An Analysis of Machine and Deep Learning Insights on the Use of Artificial Intelligence in Oral and Maxillofacial Pathology

Authors

Siddi Sathvik Kuruba

B.Tech students, Department of Computer Science Engineering, School of Engineering and Sciences, SRM University-AP, Andhra Pradesh (India)

Dr. Kiran Kumar Kattappagari

Professor & HOD, Department of Oral & Maxillofacial Pathology and Oral Microbiology, Sibar Institute of Dental Sciences, Guntur, Andhra Pradesh (India)

Article Information

DOI: 10.51244/IJRSI.2025.120800341

Subject Category: Microbiology

Volume/Issue: 12/9 | Page No: 3812-3817

Publication Timeline

Submitted: 2025-09-27

Accepted: 2025-10-03

Published: 2025-10-13

Abstract

Significant equipment advancements have occurred in the medical field over the years, and medical imaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, mammography, and X-rays are essential for the precise diagnosis and efficient treatment of many diseases. Artificial intelligence (AI), which is intended to replicate the human brain's capacity to process information and produce outputs based on data inputs, is becoming more and more prevalent nowadays. Because of its many uses and enormous promise, artificial intelligence is currently being actively embraced in the healthcare sector. Diagnostic accuracy may be impacted by rising workloads, the complexity of medical procedures, and the possibility of human weariness. By increasing productivity and assisting medical and dental personnel in making better judgments, the incorporation of AI into dental especially oral pathological histopathological imaging systems helps to lessen this burden. AI systems are faster and more accurate than humans at analysing vast amounts of data, and they can even more precisely identify some types of cancer. This review proposals a thorough introduction to artificial intelligence (AI), focuses on current advancements in oral pathology, and considers potential future uses for AI in Oral pathological lesions.

Keywords

AI, Oral pathological lesions, Deep Learning, Microbiology

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References

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