IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v12y2025i9p3812-3817.html
   My bibliography  Save this article

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

Author

Listed:
  • Siddi Sathvik Kuruba

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

  • Dr. Kiran Kumar Kattappagari

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

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.

Suggested Citation

  • Siddi Sathvik Kuruba & Dr. Kiran Kumar Kattappagari, 2025. "An Analysis of Machine and Deep Learning Insights on the Use of Artificial Intelligence in Oral and Maxillofacial Pathology," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(9), pages 3812-3817, August.
  • Handle: RePEc:bjc:journl:v:12:y:2025:i:9:p:3812-3817
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/uploads/vol12-iss9-pg3812-3817-202510_pdf.pdf
    Download Restriction: no

    File URL: https://www.rsisinternational.org/journals/ijrsi/article.php?id=409
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:12:y:2025:i:9:p:3812-3817. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.