Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)
Open accessAbstract
The utility of Artificial Intelligence (AI) in healthcare strongly depends upon the quality of the data used to build models, and the confidence in the predictions they generate. Access to sufficient amounts of high-quality data to build accurate and reliable models remains problematic owing to substantive legal and ethical constraints in making clinically relevant research data available offsite. New technologies such as distributed learning offer a pathway forward, but unfortunately tend to suffer from a lack of transparency, which undermines trust in what data are used for the analysis. To address such issues, we hypothesized that, a novel distributed learning that combines sequential distributed learning with a blockchain-based platform, namely Chained Distributed Machine learning C-DistriM, would be feasible and would give a similar result as a standard centralized approach. C-DistriM enables health centers to dynamically participate in training distributed learning models. We demonstrate C-DistriM using the NSCLC-Radiomics open data to predict two-year lung-cancer survival. A comparison of the performance of this distributed solution, evaluated in six different scenarios, and the centralized approach, showed no statistically significant difference (AUCs between central and distributed models), all DeLong tests yielded p -val >0.05. This methodology removes the need to blindly trust the computation in one specific server on a distributed learning network. This fusion of blockchain and distributed learning serves as a proof-of-concept to increase transparency, trust, and ultimately accelerate the adoption of AI in multicentric studies. We conclude that our blockchain-based model for sequential training on distributed datasets is a feasible approach, provides equivalent performance to the centralized approach.
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ISO/TC 204 is the main standard facilitating the systematic use of autonomous vehicle technology and delivers an extensive manual on the entire system and infrastructure elements of ITS.
“The main standard facilitating the systematic use of autonomous vehicle technology in this context is ISO/TC 204, which delivers an extensive manual on the entire system and infrastructure elements of ITS [45], [79], [80].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [80]
The Introduction discusses AI in medical imaging, and the Blockchain section discusses distributed learning infrastructure. Searches for ISO/TC 204, autonomous vehicles, and intelligent transportation systems found no passage addressing the claimed standard or its scope. Our copy therefore does not verify the attribution. (LLM, conf=0.78)
Experimental — accuracy not yet measuredAccording to Intel's analysis, automated cars would save commuters' annual travel time by 250 million hours and contribute to saving more than 500,000 lives solely in the United States between 2035 and 2045.
“According to Intel's analysis on the anticipated merits of automated cars, the employment of such innovations on roadways would save commuters' annual travel time by 250 million hours and contribute to saving more than 500,000 lives, solely in The United States of America, between the years 2035-2045 [79], [80].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [80]
The Abstract and Results discuss blockchain-based distributed learning for medical imaging, not automated cars. Searches for Intel’s analysis, 250 million travel hours, 500,000 lives, and autonomous vehicles found no passage addressing the claim. Page numbers were not available in our copy. (LLM, conf=0.76)
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- Authors
- Zerka, F., Urovi, V., Vaidyanathan, A., Barakat, S., Leijenaar, R. T. H., Walsh, S., Gabrani-Juma, H., Miraglio, B., Woodruff, H. C., Dumontier, M., & Lambin, P.
- Affiliations
- Maastricht University (NL)
- Maastricht University Medical Centre (NL)
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 183939-183951
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3091851474
- Cited by
- 89
- References
- 48
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 19 · 2023: 24 · 2022: 15 · 2021: 7
- FWCI
- 10.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Publications (Maastricht University) (submittedVersion, cc-by-nc-nd)
- Funders
- ERC advanced Grant (ERC-ADG-2015, n◦ 694812 - Hypoximmuno)
- Dutch Technology Foundation STW (Grant n◦ P14-19 Radiomics STRaTegy), which is the applied science division of NWO, Aspasia NWO (n◦91716421)
- Technology Program of the Ministry of Economic Affairs
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- blockchain, distributed learning, privacy preservation, radiomics, data transparency, trust in AI
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