AI-Assisted Early Orthodontic Screening and Referral Pathways for Saudi School-Age Children: Impact on Access, Treatment Efficiency, and Malocclusion Outcomes

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@article{1717822,
      author = {Afreen Kauser},
      title = {AI-Assisted Early Orthodontic Screening and Referral Pathways for Saudi School-Age Children: Impact on Access, Treatment Efficiency, and Malocclusion Outcomes},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {3567-3577},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717822.pdf},
      abstract = {Background: Malocclusion is common in childhood and adolescence, yet many children reach specialist care only after growth-modification opportunities have narrowed. Saudi Arabia's expanding digital health infrastructure creates a timely opportunity to connect school oral-health checks with AI-assisted orthodontic triage. Aim: This integrative review evaluates how AI-assisted early orthodontic screening and referral pathways could improve access, treatment efficiency and malocclusion-related outcomes among Saudi school-age children. Methods: A structured literature review was designed in line with PRISMA 2020 principles, focusing on English-language evidence from 2020 to 2025. Evidence was synthesised across five domains: population need, AI-enabled diagnosis, remote monitoring, patient-centred outcomes and governance. Findings: Recent orthodontic AI studies report useful performance in cephalometric landmarking, treatment-duration prediction, extraction planning and remote monitoring, but most tools remain adjuncts rather than autonomous clinical systems. School-based deployment is most defensible when AI performs calibrated risk stratification, while orthodontists retain diagnostic responsibility. In Saudi Arabia, potential benefits include earlier identification of severe overjet, crossbite, crowding and psychosocially significant aesthetic concerns; shorter avoidable delays; more consistent referral prioritisation; and better parent-school-clinic communication. The risks are equally material: dataset bias, privacy breaches, algorithmic opacity, over-referral, and inequitable digital access. Conclusion: AI-assisted screening can strengthen Saudi paediatric orthodontic pathways if it is embedded in a human-supervised, consent-based, auditable service model. Priority should be given to multicentre validation, Arabic interface usability, school-nurse training, referral feedback loops and outcome metrics beyond technical accuracy.},
      keywords = {Artificial Intelligence, Orthodontics, School Health, Saudi Arabia, Malocclusion, Referral Pathway, Teledentistry, Early Screening},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717822}
  }