Fusão de Dados Multimodais em Patologia Digital e Texto Clínico: Uma Revisão Sistemática Aplicada à Oncologia Odontológica
Resumo
The diagnosis and prognosis of oral cancer fundamentally depend on the analysis of histopathological images and patient clinical history. Despite the advances of artificial intelligence in isolated image and text analysis, the fragmentation of these modalities limits the potential of clinical decision support systems. To investigate the state-of-the-art and map how the technological frontier is addressing this problem, this paper presents a systematic literature review focused on the integration of computer vision and natural language processing models applied to dental oncology. Conducted under the Kitchenham guidelines and the PRISMA protocol, the search covered five scientific databases (ACM, IEEE, Scopus, WoS, and PubMed) limited to the period from 2021 to 2026. The initial screening of 511 records resulted in 90 primary studies of high methodological quality. The findings demonstrate that although advanced architectures such as vision transformers and large language models are widely explored unimodally, multimodal fusion approaches (early, late, or joint fusion) and systems aimed at automated treatment planning are still scarce. We conclude that the development of integrated and explainable multimodal frameworks represents the main gap and research opportunity for digital oral health.
Palavras-chave:
Artificial Intelligence, Multimodal Fusion, Oral Cancer, Systematic Review, Clinical Decision Support Systems
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Publicado
27/10/2026
Como Citar
NICOLETTI, Bianca Rocha; GARCÍA, Raúl Eduardo Sousa; LEMBOR, Victor Hugo da Silva; MACEDO, Alessandra Alaniz.
Fusão de Dados Multimodais em Patologia Digital e Texto Clínico: Uma Revisão Sistemática Aplicada à Oncologia Odontológica. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 32. , 2026, Lavras/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 622-636.
DOI: https://doi.org/10.5753/webmedia.2026.30947.
