability. Translation of AI in medical imaging to clinical practice depends upon rigorous, careful development and testing of algorithms; ethical, rigorous, and transparent AI development and deployment has the potential to increase access to care and contribute to health equity. Ongoing concerns include the potential for increasing existing health inequities at all scales, from global health to the individual level, through potential adverse impacts of bias in AI performance for patient subpopulations that are underrepresented in the training data.

The theme of this JMI special issue is the role of equity, bias, and diversity in the development, regulation, and clinical application of AI, and AI’s impact on global health. The articles are published in JMI volumes 10-11 and collected here:

Special Section Guest Editorial: Global Health, Bias, and Diversity in AI in Medical Imaging

Judy W. Gichoya, Rui C. Sá, Ronald M. Summers, and Heather Whitney

Fairness-related performance and explainability effects in deep learning models for brain image analysis

Emma A. M. Stanley, Matthias Wilms, Pauline Mouches, and Nils D. Forkert

Homogenization of multi-institutional chest x-ray images in various data transformation schemes

Hyeongseok Kim, Seoyoung Lee, Woo Jung Shim, Min-Seong Choi, and Seungryong Cho

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment

Karen Drukker, Weijie Chen, Judy W. Gichoya, Nicholas P. Gruszauskas, Jayashree Kalpathy-Cramer, Sanmi Koyejo, Kyle J. Myers, Rui C. Sá, Berkman Sahiner, Heather M. Whitney, Zi Zhang, and Maryellen L. Giger

Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons

Heather M. Whitney, Natalie Baughan, Kyle J. Myers, Karen Drukker, Judy Gichoya, Brad Bower, Weijie Chen, Nicholas Gruszauskas, Jayashree Kalpathy-Cramer, Sanmi Koyejo, Rui C. Sá, Berkman Sahiner, Zi Zhang, and Maryellen L. Giger

Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts

John Lee Burns, Zachary Zaiman, Jack Vanschaik, Gaoxiang Luo, Le Peng, Brandon Price, Garric Mathias, Vijay Mittal, Akshay Sagane, Christopher Tignanelli, Sunandan Chakraborty, Judy W. Gichoya, and Saptarshi Purkayastha

Image harmonization and deep learning automated classification of plus disease in retinopathy of prematurity

Ananya Subramaniam, Faruk Orge, Michael Douglass, Basak Can, Guillermo Monteoliva, Evelin Fried, Vanina Schbib, Gabriela Saidman, Brenda Peña, Soledad Ulacia, Pedro Acevedo, Andrew M. Rollins, and David L. Wilson

Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation

Clara Koo, Anthony Yang, Colton Welch, Vipashyana Jadav, Liana Posch, Nicholas Thoreson, Darrell Morris, Fatima Chouhdry, Janet Szabo, David Mendelson, and Laurie R. Margolies