## 1 Introduction
The platform is also well-positioned to support geriatric care, where intelligent monitoring tools are becoming increasingly significant for managing long-term, chronic neurodegenerative conditions, by enabling continuous and personalized tracking [6]. AI systems in healthcare must provide explainable outputs and be compatible with clinical workflows in addition to having predictive performance. Adoption in delicate areas like neurodegenerative care requires that clinicians comprehend and have faith in automated recommendations, which is made possible by transparent and interpretable AI models. To improve transparency and clinical relevance, ongoing research focuses on hybrid architectures that combine deep learning with rule-based logic or attention mechanisms [7]. Moreover, ensuring fairness in AI-based neuro-monitoring requires robust validation across diverse populations to prevent algorithmic bias and health disparities [8]. The development of AI-powered healthcare platforms must align with established legal and ethical frameworks to ensure safety, trust, and clinical applicability. This includes compliance with the GDPR, the MDR, and the emerging European AI Act. These requirements are particularly important in neurodegenerative care, where sensitive data, clinical oversight, and algorithmic transparency are critical. The NeuroPredict platform is an AI-driven digital health system aimed at facilitating timely identification and ongoing monitoring of neurodegenerative diseases, namely PD, AD, and MS. It seeks to overcome existing constraints in managing diseases by supporting extensive, longitudinal gathering and analysis of data by using embedded sensing technology. The NeuroPredict platform is designed with a firm dedication to ethical principles and regulatory compliance, guaranteeing data protection, transparency, and conformity to advancing healthcare standards across its lifecycle. The multifaceted nature and variety of neurodegenerative disorders represent a considerable challenge for precise and accurate monitoring. The NeuroPredict platform addresses this need by integrating multimodal data collection with advanced AI algorithms, enabling more reliable and individualized tracking of disease progression. Due to project constraints, the NeuroPredict platform's initial design phase relied on internal expertise, literature-based requirements, and iterative lab testing rather than direct co-design with target user groups. However, the system has been intentionally developed with a scalable, flexible architecture that will facilitate full participatory co-design in future clinical validation phases. While a variety of AI-enabled digital health platforms have emerged for neurodegenerative conditions, many of them address only partial domains or lack robust compliance-by-design and modular architectures. The NeuroPredict platform aims to fill this gap by integrating multimodal sensing, scalable machine learning pipelines, and advanced privacy and regulatory safeguards in a unified system. This work should be interpreted as an experimental technology assessment focused on validating a modular architecture that integrates multimodal data acquisition, initial machine learning pipelines, and secure data visualization tools. The broader platform development will continue through subsequent participatory design and clinical validation phases, including post-project time.