Artificial Intelligence in ENT (Otolaryngology)

1. What is AI in the Context of Medicine?

• Machine Learning (ML): Algorithms that learn patterns from data without explicit programming
• Deep Learning (DL): A subset of ML using multi-layer neural networks, especially powerful for image analysis
• Convolutional Neural Networks (CNNs): Specifically designed for image classification tasks
• Natural Language Processing (NLP): Allows machines to interpret and generate human language (e.g., extracting data from clinical notes)
• Large Language Models (LLMs): Models like ChatGPT used for clinical decision support, education, and patient communication

2. AI in Otology (Ear)

Otoscopy and Tympanic Membrane Analysis

• Normal
• Acute otitis media (AOM)
• Otitis media with effusion (OME)
• Chronic otitis media (COM)
• Cholesteatoma
• Wax obstruction and perforation

• Image quality classification: 98.8% accuracy
• Ear laterality classification: 99.1% accuracy
• Disease classification: high accuracy across 2,964 annotated otoscopic images

Hearing Loss and Audiometry

• AI models predict sudden sensorineural hearing loss (SSNHL) outcomes using machine learning trained on audiometric and clinical data
• An AI model for predicting cochlear implant candidacy (Am J Otolaryngol, 2024) uses routine behavioral audiometry to identify adults who qualify for cochlear implants, streamlining referral pathways
• Deep learning-based sound coding strategies for cochlear implants improve speech intelligibility by incorporating noise reduction algorithms that reduce background interference in real time (PMID: 37030808)
• Audiogram interpretation, pure tone average calculation, and type-of-hearing-loss classification are being automated

Vestibular Disorders

• ML algorithms are being applied to differentiate Meniere's disease from vestibular migraine using speech discrimination and caloric-vHIT dissociation data
• Posturographic and EEG pattern analysis for vestibular dysfunction

3. AI in Rhinology (Nose and Sinuses)

Paranasal Sinus CT Analysis

• AI automates analysis of CT scans for chronic rhinosinusitis (CRS), grading sinus opacification (e.g., Lund-Mackay scoring) and predicting surgical outcomes
• DL models identify polyps, mucosal thickening, septal deviations, and anatomical variants relevant to FESS planning
• Radiomics extracts quantitative imaging features from MRI/CT that are invisible to the naked eye, aiding in tissue characterization and predicting recurrence after sinus surgery

Allergic Rhinitis (ARIA 2024 Initiative)

Obstructive Sleep Apnea (OSA)

• AI can screen for OSA using SpO2 data, respiratory sound analysis, pharyngeal imaging, and wearable sensor data
• AI algorithms analyze polysomnography recordings to automate sleep stage scoring - a task currently requiring expensive, time-consuming manual review
• NLP extracts OSA risk factors from unstructured EHR data for mass population screening
• Smartphone-based snore analysis using audio ML models is in active development

4. AI in Laryngology (Voice and Larynx)

Voice Disorder Detection

• Acoustic analysis using ML: AI models analyze voice recordings to detect dysphonia, vocal fold paralysis, laryngeal papillomatosis, and benign vocal fold lesions
• Features analyzed include jitter, shimmer, harmonics-to-noise ratio, and cepstral peak prominence
• CNN models applied to laryngoscopic video classify vocal fold pathology in real time, distinguishing malignant from benign lesions

Laryngeal Cancer

• AI assists in detecting early glottic and supraglottic carcinoma on white-light and narrow-band imaging (NBI) laryngoscopy
• Detection of vocal cord leukoplakia (a potentially malignant disorder) with high sensitivity using image-based DL
• AI models integrate clinicopathological and genomic markers in laryngopharyngeal cancer to guide prognosis and treatment personalization (Springer EJORL, 2025)

Speech and Swallowing

• NLP models process speech output from patients post-laryngectomy or with dysarthria to assess voice quality and rehabilitation progress
• AI-enhanced hearing aids and cochlear implant processors use noise reduction algorithms that adapt dynamically to acoustic environments

5. AI in Head and Neck Oncology

Oral Cancer

Nasopharyngeal Carcinoma (NPC)

• Automated tumor segmentation on MRI
• T-staging prediction and lymph node involvement classification
• Predicting radiation-induced toxicity (e.g., xerostomia, trismus)
• Predicting treatment response to concurrent chemoradiotherapy

Thyroid

Cervical Lymphadenopathy

6. AI in Robotic and Surgical ENT

• AI-assisted robotic systems map tumor boundaries intraoperatively, guiding safe resection margins while preserving critical neurovascular structures (facial nerve, carotid artery)
• Computer vision in transoral robotic surgery (TORS) identifies anatomical planes in real time
• AI tools analyze surgical video for intraoperative feedback, identify errors, and assess technical proficiency for training purposes
• Surgical planning: AI processes preoperative imaging to generate patient-specific 3D models of the skull base, paranasal sinuses, and temporal bone for FESS and cochlear implant surgery
• Drug repurposing algorithms screen large biomedical datasets to suggest novel compounds for head and neck cancer treatment

7. Generative AI and LLMs in ENT

• GPT-4 consistently outperformed GPT-3.5 in both education and CDS domains
• Hallucinations and performance variability noted in complex clinical reasoning tasks
• Best performance in structured, well-defined tasks; lowest in nuanced clinical reasoning

• Answering patient questions about postoperative care, chronic rhinosinusitis, hearing loss
• Generating readable patient education materials tailored to literacy level
• Preoperative counseling documents and consent form drafting
• Summarizing clinical notes and extracting structured data from EHRs (NLP)
• Assisting ENT residency education through case-based learning modules

8. Current Limitations and Challenges

9. Future Directions

• Multimodal AI: Combining imaging, genomics, pathology, and clinical data for precision oncology in H&N cancer
• Real-time surgical AI: Live intraoperative guidance using computer vision and augmented reality overlays
• AI-powered hearing devices: Adaptive algorithms that personalize sound processing per listener and environment
• Population-level screening: AI tools on smartphones enabling self-screening for ear infections, voice disorders, and oral cancer in low-resource settings
• Digital twins: Patient-specific computational models for surgical simulation and outcome prediction
• Federated learning: Training AI on distributed hospital datasets without sharing patient data, addressing privacy concerns while expanding training sets

Summary

• Hack et al., Auris Nasus Larynx 2025 - Generative AI meta-analysis in ENT (PMID: 40912131)
• Makitie et al., Adv Ther 2023 - AI in H&N cancer systematic review of reviews (PMID: 37291378)
• Taciuc et al., Biomed Rep 2024 - Neural networks in otolaryngology (PMID: 38765859)
• AAO-HNS AI Task Force Report, approved Nov 2025
• Demir et al., Eur Arch Otorhinolaryngol 2025 - Current trends and application areas