Why the Change Is Coming

• ► Rehabilitation is becoming data-rich but decision-poor
• ► Black-box AI lacks transparency and cannot meet clinical accountability standards
• ► Regulations will soon require explainable AI, powered by Knowledge Graph (KG) + ontologies

The Role of AI + KG Ontologies

Integrate biomechanics, patient history, and treatment protocols into a single reasoning system. Provide standardised terminology so all clinicians and systems “speak the same language”. Trace every recommendation back to its source data and reasoning path

Example: In a modern rehabilitation centre, wearable sensors capture subtle changes in a patient’s gait — reduced right stride length combined with increased lateral sway. A customized framework using AI and KG immediately connects this movement pattern to a database of prior neurological rehabilitation cases, revealing strong similarities to patients recovering from early-stage Parkinson’s disease. Drawing on this evidence, the AI generates an explainable recommendation: “Consider targeted balance training — 87% recovery rate in similar patients.” The therapist, reviewing the suggestion alongside the patient’s medical history, therapy goals, and current progress, makes the final decision on whether and how to apply the intervention. This collaborative process blends machine-driven insight with human clinical judgment. By 2030, rehabilitation centres without such AI + KG (+ontology) systems risk being left behind, as the future of rehabilitation will be transparent, explainable, and firmly therapist-led.