Artificial Intelligence in Chronic Rhinosinusitis: From Molecular Profiling to Personalized Clinical Management—A Systematic Review
6 affiliations
- 1School of Medicine, National Taiwan University, Taipei 100233, Taiwan
- 2Department of Otolaryngology Head and Neck Surgery, National Taiwan University Hospital, Taipei 100225, Taiwan
- 3Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701301, Taiwan
- 4Department of Otorhinolaryngology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- 5Department of Otolaryngology Head and Neck Surgery, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu 300195, Taiwan
- 6Institute of Medical Device and Imaging, National Taiwan University, Taipei 100233, Taiwan
Abstract
Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease imposing a substantial health and economic burden. Artificial intelligence (AI) has emerged as a promising tool to address CRS complexity in both clinical and research settings. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, and Embase for peer-reviewed articles utilizing machine learning, deep learning, and natural language processing, with a focus on molecular-level or immunologic mechanisms in CRS. Thirty-one studies were included in the qualitative synthesis. Our results demonstrate that while AI-driven analysis helps stratify patient endotypes, the identified schemes exhibit substantial heterogeneity in cluster numbers and features, raising concerns about their reproducibility. AI applications also included histopathological image interpretation, drug discovery, biologic therapy selection, and prognostic modeling to evaluate revision surgery risks and olfactory dysfunction. However, using the PROBAST+AI framework, we found that 87.1% of the assessed studies carry a high overall risk of bias, mainly due to the reliance on apparent performance or internal validation and limited independent evaluation of model performance, indicating that many reported performance estimates may be optimistic. Future large-scale, multicenter collaborations integrating AI into clinical workflows will be essential to establish reliable and generalizable AI applications for personalized CRS management.
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BibTeX
@article{huang2026artificial,
title = {{Artificial Intelligence in Chronic Rhinosinusitis: From Molecular Profiling to Personalized Clinical Management—A Systematic Review}},
author = {Chi-Sheng Huang and Duen-Lii Hsieh and Jin-Yu Lin and Maysayawan Sreesawat and Te-Huei Yeh and Chih-Feng Lin},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27198812},
url = {https://www.mdpi.com/1422-0067/27/19/8812},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Chronic Rhinosinusitis: From Molecular Profiling to Personalized Clinical Management—A Systematic Review
AU - Chi-Sheng Huang
AU - Duen-Lii Hsieh
AU - Jin-Yu Lin
AU - Maysayawan Sreesawat
AU - Te-Huei Yeh
AU - Chih-Feng Lin
PY - 2026
DA - 2026/10/01
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27198812
UR - https://www.mdpi.com/1422-0067/27/19/8812
AB - Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease imposing a substantial health and economic burden. Artificial intelligence (AI) has emerged as a promising tool to address CRS complexity in both clinical and research settings. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, and Embase for peer-reviewed articles utilizing machine learning, deep learning, and natural language processing, with a focus on molecular-level or immunologic mechanisms in CRS. Thirty-one studies were included in the qualitative synthesis. Our results demonstrate that while AI-driven analysis helps stratify patient endotypes, the identified schemes exhibit substantial heterogeneity in cluster numbers and features, raising concerns about their reproducibility. AI applications also included histopathological image interpretation, drug discovery, biologic therapy selection, and prognostic modeling to evaluate revision surgery risks and olfactory dysfunction. However, using the PROBAST+AI framework, we found that 87.1% of the assessed studies carry a high overall risk of bias, mainly due to the reliance on apparent performance or internal validation and limited independent evaluation of model performance, indicating that many reported performance estimates may be optimistic. Future large-scale, multicenter collaborations integrating AI into clinical workflows will be essential to establish reliable and generalizable AI applications for personalized CRS management.
SN - 1422-0067
ER -