## 5 Discussion on Challenges and Open Issues
Incorporating such approaches into research pipelines could significantly broaden the applicability of AI, IoT, and sensor-based technologies for the diagnosis of ASD, ensuring that they are both equitable and globally relevant. At the same time, the image classification techniques employed in the reviewed studies often face challenges related to uncertainties or inaccuracies in the analyzed data, which can undermine their effectiveness. This limitation highlights the need for more robust methodologies to address these inconsistencies. Recent advances in fuzzy logic-based classifiers offer a promising alternative, as they allow for a more subtle handling of image variations and uncertainties. These methods involve grouping similar images through fuzzy divergence computations that measure distances within a specific functional space. A representative feature image is then extracted for each grouping, and the classification is performed by comparing the fuzzy divergences between the input image and these representative images. Although not yet implemented in the reviewed studies, this approach has significant potential to enhance the robustness of image-based analyses. Future research could explore the integration of fuzzy logic into autism diagnostic methodologies to improve reliability and precision in image classification processes [54]. In addition, an important consideration is that the increasing reliance on sensitive data, including behavioral, physiological, and microbiome information, raises important ethical concerns around data privacy and transparency. Strong data protection measures, clear communication about how data are used, and accountability mechanisms are critical to building trust among users. Furthermore, while novel approaches such as microbiome-based diagnostics have potential, they are still in the early stages of research and require further validation in diverse populations to establish their reliability and diagnostic relevance. Integration into existing diagnostic frameworks presents another challenge, as these innovative tools must complement traditional methodologies without overburdening clinicians or families. Finally, interdisciplinary collaboration across fields such as neuroscience, biomechanics, bioinformatics, psychology, and clinical medicine is essential for the development of effective tools, but achieving alignment of research objectives and methodologies remains a persistent challenge. To address the challenges identified in this section, we propose a comprehensive and holistic framework for ASD detection (Figure 2) that takes advantage of emerging technologies and integrates them into an edge computing platform. This framework aims to overcome some of the limitations of traditional methods by combining objective data-driven insights with the expertise of clinicians. The edge platform will be designed to collect and process data from advanced sensor-based technologies, such as wearable devices, eye trackers, and EEG sensors, which provide detailed and quantifiable markers of behavioral and physiological characteristics associated with ASD. These technologies will be augmented by clinicians' evaluations, encompassing traditional observations of behavior, developmental milestones, and other diagnostic criteria, ensuring the system incorporates a well-rounded perspective. Processing data locally at the edge will enable real-time analysis, providing immediate feedback to clinicians while safeguarding patient privacy and reducing reliance on centralized cloud infrastructure. This capability is particularly crucial for environments with limited connectivity or high privacy concerns. Furthermore, by integrating explainable AI features, the system will allow clinicians to understand and interpret how the AI reaches its conclusions, fostering trust in its outputs and enabling collaborative decision-making. Specifically, the proposed framework outlines a comprehensive process for leveraging edge devices in ASD detection.