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TRUVACE RECORD VERSION
record: TRV-2026-2160
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-22T10:42:23.610161Z
status: published
lens: p_space
sector: health
headline: Artificial intelligence in Saudi otolaryngology research: a scoping review of the current landscape, gaps, and future directions
dek: Background Artificial intelligence (AI) is increasingly used in otolaryngology, but the extent and focus of AI research in Saudi Arabia remain unclear. This scoping review mapped the available evidence and identified key research themes and knowledge gaps. Methods A scoping review was conducted following the PRISMA-ScR guidelines. PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and the Saudi Medical Journal archive were searched for studies published between January 2019 and April 2026. Eligible studies…
gain_title: (none)
problem_title: Saudi ENT AI evidence is limited by small or non-Saudi datasets, little external validation, weak AI education, and chatbot answers that were harder to read and less actionable than standards recommend.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Saudi ENT AI evidence is limited by small or non-Saudi datasets, little external validation, weak AI education, and chatbot answers that were harder to read and less actionable than standards recommend.
problem_evidence: limited AI education, small or non-Saudi datasets, limited external validation, and implementation barriers remained important challenges | their responses were harder to read and less actionable than patient-education standards recommend
quick_read: A scoping review searched five literature sources for work published between January 2019 and April 2026 on AI or clinician views of AI in Saudi ear, nose and throat medicine. Thirteen studies qualified, spanning awareness and education, chatbot-based patient education, cochlear implants, rhinology, facial plastic surgery, voice, and head and neck cancer.

The mapping shows that AI work in this specialty is growing but still thin, which limits how far its results can guide clinical adoption. Whether models trained on small or non-Saudi data work for local patients, and whether Arabic and English patient information is adequate, remain unresolved.
limitation: The evidence base is small, and the included studies often relied on small or non-Saudi datasets with limited external validation, so the promising performance results cannot yet be treated as clinically validated locally.
tag: Evidence-backed problem
key_points: Database searches found 2,925 records, plus four from other sources and seven from an updated search and expert suggestions; thirteen studies were included. | No eligible study addressed surgical or other ENT interventions for obstructive sleep apnoea. | Clinicians were generally positive about AI, and clinical studies reported promising model performance. | Chatbot postoperative advice was generally accurate, but validated readability and actionability tools rated it below patient-education standards. | The authors call for Saudi-specific datasets, multicentre validation, structured AI education, and bilingual evaluation of LLM patient information.
rundown: The review followed PRISMA-ScR guidelines and searched PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and the Saudi Medical Journal archive. Eligible studies examined AI applications or clinician perspectives within Saudi otolaryngology.

Evidence was grouped by domain, including cochlear implantation and hearing, rhinology and skull base surgery, facial plastic surgery, laryngology and voice, head and neck oncology, AI education, and AI-assisted patient education. The sleep apnoea domain had no eligible studies.

The authors conclude that research is limited but expanding, and recommend safe integration of AI into clinical practice in line with Saudi Vision 2030.
sources:
- peer_reviewed | Frontiers in Surgery | https://doi.org/10.3389/fsurg.2026.1983298 | 2026-10-21
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