Call for papers - Bias detection and mitigation in medical informatics

Guest Editors

Nao Hagiwara, PhD, University of Virginia, USA
Lon Jeffrey Van Winkle, PhD, Rocky Vista University, USA

Submission Status: Open | Submission Deadline: 11 June 2025

BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Bias detection and mitigation in medical informatics.

Health and care disparities, such as those on racial, ethnic, gender, or any other social basis, have long existed, hence are structurally embedded in health data and decision-making processes, undermining the reliability of prediction models built on those data. This Collection aims to gather research that explores the development of computational methods and tools for detecting and mitigating biases in medical informatics, as well as strategies for promoting inclusive and equitable healthcare practices. Researchers are invited to submit their work on topics including but not limited to bias detection frameworks, metrics to evaluate bias, and mitigation strategies.

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Meet the Guest Editors

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Nao Hagiwara, PhD, Department of Public Health Sciences, University of Virginia, USA

Dr Hagiwara is a professor of Public Health Sciences and Director of the Program on Health Disparities and Community Engagement Research at the University of Virginia. She holds a PhD in Social Psychology and completed her postdoctoral fellowship in Behavioral Oncology. Dr Hagiwara is a leading researcher in the field of healthcare provider bias and disparities in healthcare and health. With multiple NIH-funded projects, she examines the dynamics of provider bias and its impact on the quality of patient care across various clinical settings. Her recent work has appeared in flagship journals, including the Lancet and Science Advances.

Lon Jeffrey Van Winkle, PhD, Department of Medical Humanities, Rocky Vista University, USA

Dr Lon J. Van Winkle earned his PhD in Biochemistry from Wayne State University in 1975. He joined the faculty at several community colleges near Detroit in 1974 and became a full-time faculty member in Natural Sciences at the Dearborn campus of the University of Michigan in 1977. He then took a position at Midwestern University in 1979. He retired as Professor and Chair of Biochemistry at that institution in August 2015. Dr Van Winkle is currently a Professor of Medical Humanities at Rocky Vista University. He publishes regularly in the scientific literature on both early embryonic development and science education. This work has been supported by grants from the Illinois Board of Higher Education and the National Institutes of Health. His continuing interest in basic research, science education, and human growth and development is exemplified by his participation in numerous conferences on the adult development of women and men and as an invited speaker at national and international conferences on healthcare professional education, embryology, and biomembrane transport. He is a member of the American Society for Biochemistry and Molecular Biology, the International Association of Medical Science Educators, and the Gold Humanism Honor Society.

About the Collection

BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Bias detection and mitigation in medical informatics.

Health and care disparities, such as those on racial, ethnic, gender, or any other social basis, have long existed, hence are structurally embedded in health data and decision-making processes, undermining the reliability of prediction models built on those data. Under-representation or mis-representation issues have become a concerning challenge with the rise of big data and machine learning: without proper bias detection and mitigation methods, several techniques might reproduce health inequalities. The awareness of such issue in the scientific and clinical community have pushed the development of tools tailored for the detection, measurement, and correction of data-embedded or methodologically induced bias, leading to a critical area of research in medical informatics, that focuses on identifying and addressing biases.

This Collection aims to gather research that explores the development of computational methods and tools for detecting and mitigating biases in medical informatics, as well as strategies for promoting inclusive and equitable healthcare practices. Researchers are invited to submit their work on topics including but not limited to bias detection frameworks, metrics to evaluate bias, and mitigation strategies.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ''Bias detection and mitigation in medical informatics'' from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.