Artificial intelligence, data sharing, and privacy for retinal imaging under Brazilian Data Protection Law
Abstract The integration of artificial intelligence (AI) in healthcare has revolutionized various medical domains, including radiology, intensive care, and ophthalmology. However, the increasing reliance on AI-driven systems raises concerns about bias, particularly when models are trained on non-rep...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | International Journal of Retina and Vitreous |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40942-024-00596-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract The integration of artificial intelligence (AI) in healthcare has revolutionized various medical domains, including radiology, intensive care, and ophthalmology. However, the increasing reliance on AI-driven systems raises concerns about bias, particularly when models are trained on non-representative data, leading to skewed outcomes that disproportionately affect minority groups. Addressing bias is essential for ensuring equitable healthcare, necessitating the development and validation of AI models within specific populations. This viewpoint paper explores the critical role of data in AI development, emphasizing the importance of creating representative datasets to mitigate disparities. It discusses the challenges of data bias, the need for local validation of AI algorithms, and the misconceptions surrounding retinal imaging in ophthalmology. Additionally, highlights the significance of publicly available datasets in research and education, particularly the underrepresentation of low- and middle-income countries in such datasets. The Brazilian General Data Protection Law is also examined, focusing on its implications for research and data sharing, including the legal and ethical measures required to safeguard data integrity and privacy. Finally, the manuscript underscores the importance of adhering to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) to enhance data usability and support responsible AI development in healthcare. |
|---|---|
| ISSN: | 2056-9920 |