Inteligência Artificial no Controle do Câncer: Uma Análise Bibliométrica Global de Tendências, Aplicações e Desafios de Implementação
DOI:
https://doi.org/10.32635/2176-9745.RBC.2026v72n2.5614Palavras-chave:
Inteligência Artificial, Bibliometria, Big Data, Indicadores de Produção Científica, Ciência, Tecnologia e SociedadeResumo
Introdução: A inteligência artificial (IA) tem sido cada vez mais aplicada ao longo do continuum de controle do câncer, abrangendo prevenção, detecção precoce, diagnóstico, tratamento e gestão dos sistemas de saúde. O rápido crescimento da pesquisa em IA em oncologia reflete diversidade metodológica e interesse global, com variabilidade nos contextos de implementação e validação. Objetivo: Mapear a produção científica global sobre aplicações de IA na prevenção e no controle do câncer, identificando tendências de publicação, distribuição geográfica, desenhos de estudo, tipos de IA, níveis de implementação e implicações para sistemas de saúde e políticas públicas. Método: Revisão bibliométrica conforme Donthu et al. Buscas em seis bases de dados em abril de 2025, guiadas por princípios PRISMA. Triagem e elegibilidade por dois revisores de forma independente, com terceiro para adjudicação. Variáveis: ano, autoria/afiliação, periódicos, países, palavras-chave, desenho, tipo de câncer, fase do controle, tipo de IA, nível de implementação, custo-efetividade e Big Data. Análise com Excel, VOSviewer e EndNote. Resultados: De 482 registros, 134 estudos foram incluídos. As publicações aumentaram após 2021, sobretudo em países de alta renda (EUA e China). Predominaram machine learning e deep learning. Cerca de um terço relatou aplicação clínica no mundo real; a maioria foi observacional, narrativa ou de modelagem. Poucos abordaram custo-efetividade ou Big Data em larga escala. Conclusão: Há crescimento e diversificação sustentados da IA no controle do câncer, com ênfase em diagnóstico e tratamento, níveis heterogêneos de implementação e relevância para organização do sistema e políticas, inclusive em contextos com poucos recursos.
Downloads
Referências
Jones OT, Matin RN, van der Schaar M, et al. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit Health. 2022;4(6):e466-76. doi: https://www.doi.org/10.1016/S2589-7500(22)00023-1 DOI: https://doi.org/10.1016/S2589-7500(22)00023-1
Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol. 2023;96:82-99. doi: https://www.doi.org/10.1016/j.semcancer.2023.09.005 DOI: https://doi.org/10.1016/j.semcancer.2023.09.005
Mitsala A, Tsalikidis C, Pitiakoudis M, et al. Artificial intelligence in colorectal cancer screening, diagnosis and treatment: a new era. Curr Oncol. 2021;28(3):1581-607. doi: https://www.doi.org/10.3390/curroncol28030149 DOI: https://doi.org/10.3390/curroncol28030149
Uwimana A, Gnecco G, Riccaboni M. Artificial intelligence for breast cancer detection and its health technology assessment: a scoping review. Comput Biol Med. 2025;184:109391. doi: https://www.doi.org/10.1016/j.compbiomed.2024.109391 DOI: https://doi.org/10.1016/j.compbiomed.2024.109391
Cortlana VM, Itodo K, Leyfman Y, et al. Artificial intelligence in cancer care: addressing challenges and health equity. Oncology. 2025;39(3):105-10. doi: https://www.doi.org/10.46883/2025.25921037 DOI: https://doi.org/10.46883/2025.25921037
Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med Phys. 2020;47(5):e185-202. doi: https://www.doi.org/10.1002/mp.13678 DOI: https://doi.org/10.1002/mp.13678
Issa NT, Stathias V, Schürer S, et al. Machine and deep learning approaches for cancer drug repurposing. Semin Cancer Biol. 2021;68:132-42. doi: https://www.doi.org/10.1016/j.semcancer.2019.12.011 DOI: https://doi.org/10.1016/j.semcancer.2019.12.011
Sultan AS, Elgharib MA, Tavares T, et al. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med. 2020;49(9):849-56. doi: https://www.doi.org/10.1111/jop.13042 DOI: https://doi.org/10.1111/jop.13042
