Dealing with Autism Spectrum Disorders: Journey from Traditional Methods to Artificial Intelligence

Abstract

Background

World Health Organisation (WHO) in 2024 identified that approximately one in 100 children globally has autism spectrum disorder (ASD). ASD is a collection of neurodevelopmental disorders that impact a person’s ability to socially interact and communicate, which can typically be noticed in early childhood. While ‘autism’ as a term was initially used for schizophrenic patients, later psychiatrists Dr. Kanner and paediatrician Dr. Asperger introduced it as a syndrome in children with behavioural differences in social interaction and communication with restrictive and repetitive interests. In today’s time, the umbrella term ‘ASDs’ is used to describe a clinically heterogeneous group of neurodevelopmental disorders (NDDs).

Purpose

To examine the role of traditional approaches and the potential effectiveness of artificial intelligence (AI) methods in dealing with ASDs for improving the accuracy in its diagnosis and treatment.

Methodology

The study adopts a narrative review approach to understand the application of AI in ASD. For this purpose, around a hundred research articles were selected from the years 2010–2024. Inclusion and exclusion criteria were identified. The review is organised and grounded on the medical treatment, occupational remedy, vocational remedy, psychology, family remedy and recuperation engineering.

Results and Conclusion

The results show the undisputed role of AI and its ability to identify early indicators of autism, in accordance with the UN Sustainable Development Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice and Strong Institutions). Further, healthcare sectors which are using a variety of AI analyses on data sources, genetics, neuroimaging, behavioural patterns and electronic medical records are able to early detect for individualised evaluation of ASD. The significance of timely interventions with the help of machine learning (ML) algorithms demonstrates high accuracy in differentiating ASD from neurotypical development and other developmental disorders.

AI-driven therapeutic interventions expand social interactions and communication skills in people with ASD in the form of virtual reality-based training, augmentative communication systems and robot-assisted therapies. Thus, the future of AI in ASD holds promise for improving diagnostic accuracy, implementing telehealth platforms and customising treatment plans, despite obstacles such as data privacy and interpretability.

Introduction

As it is rightly stated, ‘The world needs all kinds of minds. The same applies to the one with autism spectrum disorder (ASD), which is a neurodevelopmental disorder (NDD) characterised by deficits in social communication and the presence of restricted interests and repetitive behaviours. Over time, there have been significant changes in the diagnostic standards and therapeutic modalities for ASDs. ¹

ASD is a complex NDD defined by continued difficulties in social communication, social interactions and the presence of limited and repetitive behaviours, movements, interests or activities. Like many other disorders, it manifests in a spectrum. The severity and symptoms vary between individuals.

The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Text Revision (DSM-5-TR) provides uniform criteria for diagnosing ASD. It is divided into five criteria and severity levels. Usually marked by deficits in social communication, interaction, repetitive patterns of behaviour are frequently visible during early childhood. The severity level is from requiring the least support to requiring extensive support in daily life from professionals.

Key Domains of Autism Spectrum Disorder

Social Interaction

Individuals with ASD frequently display significant difficulties in social relations. These difficulties may include problems in understanding the passions and intentions of others, maintaining eye contact, facial expressions and conforming to social norms. Individuals with ASD may witness challenges in sharing interests, establishing and maintaining comfort which is required for the two-way nature of social relations.2

Communication

The ability to communicate by using language depends on intellectual and social development. People with ASD may face difficulties such as using speech or language, understanding the meaning and rhythm of words and sentences. They also have issues in understanding body language and the meanings of different vocal tones. Overall, these difficulties affect the ability to interact with others, especially people in their own age group. ³

Repetitive Behaviours

People with ASD frequently show limited, monotonous patterns of behaviour. This may involve an obsession with particular subjects or exercises, repetitive body movements (e.g., shaking and clapping) and a dependency on day-by-day schedules. These tedious behaviours are some of the times seen as a way of self-soothing or as an endeavour to control an environment. ⁴

Sensory Sensitivity

Many individuals with ASD have abnormalities in sensory processing and can have very strong or delayed reactions to sound, light, touch, taste and odour. For example, people with ASD may perceive a background which may or may not exist.

The Evolutionary Narrative

The history of research and examinations concerning ASDs stems from the mid-1940s. A US psychiatrist, Leo Kanner, was the first to elaborate on it. He detailed a unique set of behaviours, which he defined as ‘Self-isolation extreme’. Around the same time, Austrian psychologist Hans Asperger described a similar but higher form of social withdrawal known later to be Asperger’s syndrome. These distinct yet intertwining studies served as the cornerstone for recognising and understanding ASDs. During the period after the 1940s, ASD was seen very sparsely. ⁵ As with many other psychological conditions, ASD was frequently erroneously diagnosed as schizophrenia. Due to an insufficient grasp of the problem, prescribed criteria became vague, which meant that interventions mostly focused on behaviour modification as well as psychotherapy without any depth to address the complication. From the mid-1990s, an increase in awareness towards the genetic components contributed to an increase in the rate of ASD diagnoses. This further enhanced the understanding of the condition, which led to refining and broadening the parameters set for serving the intended individuals, leading to a more holistic comprehension of the intricate cases. During this period, there has also been a greater understanding of the need for early diagnosis and intervention for ASD, which has remarkably improved prognosis. ⁶

Although the majority of ASD cases are believed to be caused by polygenic interactions, some cases are directly linked to variations in a single-gene; these are known as monogenic cases. However, these make up a relatively small percentage of all ASD cases. Monogenic cases offer an important window into understanding the genetic basis of ASD. Several specific genetic syndromes, including tuberous sclerosis (TSC), fragile X syndrome, 15q11-q13 duplication syndrome and Rett syndrome, have been found to be associated with a higher risk of ASD. These conditions are commonly caused by mutations or abnormalities.

