Artificial intelligence in Saudi otolaryngology research: a scoping review of the current landscape, gaps, and future directions
- 1Department of Otolaryngology, Head and Neck Surgery, College of Medicine, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
- 2Department of Otolaryngology, Head and Neck Surgery, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
- 3Department of Otolaryngology, Head and Neck Surgery, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Abstract
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 investigated AI applications or clinician perspectives within Saudi otolaryngology. The evidence was mapped across major otolaryngology domains, including cochlear implantation and hearing, rhinology and skull base surgery, facial plastic surgery, laryngology and voice, head and neck oncology, surgical and other ENT-related interventions for obstructive sleep apnoea, AI education and implementation, and AI-assisted patient education.
Results: The searches identified 2,925 database records, four records from other sources and seven records from an updated search and expert suggestions; thirteen studies met the inclusion criteria. The evidence covered AI awareness and education, large language model (LLM)-based patient education, cochlear implantation, rhinology and skull base surgery, facial plastic surgery, laryngology and voice, and head and neck oncology; no eligible study addressed surgical or other ENT-related interventions for obstructive sleep apnoea. Clinicians were generally positive about AI, while clinical studies showed promising performance of AI models. LLM chatbots generally gave accurate postoperative advice, although, when validated tools were used, their responses were harder to read and less actionable than patient-education standards recommend. However, limited AI education, small or non-Saudi datasets, limited external validation, and implementation barriers remained important challenges.
Conclusion: AI research in Saudi otolaryngology remains limited but is expanding across several clinical domains. Future research should prioritise Saudi-specific datasets, multicentre clinical validation, structured AI education, rigorous bilingual (Arabic and English) evaluation of LLM-generated patient information, and safe integration of AI into clinical practice in line with Saudi Vision 2030.
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BibTeX
@article{aldrees2026artificial,
title = {{Artificial intelligence in Saudi otolaryngology research: a scoping review of the current landscape, gaps, and future directions}},
author = {Turki Aldrees and Abdullah Alabdulqader and Mohammed Al-Bar and Feras Alkholaiwi},
journal = {Frontiers in Surgery},
year = {2026},
month = oct,
doi = {10.3389/fsurg.2026.1983298},
url = {https://doi.org/10.3389/fsurg.2026.1983298},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence in Saudi otolaryngology research: a scoping review of the current landscape, gaps, and future directions
AU - Turki Aldrees
AU - Abdullah Alabdulqader
AU - Mohammed Al-Bar
AU - Feras Alkholaiwi
PY - 2026
DA - 2026/10/21
JO - Frontiers in Surgery
DO - 10.3389/fsurg.2026.1983298
UR - https://doi.org/10.3389/fsurg.2026.1983298
AB - 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 investigated AI applications or clinician perspectives within Saudi otolaryngology. The evidence was mapped across major otolaryngology domains, including cochlear implantation and hearing, rhinology and skull base surgery, facial plastic surgery, laryngology and voice, head and neck oncology, surgical and other ENT-related interventions for obstructive sleep apnoea, AI education and implementation, and AI-assisted patient education. Results: The searches identified 2,925 database records, four records from other sources and seven records from an updated search and expert suggestions; thirteen studies met the inclusion criteria. The evidence covered AI awareness and education, large language model (LLM)-based patient education, cochlear implantation, rhinology and skull base surgery, facial plastic surgery, laryngology and voice, and head and neck oncology; no eligible study addressed surgical or other ENT-related interventions for obstructive sleep apnoea. Clinicians were generally positive about AI, while clinical studies showed promising performance of AI models. LLM chatbots generally gave accurate postoperative advice, although, when validated tools were used, their responses were harder to read and less actionable than patient-education standards recommend. However, limited AI education, small or non-Saudi datasets, limited external validation, and implementation barriers remained important challenges. Conclusion: AI research in Saudi otolaryngology remains limited but is expanding across several clinical domains. Future research should prioritise Saudi-specific datasets, multicentre clinical validation, structured AI education, rigorous bilingual (Arabic and English) evaluation of LLM-generated patient information, and safe integration of AI into clinical practice in line with Saudi Vision 2030.
SN - 2296-875X
ER -