ENTERPRISE AI ANALYSIS
Future directions for otorhinolaryngology residency in the age of artificial intelligence: a review article
Artificial intelligence (AI) has achieved significant breakthroughs in various areas of medicine; however, its integration into otorhinolaryngology (ORL) residency training remains limited. AI can offer many opportunities to enhance ORL residency programs through better study methods, research facilitation, and clinical skills development. This review examines current literature on the possible future applications of AI in otolaryngology residency training, focusing on educational support, clinical applications, and research productivity. Studies on large language models (LLMs), deep learning (DL) platforms, and survey-based evaluations of specialists and residents were analyzed to highlight emerging trends, opportunities, and limitations. AI technologies such as large language models (LLMs) and deep learning (DL) can offer case-based simulations, exam preparation support, and automated feedback on surgical skills. They can also ease research by making literature reviews more efficient, strengthening data analysis, and helping refine study designs. Surveys show strong support among otolaryngology specialists and trainees for AI integration, though concerns remain about reliability, trust, and ethical use. Key limitations include the 'black box' nature of algorithms, limited datasets, and unfamiliarity with AI tools. Despite these challenges, AI holds great promise in supporting traditional teaching, enhancing diagnostic accuracy, and enriching clinical training in ORL residency programs. To achieve this, it must be implemented responsibly, with clear attention to transparency and ethical considerations.
Executive Impact Summary
The integration of AI into Otorhinolaryngology (ORL) residency training, while currently limited, presents significant opportunities across education, research, and clinical practice. AI tools, including Large Language Models (LLMs) and Deep Learning (DL), can revolutionize surgical skill assessment, diagnostic accuracy (e.g., tumor detection, hearing loss classification), and research efficiency. Surveys indicate strong support from ORL specialists and residents for AI integration, though ethical considerations, data reliability, and algorithm transparency remain key challenges. Responsible implementation with clear guidelines is crucial to harness AI's full potential in ORL.
ORL specialists believe AI offers valuable clinical applications
ORL specialists comfortable with AI for patient data management
AI accuracy on ORL residency in-service exams
AI sensitivity for head and neck tumor detection
Deep Analysis & Enterprise Applications
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AI offers significant advancements in educational support for ORL residents. This includes case-based simulations that provide real-time feedback, exam preparation support with practice questions and explanations, and automated feedback for surgical skill development using deep learning models. These tools enhance practical training, standardize skill assessment, and reinforce foundational knowledge, making learning more personalized and data-driven.
In clinical practice, AI can greatly improve diagnostic accuracy and decision-making for ORL residents. AI-powered tools can detect head and neck tumors, differentiate hearing loss types, and analyze complex CT images (e.g., sinonasal). Deep learning models, including CNNs, have shown comparable performance to experienced radiologists in evaluating adenoid size and detecting otitis media, aiding early recognition of clinically significant conditions and reducing subjectivity in diagnosis.
AI streamlines research processes for ORL residents by making literature reviews more efficient, strengthening data analysis, and assisting in refining study designs. LLMs can generate clinically relevant content, summarize complex topics, and aid in manuscript drafting, saving significant time for residents facing heavy workloads. This facilitates the production of high-quality research, which is often challenging during residency due to time constraints.
Enterprise Process Flow
Surgical Training (Skill assessment, Endoscopic surgery evaluation)
→
Communication & Clinical Reasoning (Simulated encounters, Decision-making support)
→
Research & Knowledge Reinforcement (Literature review, Exam prep)
→
Diagnostic Support (CT scans, Tumor detection, Audiograms)
of ORL specialists believe AI offers very valuable clinical applications.
A recent study found that a significant majority of otolaryngologists view AI as a valuable asset in clinical practice, particularly for enhancing diagnostic capabilities and patient data management. This indicates a high level of openness to integrating AI into ORL residency training.
| AI Application | AI Method Used | Residency Focus | How it Helps Residents | Study |
|---|---|---|---|---|
| Simulate real-life patient encounters | Large language models (LLMs) | Communication/clinical skills | By simulating patient encounters and providing real-time feedback on clinical decision-making and communication | Hicke et al. [15] |
| Surgical skills assessment | Deep learning (DL), deep neural networks (DNNs) | Surgical skills | By assessing their surgical skills and providing automated and standardized feedback without the need of another supervisor |
|
| Exam preparation and study support | LLMs | Studying and knowledge reinforcement | By generating practice questions, helping know the correct answers, and offering explanations for better exam preparations | Mahajan et al. [11] |
| Clinical decision support | LLMs | Clinical reasoning | By assisting in managing complex clinical scenarios by offering clinical suggestions and rationale | Park et al. [12] |
| Research assistance | LLMs | By helping with literature review, study designs, and manuscript drafting; saving time and improving research output |
|
|
| CT imaging interpretation (sinonasal) | DL and CNNs | Diagnosis | By training residents to recognize important findings in sinus CTs and enhance their diagnostic confidence | Chowdhury et al. [24] |
| Other imaging interpretations (adenoid size) | DL and CNNs | Diagnosis | By enabling early recognition of clinically significant adenoid hypertrophy and airway compromise |
|
| Tumor detection (head and neck) |
|
Diagnosis, prognosis, and treatment | By aiding in identifying head and neck cancer, its staging, drug innovation, surgical guidance, and treatment plans |
|
| Hearing loss classification | ML | Diagnosis | By teaching trainees to interpret audiograms, classify hearing loss, and predict outcomes based on patient data | Bing et al. [8] |
| Otitis media diagnosis |
|
Diagnosis | By improving the diagnostic accuracy of otitis media diseases, specifically in pediatric age groups | Navarathna et al. [26] |
AI for Enhanced Surgical Training: Deep Learning in Action
Deep Learning (DL) models can analyze surgical videos and motion data to provide automated, objective feedback on residents' technical skills. This standardizes evaluation, reduces reliance on constant human supervision, and accelerates skill development, particularly for complex procedures like endoscopic sinus or ear surgery.
Conclusion: DL's ability to offer detailed, step-by-step feedback addresses a critical challenge in surgical residency by providing consistent, data-driven skill assessment.
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