Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review
AI systems for OSCC are maturing and clinically oriented. Before routine adoption, studies must demonstrate external validity, clinician-in-the-loop performance, calibration, and impact on time-to-diagnosis and patient outcomes. Pragmatic trials and transparent reporting are essential to move beyond proof-of-concept into equitable clinical benefit.
Authors
- Shin-ichiro Hiraoka (ORCID: https://orcid.org/0000-0002-0018-9523)
- Ryo Akiyama (ORCID: https://orcid.org/0000-0002-6185-3811)
- Susumu Tanaka (ORCID: https://orcid.org/0000-0003-4804-7839)
- Yutaka Itakura
- Narikazu Uzawa
- Kohei Kawamura
Institutions
- Osaka City University (JP)
- Osaka Dental University (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- International Journal of Clinical Oncology
- Published
- 2026-03-12
- DOI
- https://doi.org/10.1007/s10147-026-03002-5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society of Clinical Oncology