Current opinion in otolaryngology & head and neck surgery · 2026
Artificial intelligence in head and neck cancer rehabilitation services: current state and future perspectives.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
van Koevorden Josephine Willemijn, van Sluis Klaske Elisabeth, van der Molen Lisette
Аннотация
PURPOSE OF REVIEW: This review evaluates the current state of artificial intelligence (AI) in head and neck cancer (HNC) rehabilitation services by mapping current applications across rehabilitation pathways and allied healthcare professionals (AHPs), and outlines future directions and recommendations for integrating AI into routine healthcare.
RECENT FINDINGS: HNC care is highly specialized and requires intensive collaboration among many professionals, yet rehabilitation services remain fragmented, and AI developments are limited. More broadly, AI applications are emerging across multiple rehabilitation domains, including speech and swallowing assessment, motion analysis, nutrition support, mental health detection, and administrative automation. The most mature tools with high Technology Readiness Levels (TRLs) support patient education, telehealth communication, and real-time monitoring, particularly in dietetics, physiotherapy, and speech therapy. Low TRLs - such as multidisciplinary decision support, personalized exercise planning, mental health detection, and automated speech disorder assessment - remain in early development. Major barriers include fragmented data infrastructures, limited representativeness of HNC populations, ethical concerns, and the need to preserve the therapeutic patient-provider relationship. large language models (LLMs) show potential for structuring information and generating rehabilitation plans but require expert oversight.
SUMMARY: AI has the potential to enhance coordinative rehabilitation and shows promise for both health care providers and patients. However, meaningful progress toward clinical implementation requires rigorous validation, the development of shared standards, and strong interdisciplinary collaboration.
RECENT FINDINGS: HNC care is highly specialized and requires intensive collaboration among many professionals, yet rehabilitation services remain fragmented, and AI developments are limited. More broadly, AI applications are emerging across multiple rehabilitation domains, including speech and swallowing assessment, motion analysis, nutrition support, mental health detection, and administrative automation. The most mature tools with high Technology Readiness Levels (TRLs) support patient education, telehealth communication, and real-time monitoring, particularly in dietetics, physiotherapy, and speech therapy. Low TRLs - such as multidisciplinary decision support, personalized exercise planning, mental health detection, and automated speech disorder assessment - remain in early development. Major barriers include fragmented data infrastructures, limited representativeness of HNC populations, ethical concerns, and the need to preserve the therapeutic patient-provider relationship. large language models (LLMs) show potential for structuring information and generating rehabilitation plans but require expert oversight.
SUMMARY: AI has the potential to enhance coordinative rehabilitation and shows promise for both health care providers and patients. However, meaningful progress toward clinical implementation requires rigorous validation, the development of shared standards, and strong interdisciplinary collaboration.