Inteligência artificial e a mulher: avanços no diagnóstico precoce do câncer de ovário e de mama. Uma revisão integrativa
Palavras-chave:
Câncer de mama. Câncer de ovário. Inteligência Artificial. Diagnóstico.Resumo
Os cânceres de mama e de ovário estão entre os tumores mais comuns que acometem a população feminina. A grande taxa de mortalidade, característica de ambos os tipos, decorre do diagnóstico muitas vezes tardio e impreciso, o qual pode ser sanado através
do auxílio de ferramentas tecnológicas, como a Inteligência Artificial (IA). Objetivo: Avaliar a
influência da IA na identificação correta e precoce das neoplasias malignas supracitadas. Metodologia: Realizou-se uma revisão integrativa nas bases de dados da PubMed, SciELO, Web
of Science, LILACS e Scopus, nos quais foram selecionados artigos científicos publicados entre 2020 e 2025. Ao final da pesquisa, foram utilizados vinte e sete artigos nesta revisão. Resultados e Discussão: A utilidade da IA, analisada pelos artigos, variou desde algoritmos de
detecção precisa de tumores a criação de protocolos medicamentosos personalizados para
cada paciente. Um dos textos ressalta a grande capacidade desta tecnologia de armazenar
informações, que podem ser úteis para a decisão clínica do profissional da saúde, além da
habilidade denominada ‘self-learning’ que possibilita ao algoritmo um aperfeiçoamento através de seu próprio funcionamento. Conclusão: Assim, conclui-se que os dados, extraídos de
fontes científicas diversas, permitem a percepção de um padrão positivo no uso de Inteligência Artificial na medicina oncológica feminina em questão.
Referências
LOBO, L. C. Inteligência Artificial e Medicina. Revista Brasileira de Educação Médica, v. 41, n. 2, p.
–193, abr. 2017.
BADAWY, E. et al. Performance of AI-aided mammography in breast cancer diagnosis: Does
breast density matter? Egyptian Journal of Radiology and Nuclear Medicine, v. 54, n. 1, artigo 178, out. 2023.
BHATTACHARYA, S. et al. Empowering precision medicine: regenerative AI in breast cancer. Frontiers in Oncology, v. 14, artigo 1465720, set. 2024.
OBERIJE, C. J. G. et al. Comparing prognostic factors of cancers identified by artificial intelligence (AI) and human readers in breast cancer screening. Cancers, v. 15, n. 12, artigo 3069, jun. 2023.
XU, W. et al. Consistency of CSCO AI with multidisciplinary clinical decision-making teams in
breast cancer: a retrospective study. Breast Cancer: Targets and Therapy, v. 16, p. 413-422, 2024.
SANTERAMO, R. et al. Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study. Breast Cancer Research, v. 26, n. 1, artigo 25, fev. 2024.
ADACHI, M. et al. AI use in mammography for diagnosing metachronous contralateral breast
cancer. Journal of Imaging, v. 10, n. 9, artigo 211, set. 2024.
HIRSCH, L. et al. Early detection of breast cancer in MRI using AI. Academic Radiology, v. 32, n. 3,
p. 1218-1225, mar. 2025.
CHANG, Y. W. et al. Artificial intelligence for breast cancer screening in mammography (AISTREAM): a prospective multicenter study design in Korea using AI-based CADe/x. Journal of Breast Cancer, v. 25, n. 1, p. 57-68, fev. 2022.
ADUSUMILLI, P. et al. A methodological framework for AI-assisted diagnosis of ovarian masses
using CT and MR imaging. Journal of Personalized Medicine, v. 15, n. 2, p. 76, fev. 2025.
RAJPAL, S. et al. XAI-CNVMarker: explainable AI-based copy number variant biomarker discovery
for breast cancer subtypes. Biomedical Signal Processing and Control, v. 84, artigo 104979, jul. 2023.
HU, Zhongqian; YAN, Bing. Deep Learning-Assisted Intelligent Artificial Vision Platform Based on
Dual-Luminescence Eu(III)-Functionalized HOF for the Diagnosis of Breast and Ovarian Cancer. Analytical
Chemistry, [S. l.], v. 95, n. 51, p. 18889-18897, 26 dez. 2023.
EINOCH AMOR, R.; ZINGER, A.; BROZA, Y. Y.; SCHROEDER, A.; HAICK, H. Artificially Intelligent
Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers. Advanced Healthcare Materials,
[S. l.], v. 11, n. 17, p. e2200356, set. 2022.
