Clinical bottom line
Integrating AI-driven virtual reality (VR) into respiratory muscle training (RMT) for patients with neuromuscular diseases represents a promising, yet still exploratory, approach to enhance engagement, precision, and functional outcomes. Early evidence suggests that immersive VR environments combined with AI algorithms can tailor respiratory exercises to individual patient needs, potentially improving adherence and neuromuscular control. However, robust clinical trials specifically targeting respiratory muscle rehabilitation in neuromuscular populations remain limited. Clinicians should consider this technology as a putative adjunct to conventional RMT, pending further validation.
What the evidence shows
Neuromuscular diseases often impair respiratory muscle strength and endurance, contributing to morbidity and mortality. Conventional respiratory muscle training aims to improve these parameters but faces challenges including patient motivation and the need for individualized progression. AI-enhanced VR platforms offer immersive, interactive environments that can adapt in real time to patient performance, potentially overcoming these barriers.
Recent reviews and experimental studies highlight the potential of AI-driven VR to transform rehabilitation by providing personalized feedback, gamified tasks, and objective performance assessment (Pasupuleti Murali et al., 2024; İbişağaoğlu et al., 2024). These systems leverage AI algorithms to analyze user data continuously, adjusting difficulty and targeting specific motor control deficits, which is critical in neuromuscular disease rehabilitation (Duvuru et al., 2024).
While most VR rehabilitation research has focused on limb motor control and balance (Kristiina et al., 2012; Cui et al., 2025), emerging work suggests applicability to respiratory training. The immersive nature of VR can enhance patient engagement during repetitive respiratory exercises, potentially improving adherence and outcomes. AI integration allows for dynamic assessment and progression, which may optimize respiratory muscle loading and neuromuscular adaptation (Duvuru et al., 2024).
Furthermore, VR combined with AI has demonstrated benefits in related neuromuscular rehabilitation domains, such as hand exoskeleton training and cognitive screening, supporting the feasibility of this approach for respiratory muscles (Cui et al., 2025; Sarantopoulos et al., 2025). These findings underscore the putative value of AI-driven VR in delivering personalized, adaptive rehabilitation protocols.
Caveats and uncertainty
Despite promising theoretical and preliminary data, direct evidence for AI-driven VR in respiratory muscle training among neuromuscular disease patients is sparse. Most studies to date are proof-of-concept or pilot investigations with small sample sizes and heterogeneous populations (Duvuru et al., 2024). The optimal VR content, AI algorithms, and training parameters for respiratory muscle rehabilitation remain undefined.
Additionally, the transferability of VR-based respiratory training improvements to real-world functional outcomes, such as reduced dyspnea or improved pulmonary function, requires rigorous validation. Technical challenges include ensuring accurate respiratory muscle load measurement and integrating these metrics into AI-driven adaptive protocols.
Patient factors such as cognitive impairment, fatigue, and disease severity may limit VR usability and adherence. Accessibility and cost considerations also pose barriers to widespread clinical implementation. Finally, long-term safety and efficacy data are lacking, necessitating cautious interpretation.
How this may change practice
If validated, AI-driven VR respiratory muscle training could complement existing rehabilitation approaches by enhancing patient motivation, enabling personalized exercise progression, and providing objective performance monitoring. This technology may be particularly useful in home-based or tele-rehabilitation settings, where clinician supervision is limited.
Clinicians should remain informed about ongoing developments and consider enrolling appropriate patients in clinical trials evaluating these interventions. Integration with multidisciplinary care, including pulmonology and physical therapy, will be essential to optimize outcomes.
In the near term, AI-driven VR may serve as an adjunct tool to conventional RMT, especially for patients struggling with engagement or requiring tailored exercise regimens. However, adoption should be guided by emerging evidence and institutional resources, with attention to patient safety and usability.