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Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep

Laura Iosif1, Marina Imre1, Andreea Gabriela Wagner1

  • 1Department of Prosthodontics, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 010232 Bucharest, Romania.

Diagnostics (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) shows promise for diagnosing orofacial pain (OFP). AI performs best for neurovascular and musculoskeletal pain, but challenges remain for odontogenic and neuropathic pain diagnosis.

Area of Science:

  • Dentistry and Oral Health
  • Neurology
  • Artificial Intelligence in Medicine

Background:

  • Orofacial pain (OFP) encompasses diverse conditions with overlapping symptoms, complicating accurate diagnosis.
  • Increasing diagnostic challenges drive interest in artificial intelligence (AI) for enhancing OFP diagnostic precision.

Purpose of the Study:

  • To review the application of AI in the diagnosis, classification, and prediction of adult OFP.
  • To assess the diagnostic performance of AI across different OFP categories.

Main Methods:

  • A narrative review of studies published between 2016-2026 was conducted.
  • Searches were performed in PubMed/MEDLINE, Scopus, and Web of Science.
  • Included studies applied AI to OFP diagnosis and reported at least two performance metrics.
Keywords:
artificial intelligencedental diagnosisdiagnosismaxillofacial painneuropathic painneurovascular painodontogenic painoralpain measurementtemporomandibular joint disorders

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Main Results:

  • AI demonstrated high and consistent diagnostic performance for neurovascular pain (e.g., migraine) and musculoskeletal pain (e.g., TMDs).
  • Odontogenic pain diagnosis showed lower and more variable AI performance, with imaging-based models yielding better results.
  • Neuropathic pain exhibited moderate to high performance in specific radiomics studies, but results were inconsistent due to variability.

Conclusions:

  • AI holds significant potential for OFP diagnosis, particularly for neurovascular and musculoskeletal pain.
  • Clinical translation is hindered by data heterogeneity and a lack of validation.
  • Future progress requires multimodal datasets and multicenter studies for robust and generalizable AI tools.