ReviewOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2025
American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Report on Artificial Intelligence.
Review in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Ambient Artificial Intelligence Scribes: A Scoping Review With Implications for Otolaryngology.The Laryngoscope · 2026Article
- Audiologist-Guided Multimodal AI for Pure-Tone Audiometry and Tympanometry Interpretation and Reporting.Journal of medical systems · 2026Article
- Turing problems in otolaryngology: a scoping review of the principal challenges of artificial ıntelligence and large language models.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- Comparative evaluation of four large language models (ChatGPT-5, Gemini 2.5 Pro, Claude Sonnet-4, and DeepSeek-R1) for diagnostic and therapeutic decision-making in real-world otolaryngology scenarios.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Artificial intelligence in trauma care: applications, ethical challenges, and pathways toward responsible integration.Current opinion in anaesthesiology · 2026Article
- Understanding and Mitigating Bias From Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026Review
- Navigating the challenges of artificial intelligence integration in thoracic surgery.Current challenges in thoracic surgery · 2025Article
- Tracheostomy in the Digital Age: How Artificial Intelligence and Immersive Technologies Are Redefining Airway Care.Tracheostomy (Warrenville, Ill.) · 2025Article
- Strategies to Minimize Virus Transmission During Anesthesia Procedures in COVID-19 Patients.Anesthesia and critical care (Houston, Tex.) · 2024Article
- Across-frequency behavioral estimates of the contribution of inner and outer hair cell dysfunction to individualized audiometric loss.Frontiers in neuroscience · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
This report synthesizes the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Task Force's guidance on the integration of artificial intelligence (AI) in otolaryngology-head and neck surgery (OHNS). A comprehensive literature review was conducted, focusing on the applications, benefits, and challenges of AI in OHNS, alongside ethical, legal, and social implications. The Task Force, formulated by otolaryngologist experts in AI, used an iterative approach, adapted from the Delphi method, to prioritize topics for inclusion and to reach a consensus on guiding principles. The Task Force's findings highlight AI's transformative potential for OHNS, offering potential advancements in precision medicine, clinical decision support, operational efficiency, research, and education. However, challenges such as data quality, health equity, privacy concerns, transparency, regulatory gaps, and ethical dilemmas necessitate careful navigation. Incorporating AI into otolaryngology practice in a safe, equitable, and patient-centered manner requires clinician judgment, transparent AI systems, and adherence to ethical and legal standards. The Task Force principles underscore the importance of otolaryngologists' involvement in AI's ethical development, implementation, and regulation to harness benefits while mitigating risks. The proposed principles inform the integration of AI in otolaryngology, aiming to enhance patient outcomes, clinician well-being, and efficiency of health care delivery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.