## 4 Results
### EEG-Based Approaches, IoT, Smart Devices, and Serious Games
In a parallel approach, [PERSON] et al. (2021) [42] developed a wearable sensor system focused on monitoring attention and stress levels in children with ASD. The methodology involved tracking physiological indicators, such as heart rate and skin conductance, which were classified using machine learning algorithms to detect changes in emotional and attentional states. The system demonstrated strong reliability, achieving an accuracy of 89% and high sensitivity in identifying fluctuations in stress and attention. These findings suggest that wearable devices equipped with machine learning algorithms can provide real-time insights into the behavioral responses of children with ASD, supporting caregivers in managing emotional and attentional shifts. The study concluded that wearable sensors not only enable accurate ASD detection but also offer immediate feedback, thereby facilitating timely interventions that are responsive to each child's needs. [PERSON] et al. (2022) [26] conducted a clinical evaluation of an AI-enhanced diagnostic device specifically designed to assist clinicians in ASD diagnosis through structured behavioral prompts and physiological data analysis. The device integrates input from three sources: a caregiver questionnaire, two short home videos of the child, and a healthcare provider questionnaire. This input is processed using a machine learning algorithm to produce an output of either ASD positive, ASD negative, or indeterminate. The system's design focused on maximizing accuracy and minimizing false negatives, ensuring safety and utility in primary care settings. The device's accuracy was rigorously assessed through clinical trials, where it demonstrated a sensitivity of 93% and specificity of 91%, effectively distinguishing ASD-related behaviors from those of neurotypical children. By benchmarking the device's performance against established diagnostic criteria, the study concluded that the AI-enhanced tool could significantly reduce the diagnostic timeline, providing clinicians with a reliable, data-driven approach to support early ASD identification. The authors emphasized the device's potential to streamline ASD assessment, ultimately aiding healthcare providers in delivering faster, more objective diagnoses. Building on AI integration in ASD diagnostics, [PERSON] et al. (2022) [35] examined the feasibility and impact of an AI-based diagnostic aid within the Extension for Community Health Outcomes (ECHO) autism model in primary care. The methodology involved integrating an AI-based diagnostic aid into the ECHO autism model for primary care evaluation of ASD in children aged 18-72 months. Similarly to the work by [PERSON] et al. (2022) [26], the diagnostic aid used three inputs: a caregiver questionnaire via a mobile app, analysis of two short home videos by trained video analysts, and a healthcare provider questionnaire. These inputs were processed by an AI algorithm to provide an ASD-positive, ASD-negative, or indeterminate result. Clinicians combined the AI outputs with their observations and clinical judgment for diagnosis. The approach aimed to reduce diagnostic delays and improve early intervention access.This tool incorporated data from caregiver questionnaires, video analysis, and healthcare provider input to generate ASD diagnostic recommendations. Methodologically, the study focused on clinical outcomes by evaluating how the tool affected diagnostic timelines and accuracy in primary care settings. Results showed that the AI diagnostic aid significantly reduced the time from initial caregiver concern to diagnosis, facilitating earlier interventions. The study concluded that implementing AI in primary care settings holds the potential to streamline the diagnostic process, thereby bridging the gap between initial suspicion of ASD and formal diagnosis, which can often be delayed due to limited resources or referral wait times. In a related effort, [PERSON] and [PERSON] (2020) [38] introduced Autism AI, a mobile application for ASD screening based on artificial intelligence.