## 6 Discussion
Researchers have concentrated on detecting ASD using eye tracking and examining eye movement's biological and behavioral patterns based on ML algorithms, particularly in children with several developmental problems, including ASD [109]. The eye-tracking tool is a biomarker for evaluating children with ASD and of fers few advantages. Firstly, it facilitates eye tracking for young children, enabling early identification of ASD concerns. Secondly, eye tracking data furnishes information that can be utilized as biomarkers to signify unusual visual attention. Thirdly, eye-tracking technology is a straightforward metric associated with the diagnostic instruments employed for ASD screening [112; 115]. Unfortunately, there are some limitations to this modality. There are few publicly available eye-tracking and imbalance datasets. However, they are not appropriate for accurate ASD screening since they aggregate several eye-tracking scan path photos from a small number of participants rather than a single trial from a large sample. Additionally, the accuracy of the suggested ASD screening method depends on the quality and consistency of the training data. Therefore, the clinician needs to make sure the child is looking at the eye tracker during the trials and that there are no distractions in the recording environment that could cause the child to lose focus [109; 115]. Genetics significantly contributes to the early identification of ASD by finding particular variations linked to ASD. Advancements in genomic technology, including whole exome sequencing (WES) and genome-wide association studies (GWAS), have enabled the identification of many genetic variants associated with ASD, but these are time-consuming and expensive. Nonetheless, computational tools offer rapid, more dependable, cost-effective alternatives for predicting or prioritizing potential disease genes. Computational methods integrate many data sources and gene functional information related to connected disorders to predict disease genes using machine learning techniques [130]. However, there are a few challenges regarding genetic data, such as data quality interpretability, and most genomic data exhibit a significantly smaller sample size relative to their gene characteristics [133; 134]. AI is developing efficiently and has various possible uses in multiple domains. However, several issues in ASD detection must be resolved if AI systems are to be implemented safely and efficiently. Some major difficulties are ensuring data size, quality, complexity, interpretability, and privacy and data protection. To fully realize AI's promise across a range of industries, these challenges must be overcome. They are described below. **Data quality. ** AI models' accuracy and prediction ability are greatly influenced by the data quality used to train them. Ensuring good data quality through thoughtful gathering, preprocessing, cleaning, feature extraction, and validation is crucial for ASD research because identifying nuanced patterns and behaviors is crucial [56; 124]. **Data availability. ** Large datasets are necessary for AI systems, especially DL models, to learn efficiently and generalize correctly. However, limited and diverse data are a common problem in ASD research, which might make AI models underperform [156]. **Interpretability. ** Understanding the fundamental mechanisms of DL algorithms can be difficult due to their proficiency in mapping complex, nonlinear functions. The interpretability of results is essential in the healthcare sector, as understanding the factors that impact consequences is as vital as generating accurate predictions. Interpretability enhances confidence and assists the prompt integration of these technologies into clinical procedures, improving decision-making and allowing medical practitioners to make educated choices based on algorithmic insights [156]. **Ethical consideration. ** Strict adherence to legal and ethical obligations is crucial for managing medical data, especially for ASD.