Understanding Bias in Artificial Intelligence: A Practice Perspective

References

1. 1.↵
   2. Davis MA,
   3. Lim N,
   4. Jordan J, et al

   . Imaging artificial intelligence: a framework for radiologists to address health equity, from the AJR Special Series on DEI. AJR Am J Roentgenol 2023;221:302–08 doi:10.2214/AJR.22.28802 pmid:37095660

   CrossRefPubMed
2. 2.↵
   2. Martins N,
   3. Aires A,
   4. Mendez B, et al

   . Ghost infarct core and admission computed tomography perfusion: redefining the role of neuroimaging in acute ischemic stroke. Interv Neurol 2018;7:513–21 doi:10.1159/000490117 pmid:30410531

   CrossRefPubMed
3. 3.↵
   2. Albers GW,
   3. Marks MP,
   4. Kemp S, et al

   ; DEFUSE 3 Investigators. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 2018;378:708–18 doi:10.1056/NEJMoa1713973 pmid:29364767

   CrossRefPubMed
4. 4.↵
   2. Menon S,
   3. Damian A,
   4. Hu S, et al

   . Pulse: self-supervised photo upsampling via latent space exploration of generative models. arXiv 2020 doi:10.48550/arXiv.2003.03808 https://arxiv.org/abs/2003.03808. Accessed June 23, 2023

   CrossRef
5. 5.↵
   2. Rouzrokh P,
   3. Khosravi B,
   4. Faghani S, et al

   . Mitigating bias in radiology machine learning, 1: data handling. Radiol Artif Intell 2022;4:e210290 doi:10.1148/ryai.210290 pmid:36204544

   CrossRefPubMed
6. 6.↵
   2. Lee H,
   3. Yune S,
   4. Mansouri M, et al

   . An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 2019;3:173–82 doi:10.1038/s41551-018-0324-9 pmid:30948806

   CrossRefPubMed
7. 7.↵
   2. Zech JR,
   3. Badgeley MA,
   4. Liu M, et al

   . Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 2018;15:e1002683 doi:10.1371/journal.pmed.1002683 pmid:30399157

   CrossRefPubMed
8. 8.↵
   2. Filippi CG,
   3. Stein JM,
   4. Wang Z, et al

   . Ethical considerations and fairness in the use of artificial intelligence for neuroradiology. AJNR Am J Neuroradiol 2023;44:1242–48 doi:10.3174/ajnr.A7963 pmid:37652578

   Abstract/FREE Full Text
9. 9.↵
   2. O’Neill TJ,
   3. Xi Y,
   4. Stehel E, et al

   . Reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage. Radiol Artif Intell 2021;3:e200024 doi:10.1148/ryai.2020200024 pmid:33937858

   CrossRefPubMed
10. 10.↵
    2. Voter AF,
    3. Meram E,
    4. Garrett JW, et al

    . Diagnostic accuracy and failure mode analysis of a deep learning algorithm for the detection of intracranial hemorrhage. J Am Coll Radiol 2021;18:1143–52 doi:10.1016/j.jacr.2021.03.005 pmid:33819478

    CrossRefPubMed
11. 11.↵
    2. Pianykh OS,
    3. Langs G,
    4. Dewey M, et al

    . Continuous learning AI in radiology: implementation principles and early applications. Radiology 2020;297:6–14 doi:10.1148/radiol.2020200038 pmid:32840473

    CrossRefPubMed