Conventionally, a technical system is defined in the design phase and considers all important requirements and aspects. The expected operations and the circumstances of operations are to be known in advance. If these are known, then the designed system can even be validated before it is produced or launched on the market. Validation is typically based on predictive analyses or simulations. However, these do not apply completely in the case of smart systems such as smart cyber-physical systems (S-CPSs) which self-manage their operation, or at least a part of it. Being able to adapt during run-time and evolve over time, S-CPSs cannot be validated using conventional deterministic approaches. Typical examples of these self-managing systems are S-CPSs already used as instrumentation in the medical field. The above circumscribed situation stimulated our background research, the results of which are concisely summarized and critically concluded in this paper. The literature has been found fairly narrow in terms of novel validation approaches for self-managing systems. The literature proposes to share the tasks of operational and behavioral validation among the system designers and the technical systems themselves. While designers need prognostic approaches to validate system operation, systems need to construct validation plans and execute them at run-time. This requires additional, validation-specific functionalities and context-dependent mechanisms such as run-time validation frameworks or meta-models, objective-sensitive self-monitoring mechanisms, self-constraining and self-supporting mechanisms, and other enablers. Extensive foundational research and system prototype testing are deemed to be indispensable. To make the first small step in this direction, this paper proposes a concept for the validation of smart medical CPSs. This relies on the following hypothesis: If a system has the freedom for self-adaptation, then it should also be equipped with a self-control mechanism, meta-knowledge, and a supervisory controller. These additional resources enable purpose- and context-dependent semantic reasoning about the operational objectives and behavioral states. This paper suggests a number of topics for future research towards a run-time validation engine.