## 5 Discussion
Throughout the semi-clinical validation phase, other components that are currently under development--like automatic regulatory notifications and real-time compliance monitoring--will be incorporated. Remodeling role-based dashboards and making user consent reminders clearer are just two examples of how insights from audit log assessments and consent flow simulations are currently impacting platform design improvements. Setting up regulatory applications, coordinating internal processes with MDR and AI Act compliance protocols, and being ready for the integration of dynamic consent and compliance milestones in practical deployments are all examples of ongoing accomplishments. In line with the recent European Commission regulations for reliable AI, projected improvements also include comprehensive checks for data authenticity and algorithmic transparency. It is particularly important to underscore that all the findings presented in this research are a result of preclinical, laboratory-controlled testing, deploying simulated user situations to distinguish between validated outcomes and intended improvements. The platform has not yet collected or processed clinical data from actual patient populations. As a result, while some features, like versioned preprocessing, multimodal data acquisition, and fall detection inference, have been successfully tested and deployed, additional characteristics, like responsive training, real-time compliance warnings, and dynamic deployment dashboards, are still being developed or evaluated. To match objectives with the progressive testing and validation plan and to preserve clarity on the system's present technological maturity, this difference is required. Despite its demonstrated technical maturity, the NeuroPredict platform has some operational _limitations_ that require more development before clinical implementation. Generalizability to heterogeneous patient populations and real-world clinical variability remains unconfirmed, as the platform has not yet been tested across diverse clinical infrastructures or multisite deployments. Although the platform includes digital informed consent workflows and secure data handling protocols, its effectiveness in dynamic, real-world environments--particularly across varying levels of digital literacy--has not yet been validated. The ability to maintain usability and compliance across user types remains a challenge. One relevant limitation concerns the absence of end-user involvement during the initial design phase. This was a consequence of the project funding model, which restricted external partnerships. Nevertheless, the NeuroPredict platform was architected to remain adaptable, enabling easy integration of user feedback and participatory co-design approaches in future semi-clinical and clinical trials. The platform was iteratively evaluated during development through internal testing by team members and technical staff within the institute. Structured questionnaires and scenario-based simulations were used to assess usability, data flow, and interface responsiveness. While not a full co-design process, this internal validation provided early feedback and guided functional refinement. The platform's modular design supports future co-design sessions with clinicians and patients, planned for the upcoming semi-clinical validation phase to ensure alignment with real-world user needs. The NeuroPredict platform currently lacks real-time feedback and closed-loop decision-making capabilities. This reduces its ability to provide quick, adaptive interventions based on live data. Similarly, user contact is essentially one-way, with no mechanisms for patients or physicians to actively provide feedback or adjust monitoring settings during deployment. While the fall detection algorithms have achieved high lab accuracy, clinical calibration using gold-standard datasets remains pending. The same applies to broader anomaly detection and trend analysis functions, which require further refinement to ensure clinical interpretability.