A Systematic Review of ‘Fair’ AI Model Development for Image Classification and Prediction
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40846_2022_754_ReferencePDF.pdf
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Author(s)
Correa, Ramon
Shaan, Mahtab
Trivedi, Hari
Patel, Bhavik
Celi, Leo A. G.
Gichoya, Judy W.
Banerjee, Imon
Date Issued
October 15, 2022
Publisher
Springer Berlin Heidelberg
Citation
Correa, Ramon, Shaan, Mahtab, Trivedi, Hari, Patel, Bhavik, Celi, Leo A. G. et al. 2022. "A Systematic Review of ‘Fair’ AI Model Development for Image Classification and Prediction."
Version
Author's final manuscript
Abstract
Abstract
Purpose
The new challenge in Artificial Intelligence (AI) is to understand the limitations of models to reduce potential harm. Particularly, unknown disparities based on demographic factors could encrypt currently existing inequalities worsening patient care for some groups.
Methods
Following PRISMA guidelines, we present a systematic review of ‘fair’ deep learning modeling techniques for natural and medical image applications which were published between year 2011 to 2021. Our search used Covidence review management software and incorporates articles from PubMed, IEEE, and ACM search engines and three reviewers independently review the manuscripts.
Results
Inter-rater agreement was 0.89 and conflicts were resolved by obtaining consensus between three reviewers. Our search initially retrieved 692 studies but after careful screening, our review included 22 manuscripts that carried four prevailing themes; ‘fair’ training dataset generation (4/22), representation learning (10/22), model disparity across institutions (5/22) and model fairness with respect to patient demographics (3/22). We benchmark the current literature regarding fairness in AI-based image analysis and highlighted the existing challenges. We observe that often discussion regarding fairness are limited to analyzing existing bias without further establishing methodologies to overcome model disparities.
Conclusion
Based on the current research trends, exploration of adversarial learning for demographic/camera/institution agnostic models is an important direction to minimize disparity gaps for imaging. Privacy preserving approaches also present encouraging performance for both natural and medical image domain.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s40846-022-00754-z