Tran KA, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152. doi: https://www.doi.org/10.1186/s13073-021-00968-x DOI: https://doi.org/10.1186/s13073-021-00968-x
Liu Y, Ji Y, Chen J, et al. Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform. Theranostics. 2024;14(11):4240-55. doi: https://www.doi.org/10.7150/thno.94950 DOI: https://doi.org/10.7150/thno.94950
Lami K, Yoon HS, Parwani AV, et al. Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort. Pathology. 2024;56(5):633-42. doi: https://www.doi.org/10.1016/j.pathol.2024.02.009 DOI: https://doi.org/10.1016/j.pathol.2024.02.009
Lyman GH, Kuderer NM. Artificial intelligence in cancer clinical research: II. development and validation of clinical prediction models. Cancer Invest. 2024;42(6):447-51. doi: https://www.doi.org/10.1080/07357907.2024.2354991 DOI: https://doi.org/10.1080/07357907.2024.2354991
Raciti P, Sue J, Retamero JA, et al. Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection. Arch Pathol Lab Med. 2023;147(10):1178-85. doi: https://www.doi.org/10.5858/arpa.2023-0143-OA DOI: https://doi.org/10.5858/arpa.2022-0066-OA
Gordon ER, Trager MH, Kontos D, et al. Ethical considerations for artificial intelligence in dermatology: a scoping review. Br J Dermatol. 2024;190(6):789-97. doi: https://www.doi.org/10.1111/bjd.22123 DOI: https://doi.org/10.1093/bjd/ljae040
Shah SFH, Arecco D, Draper H, et al. Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses. Br J Dermatol. 2025;192(3):520-9. doi: https://www.doi.org/10.1093/bjd/ljae434 DOI: https://doi.org/10.1093/bjd/ljae434
Banatwala U, Ibrahim MT, Shaikh RH, et al. A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review. J Pak Med Assoc. 2024;74(Suppl 4):S90-6. doi: https://www.doi.org/10.47391/JPMA.AKU-9S-14 DOI: https://doi.org/10.47391/JPMA.AKU-9S-14
Mollura DJ, Culp MP, Pollack E, et al. Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology. 2020;297(3):513-20. doi: https://www.doi.org/10.1148/radiol.2020201434 DOI: https://doi.org/10.1148/radiol.2020201434
Souadka A. Artificial intelligence in surgical oncology: enhancing precision and personalized treatment in low- and middle-income countries. J Surg Oncol. 2024;130(2):341-2. doi: https://www.doi.org/10.1002/jso.27733 DOI: https://doi.org/10.1002/jso.27733
Donthu N, Kumar S, Mukherjee D, et al. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res. 2021;133:285-96. doi: https://www.doi.org/10.1016/j.jbusres.2021.04.070 DOI: https://doi.org/10.1016/j.jbusres.2021.04.070
Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. doi: https://www.doi.org/10.1136/bmj.n160 DOI: https://doi.org/10.1136/bmj.n160
Jiao Z, Hu P, Xu H, et al. Machine learning and deep learning in chemical health and safety: a systematic review of techniques and applications. J Chem Health Saf. 2020;27(6):316-34. doi: https://www.doi.org/10.1021/acs.chas.0c00075 DOI: https://doi.org/10.1021/acs.chas.0c00075
Kumar K, Chaudhury K, Tripathi SL. Future of machine learning and deep learning in healthcare monitoring system. In: Ghai D, Tripathi SL, Saxena S, et al. Machine learning algorithms for signal and image processing. Nova Jersey: Wiley; 2022. doi: https://www.doi.org/10.1002/9781119861850.ch17
Venkatasubramanian V, Chakraborty A. Quo vadis ChatGPT? From large language models to large knowledge models. Comput Chem Eng. 2025;192:108895. doi: https://doi.org/10.1016/j.compchemeng.2024.108895 DOI: https://doi.org/10.1016/j.compchemeng.2024.108895
Pais S, Cordeiro J, Jamil MLJ. NLP-based platform as a service: a brief review. J Biomed Data. 2022;9(1):54. doi: https://doi.org/10.1186/s40537-022-00603-5 DOI: https://doi.org/10.1186/s40537-022-00603-5
Ghimire P, Kim K, Acharya M. Generative AI in the construction industry: opportunities and challenges. arXiv [preprint]. 2023. doi: https://doi.org/10.48550/arXiv.2310.04427