CHAKRAVARTHY, S. et al. Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI. CMC-Computers, Materials & Continua, [S. l.], v. 80, n. 3, p. 5029-
, 2024.
ELHAKIM, M. T. et al. Breast cancer detection accuracy of AI in an entire screening population: a
retrospective, multicentre study. Cancer Imaging, v. 23, n. 1, artigo 127, 20 dez. 2023.
LI, G. et al. Transformer-based AI technology improves early ovarian cancer diagnosis using
cfDNA methylation markers. Cell Reports Medicine, v. 5, n. 8, p. 101666, 20 ago. 2024.
MARINOVICH, M. L. et al. Artificial intelligence (AI) for breast cancer screening: BreastScreen
population-based cohort study of cancer detection. EBioMedicine, v. 90, artigo 104498, abr. 2023.
LEVY, Y. et al. The fusion of wide field optical coherence tomography and AI: advancing breast
cancer surgical margin visualization. Life, v. 13, n. 12, artigo 2340, dez. 2023.
FATHIMA, M.; MOULANA, M. Revolutionizing breast cancer care: AI-enhanced diagnosis and patient history. Computer Methods in Biomechanics and Biomedical Engineering, v. 28, n. 5, p. 642-654, 4 abr.
WANDERLEY, M. C. et al. Application of artificial intelligence in predicting malignancy risk in
breast masses on ultrasound. Radiologia Brasileira, v. 56, n. 5, p. 229-234, set./out. 2023.
WU, X.; XIA, Y.; LOU, X. et al. Decoding breast cancer imaging trends: the role of AI and radiomics
through bibliometric insights. Breast Cancer Research, v. 27, artigo 29, 2025.
LAURITZEN, A. D. et al. Early indicators of the impact of using AI in mammography screening for
breast cancer. Radiology, p. 1-10, 4 jun. 2024.
MILITELLO, C. AI applied to breast cancer: early detection and explainable predictive models as
the basis of precision medicine. Academic Radiology, v. 32, n. 3, p. 1226-1227, 2025.
MIGDA, M. et al. Diagnostic value of the gynecology imaging reporting and data system (GIRADS) with the ovarian malignancy marker CA-125 in preoperative adnexal tumor assessment. Journal
of Ovarian Research, v. 11, n. 1, p. 92, 3 nov. 2018.
AMOR, F. et al. Gynecologic imaging reporting and data system: a new proposal for classifying
adnexal masses on the basis of sonographic findings. Journal of Ultrasound in Medicine, v. 28, n. 3, p. 285-
, mar. 2009.
SUNG, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality
worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, v. 71, n. 3, p. 209-249, 2021.
YANG, W.; DEMPSEY, P. J. Diagnostic breast ultrasound: current status and future directions.
Radiologic Clinics of North America, v. 45, n. 5, p. 845-861, 2007.
INSTITUTO NACIONAL DE CÂNCER. Estimativa 2023: incidência de câncer no Brasil. Rio de Janeiro: INCA, 2023.
GOVINDARAJAN, M. et al. High-throughput approaches for precision medicine in high-grade serous ovarian cancer. Journal of Hematology & Oncology, v. 13, n. 1, p. 134, 2020.
JACOBS, I. J.; MENON, U.; RYAN, A. et al. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. The Lancet, v.
, n. 10022, p. 945-956, 2016.
KAIJSER, J.; BOURNE, T.; VALENTIN, L. et al. Improving strategies for diagnosing ovarian cancer:
a summary of the International Ovarian Tumor Analysis (IOTA) studies. Ultrasound in Obstetrics & Gynecology, v. 41, n. 1, p. 9-20, 2013.
BITENCOURT, A.; DAIMIEL NARANJO, I.; LO GULLO, R. et al. AI-enhanced breast imaging: where
are we and where are we heading? European Journal of Radiology, v. 142, p. 109882, 2021.
RODRIGUEZ-RUIZ, A.; LANG, K.; GUBERN-MERIDA, A. et al. Stand-alone Artificial Intelligence for
Breast Cancer detection in Mammography: comparison with 101 radiologists. Journal of the National Cancer Institute, v. 111, n. 9, p. 916-922, 2019.
LOTTER, W.; DIAB, A. R.; HASLAM, B. et al. Robust Breast Cancer detection in mammography and
digital breast tomosynthesis using an annotation-efficient deep learning approach. Nature Medicine, v.
, n. 2, p. 244-249, 2021.
