Research Dynamics and Trends of Machine Learning in 
Oral Cancer

Yifan Zhao; Ran Li; Minjia Tao; Liming Wen*; Chunying Zhang*

doi:10.70711/pmr.v2i5.6720

Research Dynamics and Trends of Machine Learning in Oral Cancer

Yifan Zhao, Ran Li, Minjia Tao, Liming Wen*, Chunying Zhang*

Abstract

Objective: The integration of machine learning (ML) in oral cancer research has garnered increasing scholarly interest, particularly in early diagnosis, personalized treatment, and prognosis prediction. This study aims to analyze recent advancements and emerging
trends in ML applications within the field of oral cancer, providing insights for future research and industrial development. Methods: Relevant literature from 2015 to 2025 was retrieved from the Web of Science core database using a structured search strategy. Bibliometric
analysis was conducted using CiteSpace software to examine authors, institutions, countries, and keyword trends. Keyword emergence
analysis was performed to identify current research hotspots. Results: A total of 275 publications were analyzed. Findings indicate a significant rise in ML-related oral cancer research since 2020, primarily focusing on diagnosis, treatment prediction, and personalized therapies for oral squamous cell carcinoma. High-frequency keywords include deep learning, artificial neural network, early diagnosis
and prognosis prediction. Keyword emergence analysis revealed that the current research hotspots are deep learning and tumor feature extraction. Conclusion: Machine learning holds great promise in oral cancer research, particularly in early diagnosis and precision
medicine. With ongoing technological advancements and data accumulation, ML is expected to play an increasingly crucial role in clinical
applications and scientific exploration.

Keywords

Oral cancer; Machine learning; Artificial intelligence; Bibliometrics; CiteSpace

Full Text:

PDF

Included Database

References

[1] Garca-Pola M, Pons-Fuster E, Surez-Fernndez C, et al. Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review[J]. Cancers, 2021, 13(18): 4600.

[2] Alabi R O, Almangush A, Elmusrati M, et al. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision

Medicine[J]. Frontiers in Oral Health, 2022, 2.

[3] Chen C, Song M. Visualizing a field of research: A methodology of systematic scientometric reviews[J]. PLOS ONE, 2019, 14(10):

e0223994.

[4] Mukherjee D, Lim W M, Kumar S, et al. Guidelines for advancing theory and practice through bibliometric research[J]. Journal of Business Research, 2022, 148: 101-115.

[5] Lian W, Lindblad J, Runow Stark C, et al. Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection[J]. Computers in Biology and Medicine, 2025, 185: 109498.

[6] Xu X, Xi L, Wei L, et al. Deep learning assisted contrast-enhanced CTbased diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases[J]. European Radiology, 2023, 33(6): 4303-4312.

DOI: http://dx.doi.org/10.70711/pmr.v2i5.6720

Refbacks

There are currently no refbacks.