Virginia Sandulescu

The present Special Issue, “Artificial Intelligence-Enabled Smart Healthcare”, was conceived as a scholarly forum dedicated to examining how artificial intelligence is reshaping contemporary healthcare systems through intelligent diagnostics, predictive analytics, remote and physiological monitoring, edge intelligence, telemedicine-enabled care, digital therapeutics, and human-centered clinical decision support

AI-driven smart healthcare platforms increasingly combine multimodal data acquisition, remote monitoring, predictive analytics, longitudinal dashboards, and role-specific information services. While these capabilities enable more continuous and personalized health monitoring, they also introduce accessibility challenges that extend beyond conventional user-interface design, particularly in how monitoring results and AI-generated outputs are structured, contextualized, and communicated. This paper addresses the gap in operationalizing accessibility at the system level by proposing a standards-mapped Accessibility-by-Design framework for AI-enabled smart healthcare platforms. Requirements derived from international accessibility, human-centred design, and software quality standards are translated into traceable architectural and component-level constraints and integrated into the development workflow. The methodology combines standards-to-requirement-to-component traceability mapping, architectural documentation analysis, component inspection, and controlled workflow walkthroughs. The framework is applied to the NeuroPredict platform, an AI-based environment for longitudinal and multimodal monitoring of neurodegenerative disorders. Accessibility is operationalized through cross-layer architectural mechanisms, including semantic interface components, predictable interaction workflows, accessible presentation mechanisms, and structured representation of monitoring and AI outputs. AI-generated results are presented in a structured form together with contextual, temporal, source-related, and explanatory information to support role adaptation; uncertainty-related fields are reserved for future validated user-facing integration. Implementation evidence from selected platform workflows demonstrates that the proposed mechanisms can be incorporated into the current platform architecture and provides preliminary support for their technical feasibility. The evidence remains limited to implemented and inspected mechanisms, without user-based accessibility validation, formal conformance auditing, clinical deployment, or regulatory certification. These results illustrate that, while maintaining the distinction between accessibility mechanisms and the underlying analytical computation, accessibility-by-design can offer an engineering foundation for integrating accessibility requirements into the architecture and development lifecycle of AI-driven smart healthcare platforms.

Background: Parkinson’s disease (PD) diagnosis benefits significantly from advancements in artificial intelligence (AI) and image processing techniques. This paper explores various approaches for processing hand-drawn Archimedean spirals in order to detect signs of PD. Methods: The best approach is selected to be integrated in a neurodegenerative disease management platform called NeuroPredict. The most innovative aspects of the presented approaches are related to the employed feature extraction techniques that convert hand-drawn spirals into a frequency spectra, so that frequency features may be extracted and utilized as inputs for various classification algorithms. A second category of extracted features contains information related to the thickness and pressure of drawings. Results: The selected approach achieves an overall accuracy of 95.24% and allows acquiring new test data using only a pencil and paper, without requiring a specialized device like a graphic tablet or a digital pen. Conclusions: This study underscores the clinical relevance of AI in enhancing diagnostic precision for neurodegenerative diseases.

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases.