Título :	The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods : an overview of the systematic reviews
Autor :	Silva, Helbert Eustáquio Cardoso da Santos, Glaucia Nize Martins Leite, André Ferreira Mesquita, Carla Ruffeil Moreira Figueiredo, Paulo Tadeu de Souza Stefani, Cristine Miron Melo, Nilce Santos de
metadata.dc.identifier.orcid:	https://orcid.org/0000-0003-2662-6987 https://orcid.org/0000-0003-4712-9779
metadata.dc.contributor.affiliation:	Brasilia University, Faculty of Health Science, Dentistry of Department Brasilia University, Faculty of Health Science, Dentistry of Department Brasilia University, Faculty of Health Science, Dentistry of Department Brasilia University, Faculty of Health Science, Dentistry of Department Brasilia University, Faculty of Health Science, Dentistry of Department Brasilia University, Faculty of Health Science, Dentistry of Department Brasilia University, Faculty of Health Science, Dentistry of Department
Assunto::	Inteligência artificial Diagnóstico por imagem Câncer - diagnóstico
Fecha de publicación :	5-oct-2023
Editorial :	Plos One
Citación :	SILVA, Helbert Eustáquio Cardoso da et al. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods : an overview of the systematic reviews. PLoS ONE, v. 18, n. 10, e0292063, 2023. DOI: https://doi.org/10.1371/journal.pone.0292063. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292063. Acesso em: 22 jan. 2024.
Abstract:	Background and purpose In comparison to conventional medical imaging diagnostic modalities, the aim of this over view article is to analyze the accuracy of the application of Artificial Intelligence (AI) tech niques in the identification and diagnosis of malignant tumors in adult patients. Data sources The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately. Results In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 sat isfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malig nant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis.Conclusions The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal stud ies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.
metadata.dc.description.unidade:	Faculdade de Ciências da Saúde (FS) Departamento de Odontologia (FS ODT)
metadata.dc.description.ppg:	Programa de Pós-Graduação em Odontologia
Licença::	Copyright: © 2023 Silva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI:	https://doi.org/10.1371/journal.pone.0292063
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