A novel AI-guided and motorized videolaryngoscope aiming to democratize endotracheal intubation

Background: Videolaryngoscopy has improved glottic visualization during intubation, but difficulties in tube advancement and hand-eye coordination continue to limit procedural efficiency. These ergonomic challenges contribute to failed intubations even when visualization is adequate. The present study evaluated a novel artificial intelligence guided and motorized videolaryngoscope (AI-VL) designed to reduce manual dexterity demands and operator workload. Methods: A preclinical validation was conducted using 5 Thiel-embalmed cadavers. Three operators with varying airway experience performed 30 intubations using the AI-VL. Primary outcomes were first-attempt success rate, intubation time, and visible airway trauma. Secondary outcomes included operator workload using the weighted NASA task load index (NASA-TLX) and post-procedural usability Likert questionnaire. Results: First-attempt success was 100% across all procedures, with a median intubation time of 14.3 s (IQR 11.0-22.8) and no visible tissue injury. The weighted NASA-TLX global workload was 29.1 ± 6.7 (0-100 scale), below the 25th percentile of published medical task norms, indicating low to moderate perceived demand. Mean usability ratings were 4.3 ± 0.6 on 5, with the highest scores for likelihood of clinical adoption and training suitability, 4.7 ± 0.6 and 5.0 ± 0.0 respectively. Conclusion: In this cadaveric study, the AI-VL preliminarily demonstrated consistent procedural success, short intubation times, relatively low operator workload, and high usability scores. These findings support its potential as a next generation videolaryngoscope and indicates the need for further clinical trials to explore its effectiveness across diverse clinical settings.