Journal article
Understanding Biases and Disparities in Radiology AI Datasets: A Review
Journal of the American College of Radiology, v 20(9), pp 836-841
Sep 2023
PMID: 37454752
Abstract
Artificial intelligence (AI) continues to show great potential in disease detection and diagnosis on medical imaging with increasingly high accuracy. An important component of AI model creation is dataset development for training, validation, and testing. Diverse and high-quality datasets are critical to ensure robust and unbiased AI models that maintain validity, especially in traditionally underserved populations globally. Yet publicly available datasets demonstrate problems with quality and inclusivity. In this literature review, the authors evaluate publicly available medical imaging datasets for demographic, geographic, genetic, and disease representation or lack thereof and call for an increase emphasis on dataset development to maximize the impact of AI models.
Metrics
1 Record Views
Details
- Title
- Understanding Biases and Disparities in Radiology AI Datasets: A Review
- Creators
- Satvik Tripathi (Corresponding Author) - University of PennsylvaniaKyla Gabriel - Harvard Medical SchoolSuhani Dheer - University of PennsylvaniaAastha Parajuli - Kathmandu UniversityAlisha Isabelle Augustin - Drexel UniversityAmeena Elahi - University of Pennsylvania Health SystemOmar Awan - University of Maryland School of MedicineFarouk Dako - University of Pennsylvania
- Publication Details
- Journal of the American College of Radiology, v 20(9), pp 836-841
- Publisher
- Elsevier
- Number of pages
- 6
- Grant note
- RSNA Research & Education Foundation (https://doi.org/10.13039/100005625)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Arts and Sciences; Computer Science
- Web of Science ID
- WOS:001088424400001
- Scopus ID
- 2-s2.0-85173586468
- Other Identifier
- 991022207154204721