Early detection in oral cancer - are we ready for AI-driven precision?

Sep 29, 2026

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Oral cancer, especially oral squamous cell carcinoma, poses a major health burden due to poor prognosis and late diagnosis. Leukoplakia, erythroplakia, lichen planus, and submucous fibrosis exemplify oral potentially malignant disorders, yet conventional diagnostics are slow, subjective, and inconsistent, delaying treatment and reducing survival. Artificial intelligence and precision medicine enable individualized care and diagnostic accuracy by leveraging machine and deep learning from fluorescence to hyperspectral imaging. Smartphone-based AI shows expert-level lesion identification, and biomarker- and multi-omics approaches support tailored therapy, while explainability, privacy, and bias mitigation remain essential.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages2

Reading Time5 min

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Abstract

Editorial letter

Clinical importance of early oral cancer detection

Limitations of conventional diagnostic tools

AI and precision medicine: diagnostic and therapeutic potential

Evidence from smartphone-based and deep learning systems

Precision oncology via biomarkers and multi-omics

Key challenges: privacy, bias, and explainable AI needs

Editorial Early detection in oral cancer: are we ready for AI-driven precision? Sinha Kumari, MBBSa , Nikil Kumar, MBBSb , Muhamma Saad Khan, MBBSa , Sadia Sultana, MBBSc , Muddassir Khalid, MBBSd ,* Abstract Oral cancer, particularly oral squamous cell carcinoma, remains a serious health concern, with a poor prognosis and a late diagnosis. Leukoplakia, erythroplakia, lichen planus, and submucous fibrosis are examples of oral potentially malignant illnesses. However, traditional diagnostic approaches are typically laborious, subjective, and unreliable, leading to delayed diagnosis – when therapy options are limited and survival is compromised. In oral cancer, artificial intelligence (AI) and precision medicine are becoming game-changing technologies that enhance individualized care, treatment planning, and diagnostic precision. Machine learning and deep learning algorithms, particularly convolutional neural networks, can analyze massive, complex datasets from fluorescence to hyperspectral imaging, revealing patterns that are beyond human detection. Recent trials have shown AI systems based on smartphones have demonstrated expert-level accuracy in identifying oral lesions in recent experiments. Through the discovery of biomarkers and the integration of several omics, AI-driven precision medicine also makes customized treatments possible. Nonetheless, issues with patient privacy, data bias, and the opaque “black box” nature of AI systems persist. The future of proactive and individualized oral cancer therapy relies on creating Explainable AI and strong ethical frameworks that encourage transparency, trust, and equitable integration. Keywords: artificial intelligence, oral cancer care, personalized treatment Dear Editor, Oral cancer remains one of the most challenging malignancies, with poor prognosis and substantial morbidity despite advances in therapy[1] . The most common form, oral squamous cell carcinoma (OSCC), is often preceded by oral potentially malignant disorders, such as leukoplakia, erythroplakia, lichen planus, and submucous fibrosis [2] . Detecting these disorders early is critical, yet conventional diagnostic tools such as oral examination, biopsy, and histopathology are slow, subjective, and prone to fluctuation. The ongoing reliance on these procedures contributes to late diagnosis, when therapeutic options are restricted and survival is compromised. This manuscript follows the TITAN guidelines to explore the use of artificial intelligence aDepartment of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan, bDepartment of Medicine, Liaquat University of Medical and Health Sciences, Jamshoro, Pakistan, cDepartment of Medicine, Azad Jammu and Kashmir Medical College, Muzafarabad, Pakistan and dDepartment of Medicine, Nishtar Medical University, Multan, Pakistan Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. *Corresponding author. Address: Department of Medicine, Nishtar Medical University, Nishtar Road, Multan 60000, Pakistan. Tel.: +923330333544. [E-mail: dr.muddassirkhalid@gmail.com](E-mail: dr.muddassirkhalid@gmail.com) (M. Khalid). Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Annals of Medicine & Surgery (2026) 88:32–33 Received 11 November 2025; Accepted 12 November 2025 Published online 3 December 2025 [http://dx.doi.org/10.1097/MS9.0000000000004418](http://dx.doi.org/10.1097/MS9.0000000000004418) (AI) and precision medicine for the diagnostic, therapeutic, and early detection of OSCC[3] . AI and precision medicine represent a revolutionary prospect in oral oncology. AI-powered technologies are improving diagnostics’ precision, repeatability, and

FAQs

Why is early detection of oral cancer critical?

Oral cancer has poor prognosis, and oral potentially malignant disorders often progress before diagnosis. Detecting these conditions early improves the chance of earlier treatment when therapeutic options are broader.

What limitations affect traditional diagnostic methods?

Conventional tools such as oral examination, biopsy, and histopathology are slow, subjective, and prone to variability, which contributes to delayed diagnosis.

How can AI-driven precision medicine improve diagnosis and treatment planning?

Machine learning and deep learning can analyze large imaging datasets and reveal patterns beyond human detection. By integrating biomarkers and multi-omics, AI-enabled precision medicine supports individualized treatment decisions.

Sep 29, 2026

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