Zhou N, Zhang CT, Lv HY, et al. Concordance study between IBM Watson for oncology and clinical practice for patients with cancer in China. Oncologist. 2019;24(6):812-9. doi: https://doi.org/10.1634/theoncologist.2018-0255 DOI: https://doi.org/10.1634/theoncologist.2018-0255
Park T, Gu P, Kim C-H, et al. Artificial intelligence in urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea. Prostate Int. 2023;11(4):218-21. doi: https://doi.org/10.1016/j.prnil.2023.09.001 DOI: https://doi.org/10.1016/j.prnil.2023.09.001
Dhull A, Gupta G. An intelligent two phase fuzzy decision tree based clustering model for design of computer aided detection/diagnosis (CADe/CADx) system. J Med Syst. 2018;33(1):63-75. doi: https://doi.org/10.1007/s12647-017-0230-8 DOI: https://doi.org/10.1007/s12647-017-0230-8
Khan N, Kumar S, Mukherjee D, et al. Global insights and the impact of generative AI–ChatGPT on multidisciplinary: a systematic review and bibliometric analysis. Cognit Syst Res. 2024;36(1):2353630. doi: https://doi.org/10.1080/09540091.2024.2353630 DOI: https://doi.org/10.36227/techrxiv.171466626.67294030/v1
Kelly B. Investigating ChatGPT search: insights from 80 million clickstream records [Internet]. Boston: Semrush; 2025 [cited 2025 Sep 29]. Available from: https://www.semrush.com/blog/chatgpt-search-insights/
Liu J, Wang X, Ye X, et al. Improved health outcomes of nasopharyngeal carcinoma patients 3 years after treatment by the AI-assisted home enteral nutrition management. Front Nutr. 2025;11:1481073. doi: https://doi.org/10.3389/fnut.2024.1481073 DOI: https://doi.org/10.3389/fnut.2024.1481073
Yli-Hietanen J, Ylipää A, Yli-Harja O. Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling. Chin J Cancer. 2015;34(10):423-6. doi: https://doi.org/10.1186/s40880-015-0008-8 DOI: https://doi.org/10.1186/s40880-015-0008-8
Bongurala AR, Save D, Virmani A. Progressive role of artificial intelligence in treatment decision-making in the field of medical oncology. Front Med. 2025;12:1533910. doi: https://doi.org/10.3389/fmed.2025.1533910 DOI: https://doi.org/10.3389/fmed.2025.1533910
Butt SR, Soulat A, Lal PM, et al. Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer. Ann Med Surg (Lond). 2024;86(3):1531-9. doi: https://doi.org/10.1097/MS9.0000000000001733 DOI: https://doi.org/10.1097/MS9.0000000000001733
Aziz M, Ejaz SA, Zargar S, et al. Deep learning and structure-based virtual screening for drug discovery against NEK7: a novel target for the treatment of cancer. Molecules. 2022;27(13):4098. doi: https://doi.org/10.3390/molecules27134098 DOI: https://doi.org/10.3390/molecules27134098
Chen ZH, Lin L, Wu CF, et al. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021;41(11):1100-15. doi: https://doi.org/10.1002/cac2.12215 DOI: https://doi.org/10.1002/cac2.12215
Kinikoglu O, Isik D. Evaluating the performance of ChatGPT-4 o oncology expert in comparison to standard medical oncology knowledge: a focus on treatment-related clinical questions. Cureus. 2025;17(1):e78076. doi: https://doi.org/10.7759/cureus.78076 DOI: https://doi.org/10.7759/cureus.78076
Alsayed AA, Aldajani MB, Aljohani MH, et al. Assessing the quality of AI information from ChatGPT regarding oral surgery, preventive dentistry, and oral cancer: an exploration study. Saudi Dent J. 2024;36(11):1483-9. doi: https://doi.org/10.1016/j.sdentj.2024.09.009 DOI: https://doi.org/10.1016/j.sdentj.2024.09.009
Ebner F, Hartkopf A, Veselinovic K, et al. A comparison of ChatGPT and multidisciplinary team meeting treatment recommendations in 10 consecutive cervical cancer patients. Cureus. 2024;16(8):e67458. doi: https://doi.org/10.7759/cureus.67458 DOI: https://doi.org/10.7759/cureus.67458
Liu C, Liu X, Wu F, et al. Using artificial intelligence (Watson for Oncology) for treatment recommendations amongst Chinese patients with lung cancer: feasibility study. J Med Internet Res. 2018;20(9):e11087. doi: https://doi.org/10.2196/11087 DOI: https://doi.org/10.2196/11087
