The EU legal framework for using artificial intelligence and imaging databases and imaging biobanks for research purposes: applying the notion of Fairness

Objective: To analyze the issue of justice and discrimination in artificial intelligence systems based on medical image databases. Methodology: Analysis of documents that constitute the regulatory framework of the European Union for the use of artificial intelligence, compared with the report FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. Results: The study indicates that artificial intelligence trained with unbalanced data tends to generate biased predictions, which can exacerbate health inequalities and affect justice. Discrimination in artificial intelligence systems appears abstract, subtle, and difficult to detect compared to traditional forms of discrimination. Final Considerations: Robust regulation is necessary to ensure justice in artificial intelligence systems, considering the need for interdisciplinary collaboration to prepare this new generation of legal professionals with an enhanced perspective on the topic and its various dimensions. Submission: 10/01/24| Review: 10/04/24| Approval: 10/04/24

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Publication Details

Journal
CADERNOS IBERO-AMERICANOS DE DIREITO SANITÁRIO
Published
2024-11-20
DOI
https://doi.org/10.17566/ciads.v13i4.1288
Citations
2
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.24
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article

The EU legal framework for using artificial intelligence and imaging databases and imaging biobanks for research purposes: applying the notion of Fairness

Valentina Colcelli
2 citations
CADERNOS IBERO-AMERICANOS DE DIREITO SANITÁRIO
Artificial Intelligence in Healthcare and Education
0.24
article

The EU legal framework for using artificial intelligence and imaging databases and imaging biobanks for research purposes: applying the notion of Fairness

Valentina Colcelli
article en
2 citations

Abstract

Objective: To analyze the issue of justice and discrimination in artificial intelligence systems based on medical image databases. Methodology: Analysis of documents that constitute the regulatory framework of the European Union for the use of artificial intelligence, compared with the report FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. Results: The study indicates that artificial intelligence trained with unbalanced data tends to generate biased predictions, which can exacerbate health inequalities and affect justice. Discrimination in artificial intelligence systems appears abstract, subtle, and difficult to detect compared to traditional forms of discrimination. Final Considerations: Robust regulation is necessary to ensure justice in artificial intelligence systems, considering the need for interdisciplinary collaboration to prepare this new generation of legal professionals with an enhanced perspective on the topic and its various dimensions. Submission: 10/01/24| Review: 10/04/24| Approval: 10/04/24

CADERNOS IBERO-AMERICANOS DE DIREITO SANITÁRIOVol. 13(4)
Consorzio Roma Ricerche (IT)
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 39%
Artificial Intelligence in Healthcare and Education
0.24
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