## 5 Discussion
To address these limitations, the platform development plan includes co-design sessions with medical professionals and patient representatives, the incorporation of real-time user input modules, and the deployment of fairness monitoring pipelines. Semi-clinical trial preparations will also handle unpredictability in patient behavior and data settings, thereby enabling model retraining and adaptive deployment techniques. Interoperability with current healthcare information solutions, including electronic health records platforms, is an additional unresolved issue. This is important for data integrity and for a simple integration into real-world healthcare workflows. Hardware limitations, such as battery life, device durability in daily use, and usability by elderly patients, remain to be systematically tested. In terms of hardware resilience and simplicity of use, initial pilot tests are planned to systematically evaluate battery longevity, device durability, and ergonomic usability for elderly patients and those with motor deficits. These tests will include live usage simulation and extended monitoring durations to measure device performance across living conditions. Feedback from these tests will inform hardware improvements to optimize energy efficiency and user comfort, enabling long-term use and consistent data acquisition in semi-clinical and remote settings. Hardware resilience and accessibility continue to be a core priority in the development roadmap of the platform. From a regulatory perspective, formal certification procedures are still ongoing even though the architecture complies with GDPR, MDR, and the EU AI Act. Although audit and documentation procedures have been designed with future compliance in mind, they must be continuously adjusted as legal frameworks change. The NeuroPredict platform is getting ready to be used in actual clinical settings as development moves forward, with an emphasis on putting in place dynamic consent procedures, ongoing ethical supervision, and iterative improvement based on feedback from stakeholders. The _future work_ will focus on advancing the platform beyond laboratory settings and into structured semi-clinical trials. This will involve testing with actual patients from the three target groups--PD, AD, and MD--under real-world use scenarios. Efforts will also be made to:  
* Extend AI modules with federated learning capabilities to allow personalized, decentralized model training on different user profiles;
* Integrate clinician feedback loops and develop interactive visualization modules to support explainability and medical decision support;
* Expand the cognitive monitoring module with more detailed task metrics and neuropsychological correlates;
* Assess long-term device performance, usability, and user acceptance among elderly users and caregivers, with ethnographic and usability testing protocols;
* Operationalizing ethical safeguards and regulatory alignment in real-world settings. This includes validating informed consent flows, data access controls, and explainable AI interfaces through participatory testing with patients, caregivers, and clinicians. A complete DPIA and MDR conformity assessment are planned, alongside alignment with the AI Act through lifecycle monitoring, algorithm traceability, and the implementation of an AI-specific quality management system;
* Begin the technical preparation for regulatory submissions under MDR and future EU AI Act compliance frameworks, including software lifecycle documentation, performance validation protocols, and risk assessments;
* Guarantee an effortless integration into healthcare workflows, future developments will also address compatibility with clinical data infrastructures and electronic health records. Future research should therefore focus on clinical pilot studies in diverse care settings, participatory co-design processes with end users, and empirical assessments of ethical and regulatory features in practice.