Siehe weiterführend:  

Filippi, C. G., Stein, J. M., Wang, Z., Bakas, S., Liu, Y., Chang, P. D., Lui, Y., Hess, C., Barboriak, D. P., Flanders, A. E., Wintermark, M., Zaharchuk, & G., Wu, O. (2023). Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology. American Journal of Neuroradiology, 44(11), 242–1248. https://doi.org/10.3174/ajnr.A7963

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2025). Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects. Diagnostic and Interventional Radiology, 31(2), 75–88. https://doi.org/10.4274/dir.2024.242854

Siehe zur Debatte um sog. wünschenswerte Biases:

Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digitital Medicine, 3, 81. https://doi.org/10.1038/s41746-020-0288-5