Artificial intelligence (AI) offers significant opportunities to enhance clinical hypnotherapy, including personalised hypnotic scripts, bio signal-informed monitoring, and between-session conversational support. Turning that promise into safe, effective practice requires rigorous methodological frameworks that address study design, data governance, human-in-the-loop safeguards, evaluation metrics, and ethical and regulatory considerations. This article outlines practical, clinician-focused frameworks to help practitioners and researchers develop, assess, and responsibly implement AI-enabled tools within hypnotherapeutic care.
Somatic Symptom Disorder (SSD), Medically Unexplained Symptoms (MUS), and Functional Neurological Disorder (FND) remain some of the most challenging conditions in clinical practice. Their symptoms are often distressing, persistent, and frequently resistant to standard medical approaches. Growing evidence shows that clinical hypnosis, including symptom-focused suggestions, gut-directed hypnotherapy, and hypnotic techniques that support relearning in FND, can help reduce symptom intensity and improve overall functioning.