SchaferEJLaversanneMSungH, et al. Recent patterns and trends in global prostate cancer incidence and mortality: an update. Eur Urol. 2025;87(3):302-313. doi:10.1016/j.eururo.2024.11.013
2.
YuACMohajerBEngJ.External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiol Artif Intell. 2022;4(3):e210064. doi:10.1148/ryai.210064
3.
CarereSimonGJewellJonathanNasute FauerbachPatriciaV, et al. Training with local data remains important for deep learning MRI prostate cancer detection. Can Assoc Radiol J. 2026;77(2):338-347. doi:10.1177/08465371251367620.
4.
YamagishiYBabaYSuzukiJOkadaYKanaoKOyamaM.Few-shot learning for prostate cancer detection on MRI: comparative analysis with radiologists’ performance. J Imaging Inform Med. Published online June 25, 2025. doi:10.1007/s10278-025-01581-9
5.
AldermanJEPalmerJLawsE, et al. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. NEJM AI. 2025;2(1):AIp2401088. doi:10.1056/AIp2401088
6.
HasanzadehFJosephsonCBWatersGAdedinsewoDAziziZWhiteJA.Bias recognition and mitigation strategies in artificial intelligence healthcare applications. NPJ Digit Med. 2025;8(1):154. doi:10.1038/s41746-025-01503-7