Janett RS, Yeracaris PP. Electronic Medical Records in the American Health System: challenges and lessons learned. Cien Saude Colet. 2020;25:1293–304.

Article  PubMed  Google Scholar 

Gupta B, Neffendorf JE, Wong R, Laidlaw DAH, Williamson TH. Ethnic variation in vitreoretinal surgery: differences in clinical presentation and outcome. Eur J Ophthalmol. 2017;27:367–71.

Article  PubMed  Google Scholar 

Sivaprasad S, Gupta B, Gulliford MC, Dodhia H, Mohamed M, Nagi D, et al. Ethnic variations in the prevalence of diabetic retinopathy in people with diabetes attending screening in the United Kingdom (DRIVE UK). PLoS One. 2012;7:e32182.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Mastropasqua R, Luo YH, Cheah YS, Egan C, Lewis JJ, da Cruz L. Black patients sustain vision loss while White and South Asian patients gain vision following delamination or segmentation surgery for tractional complications associated with proliferative diabetic retinopathy. Eye. 2017;31:1468–74.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Chandra A, Banerjee P, Davis D, Charteris D. Ethnic variation in rhegmatogenous retinal detachments. Eye. 2015;29:803–7.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Chen DK, Modi Y, Al-Aswad LA. Promoting transparency and standardization in ophthalmologic artificial intelligence: a call for artificial intelligence model card. Asia Pac J Ophthalmol. 2022;11:215–8.

Article  Google Scholar 

Jacoba CMP, Celi LA, Lorch AC, Fickweiler W, Sobrin L, Gichoya JW, et al. Bias and non-diversity of big data in artificial intelligence: focus on retinal diseases. Semin Ophthalmol. 2023;38:433–41.

Article  PubMed  Google Scholar 

Xu D, Uhr J, Patel SN, Pandit RR, Jenkins TL, Khan MA, et al. Sociodemographic factors influencing rhegmatogenous retinal detachment presentation and outcome. Ophthalmol Retin. 2021;5:337–41.

Article  Google Scholar 

Rehman Siddiqui MA, Abdelkader E, Hammam T, Murdoch JR, Lois N. Socioeconomic status and delayed presentation in rhegmatogenous retinal detachment. Acta Ophthalmol. 2010;88:e352–3.

Article  PubMed  Google Scholar 

Anguita R, Ting MYL, Makuloluwa A, Charteris DG. Causal factors for late presentation of retinal detachment. Eye. 2023;37:185–6.

Article  PubMed  Google Scholar 

Anguita R, Roth J, Makuloluwa A, Shahid S, Katta M, Khalid H, et al. Late presentation of retinal detachment: clinical features and surgical outcomes. Retina. 2021;41:1833–8.

Article  PubMed  Google Scholar 

Ferro Desideri L, Danilovska T, Bernardi E, Artemiev D, Paschon K, Hayoz M, et al. Artificial intelligence-enhanced OCT biomarkers analysis in macula-off rhegmatogenous retinal detachment patients. Transl Vis Sci Technol. 2024;13:21.

Article  PubMed  PubMed Central  Google Scholar 

Poh SSJ, Sia JT, Yip MYT, Tsai ASH, Lee SY, Tan GSW, et al. Artificial intelligence, digital imaging, and robotics technologies for surgical vitreoretinal diseases. Ophthalmol Retin. 2024;8:633–45.

Article  Google Scholar 

Mihan A, Pandey A, Van Spall HG. Mitigating the risk of artificial intelligence bias in cardiovascular care. Lancet Digit Health. 2024;6:e749–e54.

Article  PubMed  CAS  Google Scholar 

Sufian MA, Alsadder L, Hamzi W, Zaman S, Sagar A, Hamzi B. Mitigating algorithmic bias in AI-driven cardiovascular imaging for fairer diagnostics. Diagnostics. 2024;14:2675.

Article  PubMed  PubMed Central  Google Scholar 

Burlina P, Joshi N, Paul W, Pacheco KD, Bressler NM. Addressing artificial intelligence bias in retinal diagnostics. Transl Vis Sci Technol. 2021;10:13.

Article  PubMed  PubMed Central  Google Scholar 

DeCamp M, Lindvall C. Mitigating bias in AI at the point of care. Science. 2023;381:150–2.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Mittermaier M, Raza MM, Kvedar JC. Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digit Med. 2023;6:113.

Article  PubMed  PubMed Central  Google Scholar 

Khan SM, Liu X, Nath S, Korot E, Faes L, Wagner SK, et al. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability. Lancet Digit Health. 2021;3:e51–e66.

Article  PubMed  CAS  Google Scholar 

Vagliano I, Byrne Salsas C, Wunn T, Schut MC. External validation and transportability of models to predict acute kidney injury in the intensive care unit. Stud Health Technol Inf. 2022;295:148–51.

Google Scholar 

Correa R, Pahwa K, Patel B, Vachon CM, Gichoya JW, Banerjee I. Efficient adversarial debiasing with concept activation vector—medical image case-studies. J Biomed Inf. 2024;149:104548.

Article  Google Scholar 

Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol. 2023;96:11–25.

Article  PubMed  CAS  Google Scholar 

Yang J, Soltan AAS, Eyre DW, Yang Y, Clifton DA. An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digit Med. 2023;6:55.

Article  PubMed  PubMed Central  Google Scholar