Viés racial na inteligência artificial para mamografia: um desafio à justiça e à equidade diagnóstica no Brasil
Barra lateral de artigos
Conteúdo do artigo principal
Resumo
Introduction: The application of artificial intelligence to mammography has shown promise to improve breast cancer screening, reducing interpretative variability and optimizing early detection. However, variations in performance between different population groups and clinical settings raise concerns about the validity and equity of the systems used.
Objective: To review the use of artificial intelligence in mammography, focusing on diagnostic performance, representation biases, ethical implications, and regulatory challenges for safe incorporation in Brazil.
Method: Integrative review between January 2016 and January 2025 in the PubMed, Scopus, and Web of Science databases. Multicenter studies, systematic reviews, meta-analyses, and regulatory and ethical analyses were included. Publications without peer review and articles not directly related to mammography were excluded. After screening and critical reading, thirty-four studies were selected for qualitative synthesis.
Results: Deep learning systems perform similarly to experienced radiologists and can reduce the number of repeat scans when used as a support for human reading. However, differences in sensitivity and specificity are observed between racial groups and demographic contexts, associated with the underrepresentation of certain populations in the training sets and the absence of external validation in different scenarios. In Brazil, the lack of representative and interoperable databases increases the risk of undetected biases and limits the generalization of results.
Conclusion: Responsible adoption of artificial intelligence in mammography requires local clinical validation across different regions and population groups, systematic utilization of equity metrics, regulatory transparency, and ongoing human oversight. The combination of ethical governance, independent auditing, and the participation of professionals and patients is essential for technological gains to translate into sustainable and fair clinical benefits.
Detalhes do artigo
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Referências
Deng T, Zi H, Guo XP, Luo LS, Yang YL, Hou JX, et al. Global, regional, and national burden of breast cancer, 1990-2021, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Thorac Cancer. 2025;16(9):e70052. https://doi.org/10.1111/1759-7714.70052
Nogueira MC, Fayer VA, Corrêa CSL, Guerra MR, De Stavola B, Dos-Santos-Silva I, et al. Inequities in access to mammographic screening in Brazil. Cad Saúde Pública. 2019;35(6):e00099817. https://doi.org/10.1590/0102-311X00099817
Silva JL, Albuquerque LZ, Rodrigues MES, Thuler LCS, Melo ACM. Ethnic disparities in breast cancer patterns in Brazil: examining findings from population-based registries. Breast Cancer Res Treat. 2024;206(2):359-367. https://doi.org/10.1007/s10549-024-07314-w
Santos MO, Lima FCS, Martins LFL, Oliveira JFP, Almeida LM, Cancela MC. Estimativa de incidência de câncer no Brasil, 2023-2025. Rev Bras Cancerol. 2023;69(1):e-213700. https://doi.org/10.32635/2176-9745.RBC.2023v69n1.3700
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. https://doi.org/10.1038/s41586-019-1799-6.
Elhakim MT, Stougaard SW, Graumann O, Larsen LB, Nielsen M, Foldager CB, et al. Breast cancer detection accuracy of AI in an entire screening population: a retrospective, multicentre study. Cancer Imaging. 2023;23(1):127. https://doi.org/10.1186/s40644-023-00643-x
Sharma N, Ng AY, James JJ, Khara G, Ambrózay É, Austin CC, et al. Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms. BMC Cancer. 2023;23(1):460. https://doi.org/10.1186/s12885-023-10890-7
Hsu W, Hippe DS, Nakhaei N, Wang PC, Zhu B, Siu N, et al. External validation of an ensemble model for automated mammography interpretation by artificial intelligence. JAMA Netw Open. 2022;5(11):e2242343. https://doi.org/10.1001/jamanetworkopen.2022.42343
Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021;27(12):2176-2182. https://doi.org/10.1038/s41591-021-01595-0
Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci U S A. 2020;117(23):12592-12594. https://doi.org/10.1073/pnas.1919012117
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102-27. https://doi.org/10.1016/j.zemedi.2018.11.002
Zeng A, Houssami N, Noguchi N, Nickel B, Marinovich ML. Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review. Breast Cancer Res Treat. 2024;207(1):1-13. https://doi.org/10.1007/s10549-024-07353-3
Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. 2017;4:170177. https://doi.org/10.1038/sdata.2017.177
Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236-248. https://doi.org/10.1016/j.acra.2011.09.014
Cai H, Wang J, Dan T, Li J, Fan Z, Yi W, et al. An online mammography database with biopsy confirmed types for machine diagnosis of breast (CMMD). Sci Data. 2023;10(1):123. http://doi.org/10.1038/s41597-023-02025-1
Halling-Brown MD, Warren LM, Ward D, Lewis E, Mackenzie A, Wallis MG, et al. OPTIMAM mammography image database: a large-scale resource of mammography images and clinical data. Radiol Artif Intell. 2021;3(1):e200103. https://doi.org/10.1148/ryai.2020200103
Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen LC, et al. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health. 2022;4(6):e406-14. https://doi.org/10.1016/s2589-7500(22)00063-2
Garrucho L, Kushibar K, Jouide S, Diaz O, Igual L, Lekadir K. Domain generalization in deep learning based mass detection in mammography: a large-scale multi-center study. Artif Intell Med. 2022;132:102386. https://doi.org/10.1016/j.artmed.2022.102386
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178(11):1544-7. https://doi.org/10.1001/jamainternmed.2018.3763
Kolbinger FR, Veldhuizen GP, Zhu J, Truhn D, Kather JN. Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis. Commun Med. 2024;4(1):71. https://doi.org/10.1038/s43856-024-00492-0
Muralidharan V, Adewale BA, Huang CJ, Nta MT, Ademiju PO, Pathmarajah P, et al. A scoping review of reporting gaps in FDA-approved AI medical devices. NPJ Digit Med. 2024;7(1):273. https://doi.org/10.1038/s41746-024-01270-x
Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde 2019: ciclos de vida — Brasil. Rio de Janeiro: IBGE; 2021.
Silva DMd, Cavalcante YA, Oliveira BLCAd, Lopes MVdO, Fernandes AFC, Pinheiro AKB, et al. Determinantes sociais de saúde associados à realização de mamografia segundo a Pesquisa Nacional de Saúde de 2013 e 2019. Ciênc Saúde Coletiva. 2025;30(1):e11452023. https://doi.org/10.1590/1413-81232025301.11452023
Furlam TO, Gomes LM, Machado CJ. COVID-19 e rastreamento do câncer de mama no Brasil: uma análise comparativa dos períodos pré-pandêmico e pandêmico. Ciênc Saúde Coletiva. 2023;28(1):223-30. https://doi.org/10.1590/1413-81232023281.06442022
Rocha AFBM, Freitas-Junior R, Ferreira GLR, Rodrigues DCN, Rahal RMS. COVID-19 and breast cancer in Brazil. Int J Public Health. 2023;68:1605485. https://doi.org/10.3389/ijph.2023.1605485
Gerke S, Babic B, Evgeniou T, Cohen IG. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med. 2020;3(1):53. https://doi.org/10.1038/s41746-020-0262-2
Medicines and Healthcare products Regulatory Agency (MHRA). Software and AI as a Medical Device Change Programme – Roadmap. London: MHRA; 17 out. 2022. Disponível em: https://www.gov.uk/government/publications/software-and-ai-as-a-medical-device-change-programme/software-and-ai-as-a-medical-device-change-programme-roadmap
Laux J, Wachter S, Mittelstadt B. Trustworthy artificial intelligence and the European Union AI Act: on the conflation of trustworthiness and acceptability of risk. Regul Gov. 2024;18(1):3-32. https://doi.org/10.1111/rego.12512
Jambor D. Avaliação regulatória de software como dispositivo médico com foco na inclusão e equidade nas aplicações de inteligência artificial em saúde. Rev Direito Sanit. 2025;25(1):e0017. https://doi.org/10.11606/issn.2316-9044.rdisan.2025.231708
Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Daumé III H, et al. Datasheets for datasets. Commun ACM. 2021;64(12):86-92. http://doi.org/10.1145/3458723
Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: mapping the debate. Big Data Soc. 2016;3(2):2053951716679679. http://doi.org/10.1177/2053951716679679
Chen IY, Joshi S, Ghassemi M. Treating health disparities with artificial intelligence. Nat Med. 2020;26(1):16-7. https://doi.org/10.1038/s41591-019-0649-2
Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. In: Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare. London: Academic Press; 2020. p. 295-336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Lyell D, Coiera E. Automation bias and verification complexity: a systematic review. J Am Med Inform Assoc. 2017;24(2):423-31. https://doi.org/10.1093/jamia/ocw105
Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc. 2012;19(1):121-7. https://doi.org/10.1136/amiajnl-2011-000089
De Proost M, Pozzi G. Conversational artificial intelligence and the potential for epistemic injustice. Am J Bioeth. 2023;23(5):51-3. https://doi.org/10.1080/15265161.2023.2191020
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. https://doi.org/10.1126/science.aax2342