In the context of cybernetic systems and the future of human-AI collaboration, intelligent technologies are increasingly conceived as living machines: systems that regulate themselves through feedback while co-evolving with humans. Unlike traditional automation, these systems do not merely optimize fixed goals—they must continuously adapt in response to user behavior while preserving human agency, intent, and oversight. This paper presents the design and development of a Clinical Decision Support System (CDSS) for stroke neurorehabilitation that operationalizes these principles. While no experimental trials are reported in this manuscript, the system architecture and implementation are described in detail, and validation is planned through a multicenter randomized controlled trial (RCT). By embedding a feedback-driven, clinician-guided control loop into therapy delivery. Integrated within the Rehabilitation Gaming System (RGS), a virtual reality platform for adaptive motor and cognitive training, the CDSS maintains a dynamic model of each patient’s state, derived from behavioral performance, affective self-reports, and adherence patterns. This model drives the adaptive selection of therapeutic activities, forming structured, modifiable care plans. The system enables clinicians to inspect, adjust and refine interventions through a transparent interface, ensuring that algorithmic adaptation remains aligned with expert judgment and patient-specific needs. The CDSS exemplifies a shift from automation to co-regulation by supporting mutual adaptation between human and machine. It represents a concrete step toward the design of cybernetic healthcare systems, those capable of sustaining meaningful, personalized interactions over time, in service of complex and evolving recovery goals.