## 6 Discussion
The ML- and DL-based approaches mentioned in this document can be used to diagnose ASD and to develop tools or apps to assist healthcare professionals, clinicians, and caregivers in the early detection of ASD. Although incorporating AI in identifying and categorizing ASD has revealed significant potential, numerous limitations remain in the existing research that must be rectified to improve the effectiveness and usability of AI models for ASD detection. Furthermore, the most significant limitations found in the reviewed literature include the lack of data augmentation, small sample size of datasets, imbalance data, sample homogeneity, and generalizability concerns, which limit the performance of the models. For example, we found that, in many cases, data augmentation approaches are necessary for enhancing the model's performance. For example, [PERSON] et al. [153] determined that various augmentation approaches can directly enhance classification tasks. Moreover, [PERSON] et al. [154] recommended that GANs can be used to produce synthetic data to increase the size of datasets that represent the characteristics of ASD datasets to prevent overfitting. Limited sample sizes in many studies on ASD detection and classification might result in overfitting and poor model generalizability. Models with small datasets frequently exhibit good performance on training data but poor classification accuracy on unseen data. In addition, researchers should collect larger, more varied datasets that cover a wider range of demographics, geographic areas, and clinical symptoms of ASD. Furthermore, they must collaborate with research institutions and centers to enhance data exchange and aggregation. This can enable more rigorous analysis and improve the generalizability of results. Similarly, in ASD detection, various modalities exist, including EEG, eye gaze tracking, facial images, questionnaires, genetics, biomarkers, and MRI images. Researchers have not considered using multi-modalities and used any single modality for ASD classification. This unexplored region can be a vital research breakthrough as authors can use a combination of MRI and facial images or EEG, which can be paired with biomarkers for detailed and accurate detection of ASD. Genetics is a modality researchers have not used and explored, but it can also be beneficial for early detection. Various authors have worked and presented different ML and DL modalities to detect and classify ASD. After conducting this research, we identified and derived the limitations and research gaps within these domains based on ML and DL. For instance, researchers used questionnaire data to detect ASD using ML techniques. An ASD test app and various other ASD questionnaires have been designed and developed by experts, and they consist of multiple parameters that can assist in detecting ASD. Patient's parents or caregivers filled out the questionnaire data and processed it into structured datasets. AI-based models are trained and evaluated for ASD classification using these datasets [22]. Furthermore, this modality has some limitations; there are few available datasets for researchers, while the existing datasets consist of similar features and samples belonging to a specific human race. However, these datasets can have potential human biases as filled out by parents or caregivers manually. It could affect the overall validity and accuracy of these datasets. In contrast, these datasets do not include attributes of medical tests by professionals that contain factual details related to ASD detection [155]. It is better to add more clinical assessments and medical examination attributes to the dataset to enhance or improve the accuracy of predictions [67]. Furthermore, recent research shows several facial features linked to ASD, such as a wide upper mouth, wide-set eyes, and shorter middle regions such as cheeks and nose [56]. For early detection of ASD, they used the facial images dataset sourced from Kaggle. The dataset was collected from different platforms such as Facebook and Google.