## 5 Discussion on Challenges and Open Issues
Data collection occurs externally through specialized devices, including wearables (e.g., smartwatches, EEG headsets), eye trackers, and neuroimaging systems such as MRI and fMRI, as well as behavioral data recorded via cameras, microphones, or clinical tools. These diverse data sources are then transmitted to the edge device for further processing, with optional initial filtering or normalization at the source. On the edge device, preprocessing steps such as noise filtering, normalization, and segmentation ensure compatibility with AI models. For example, EEG data from a wearable headset can be transmitted wirelessly to the edge device, where initial filtering at the source removes artifacts like muscle movement noise. Similarly, eye-tracking data, including gaze coordinates, can be transmitted after basic normalization at the device level to account for head position. On the edge device, further preprocessing steps, such as noise filtering (e.g., removing low-frequency interference from EEG signals), normalization of data ranges,and segmentation into time windows ensure the data are clean and compatible with AI models for analysis. The device integrates multimodal data streams (sensor, neuroimaging, and behavioral), applying weighting mechanisms to prioritize critical data types like neuroimaging or clinician evaluations. AI inference is performed using pre-trained models, with lightweight algorithms like random forests analyzing sensor and behavioral data, while quantized CNNs process neuroimaging data facilitates hybrid analysis for comprehensive decision-making. Real-time processing capabilities enable low-latency feedback to clinicians, highlighting critical markers and anomalies for immediate action. Explainable AI outputs provide interpretable insights, such as visualizations or heatmaps, ensuring that the results are actionable for clinicians. Privacy and security are prioritized by localizing data on the edge device, employing encryption during transmission, and restricting access to authorized personnel. Clinicians interact with AI outputs directly on the edge device, incorporating their expertise into the diagnostic process, resulting in comprehensive diagnostic reports that combine objective insights with subjective evaluations. Clinicians use ADOS to perform structured and standardized behavioral assessments, providing direct insights into social, communicative, and repetitive behaviors. They also rely on ADI-R to collect in-depth developmental history and caregiver-reported data on behavior patterns. By combining these traditional tools with data from innovative technologies such as wearable sensors and AI-driven analysis, clinicians can validate, contextualize, and enhance the reliability of diagnostic findings, forming a comprehensive and precise diagnostic input. For instance, ADOS assessments often involve structured tasks that measure a child's ability to initiate eye contact or respond to social cues during play. Innovative technologies like eye-tracking systems can complement this process by quantifying gaze patterns in real time, offering objective metrics on how the child focuses on social versus non-social stimuli. Similarly, ADI-R relies on caregiver input to identify behaviors like repetitive movements or unusual speech patterns. This information can be further validated using wearable sensors that measure movement patterns or vocal analysis tools that detect atypical speech prosody. By integrating these insights, clinicians can combine their subjective observations and caregiver-reported data with precise, real-time metrics, ensuring a more subtle and accurate diagnosis. Feedback loops allow for iterative improvement of AI models and workflows, with anonymized outcomes enabling system-wide updates where permitted. This integrated approach ensures efficient, secure, and actionable diagnostics while preserving patient privacy and supporting real-time decision-making.