ReviewCurrent opinion in otolaryngology & head and neck surgery2026
Artificial intelligence in head and neck cancer rehabilitation services: current state and future perspectives.
Review in Current opinion in otolaryngology & head and neck surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThis 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
findingsHNC 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.
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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.