Sharing Data Is Essential for the Future of AI in Medical Imaging
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If we want artificial intelligence to succeed in radiology, we must share data and learn how to share data.
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Publicly accessible benchmarks are essential for comparison for AI models and represent a crucial element of open science.
“Publicly accessible benchmarks are essential for comparison for AI models and represent a crucial element of open science.122”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [122]
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- Authors
- Bell, L. C., & Shimron, E.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Venue
- Radiology: Artificial Intelligence
- Published
- 2024-01-01
- Volume
- 6
- Issue
- 1
- Pages
- e230337-e230337
- ISSN
- 2638-6100 (ISSN-L)
- PMC
- PMC10831510
- OpenAlex
- W4389136101
- Cited by
- 30
- References
- 31
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 7 · 2023: 1
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- other-oa
- Locations
- Radiology Artificial Intelligence (publishedVersion)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- data sharing, artificial intelligence in radiology, diagnostic imaging
- MeSH terms
- Diagnostic Imaging, Radiography, Information Dissemination
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