Kim M, Park H, Kho B. Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res. 2020;9(3):507-14. doi: https://doi.org/10.21037/tlcr.2020.04.11 DOI: https://doi.org/10.21037/tlcr.2020.04.11
Hassan C, Povero M, Pradelli L, et al. Cost-utility analysis of real-time artificial intelligence-assisted colonoscopy in Italy. Endosc Int Open. 2023;11(11):E1046-55. doi: https://doi.org/10.1055/a-2136-3428 DOI: https://doi.org/10.1055/a-2136-3428
Areia M, Mori Y, Correale L, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health. 2022;4(6):e436-44. doi: https://doi.org/10.1016/S2589-7500(22)00042-5 DOI: https://doi.org/10.1016/S2589-7500(22)00042-5
Shen M, Zou Z, Bao H, et al. Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China. Lancet Reg Health West Pac. 2023;34:100726. doi: https://doi.org/10.1016/j.lanwpc.2023.100726 DOI: https://doi.org/10.1016/j.lanwpc.2023.100726
Du X, Hao S, Olsson H, et al. Effectiveness and cost-effectiveness of artificial intelligence–assisted pathology for prostate cancer diagnosis in Sweden: a microsimulation study. Eur Urol Oncol. 2025;8(1):80-6. doi: https://doi.org/10.1016/j.euo.2024.05.004 DOI: https://doi.org/10.1016/j.euo.2024.05.004
Winterstein JT, Abels J, Kuehn A, et al. AI-generated cancer prevention influencers can target risk groups on social media at low cost. Eur J Cancer. 2025;217:115251. doi: https://doi.org/10.1016/j.ejca.2025.115251 DOI: https://doi.org/10.1016/j.ejca.2025.115251
Charalambous A, Dodlek N. Big data, machine learning, and artificial intelligence to advance cancer care: opportunities and challenges. Semin Oncol Nurs. 2023;39(3):151429. doi: https://doi.org/10.1016/j.soncn.2023.151429 DOI: https://doi.org/10.1016/j.soncn.2023.151429
Nguyen MH, Tran ND, Le NQK. Big data and artificial intelligence in drug discovery for gastric cancer: current applications and future perspectives. Curr Med Chem. 2025;32(10):1968-86. doi: https://doi.org/10.2174/0929867331666230913105829 DOI: https://doi.org/10.2174/0929867331666230913105829
Guan P, Yu K, Wei W, et al. Big data analytics on lung cancer diagnosis framework with deep learning. Trans Comput Biol Bioinform. 2024;21(4):757-68. doi: https://doi.org/10.1109/TCBB.2023.3281638 DOI: https://doi.org/10.1109/TCBB.2023.3281638
Wei L, Huang Y, Chen Z, et al. Artificial intelligence combined with big data to predict lymph node involvement in prostate cancer: a population-based study. Front Oncol. 2021;11:763381. doi: https://doi.org/10.3389/fonc.2021.763381 DOI: https://doi.org/10.3389/fonc.2021.763381
Dlamini Z, Francies FZ, Hull R, et al. Artificial intelligence and big data in cancer and precision oncology. Comput Struct Biotechnol J. 2020;18:2300-11. doi: https://doi.org/10.1016/j.csbj.2020.08.019 DOI: https://doi.org/10.1016/j.csbj.2020.08.019
Suhail M, Tarique M, Tabrez S, et al. Leveraging AI and natural compounds: innovative approaches in the diagnosis and treatment of hepatocellular carcinoma. Curr Pharm Des. 2025;31:1-11. doi: https://doi.org/10.2174/0113816128364693250117060342 DOI: https://doi.org/10.2174/0113816128364693250117060342
Wang Y, Lin W, Zhuang X, et al. Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer. Oncol Rep. 2024;51(3):46. doi: https://doi.org/10.3892/or.2024.8705 DOI: https://doi.org/10.3892/or.2024.8705
Patel R, Jain A, Patel Z, et al. Artificial intelligence and machine learning in hepatocellular carcinoma screening, diagnosis and treatment: a comprehensive systematic review. Glob Acad J Med Sci. 2024;6(2):83-97. doi: https://doi.org/10.36348/gajms.2024.v06i02.007 DOI: https://doi.org/10.36348/gajms.2024.v06i02.007
Publicado
Como Citar
Edição
Seção
Licença
Os direitos morais e intelectuais dos artigos pertencem aos respectivos autores, que concedem à RBC o direito de publicação.
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