Significant variations in brain development and function can result from a single-gene, raising the likelihood of an ASD phenotype. Fragile X syndrome is one of the most common forms of inherited intellectual disability and the single-gene disorder known to be most strongly associated with ASD. Mutations in the TSC1 or TSC2 genes cause TSC, an inherited disorder that affects multiple systems. Patients with TSC are more likely to have ASD. A region of chromosome 15 is involved in 15q11-q13 duplication syndrome (Dupuy 15q syndrome), whose duplication is linked to an increased risk of ASD. Rett syndrome, which primarily affects women, is brought on by changes in the methyl-CpG binding protein (MECP2) gene, and individuals frequently display some of the characteristics of ASD, such as a lack of social skills. Rett syndrome is a serious neurological disorder. It impacts every movement of the body and is nearly exclusively observed in women. Speech issues (such as difficulty learning to speak or loss of speech), walking difficulties or loss of walking ability and loss of intentional hand use.

Causal Picture

Genetic Aspect to ASD

In addition to being essential for comprehending the genetic underpinnings of ASD, the identification of these monogenic cases may also be helpful in the creation of therapeutic and intervention-based approaches that target particular genetic variations. The expression of the genetic variants did, however, exhibit some heterogeneity even in these cases, indicating that other genetic and environmental factors may have an impact on the variety of phenotypic traits and clinical manifestations. Consequently, a thorough investigation of these disorders will lead to a better understanding of the genetic foundation of ASD to create a more individualised treatment plan of action. ⁷

Interactions Between Multiple Genes

It is commonly acknowledged that the relationship of genetic and environmental factors leads to the development of ASD, with polygenic interactions playing a key role in the disease’s genetic foundation. Variants or polymorphisms in several genes that increase the threat of ASD are known as polygenic relations, on the negative to monogenic cases. According to current research, ASD cannot be explained by a single-gene. Rather, hundreds of genes that have been discovered are linked to a higher risk of ASD. Since these genes are frequently implicated in important processes such as brain development, neuronal signalling and intercellular communication, it is possible that ASD entails a complex regulation of brain structure and function. Due to the intricacy of multigene interactions, genetic research on ASD necessitates extensive genomic data and advanced statistical techniques to identify the genomic variants that raise risk. ⁸

Several consistently replicated ASD risk gene loci, including those in the chromosomal regions 3p21, 5p14, 7q35 and 20p12, have been found through meta-analyses of large-sample genome-wide association studies. These genes, which include contactin 4 (CNTN4), contactin-associated protein-like 2 (CNTNAP2) and neurexin 1 (NRXN1), are essential for neurodevelopment and synaptic function, especially for synaptic adhesion and neurotransmission.

Environmental Aspects of ASD

Vulnerability of Mothers

Pregnancy-related exposure is the term used to describe a mother’s exposure to particular environmental elements or substances during foetal development, which may raise the child’s future risk of developing ASD. Prescription of drugs (such as opioids and anti-seizure drugs), environmental contaminants (such as heavy metals and air pollutants), infections (such as influenza and rubella viruses) and inadequate nutrition or dietary deficiencies (such as folic acid) are some examples of these exposures. These elements could make the risk higher. ⁹

Effect of Developmental Abnormalities

Regarding early developmental stages of ASD, it has been found that the brain grows quickly and forms neural networks during a child’s early development. ASD risk may rise as a result of any interference with the normal development of brain structure and function during this crucial time. Early indicators of ASD could include, for instance, delayed language development, abnormal sensory processing or a very early lack of social interaction. The brain’s nervous system struggles to process information, form connections and adjust to changes in the environment, which is reflected in these developmental abnormalities. Thus, early detection and intervention are crucial to the overall development.

Interactions Between the Environment and Genes

The complex interplay between genetic background and external environmental factors increases the risk of ASD. In particular, environmental triggers may activate specific genetic susceptibilities, resulting in the development of ASD. It can manifest by affecting important brain developmental stages, genetic variations or may increase an individual’s susceptibility to specific environmental exposures (such as substance use during pregnancy, environmental pollutants or maternal nutritional status). This necessitates the comprehension of multifactorial etiological models of ASD and the significance of creating individualised intervention strategies. ¹⁰

Methodology

This study aims to explore and gain a comprehensive understanding of ASD. Also, the role of artificial intelligence (AI) methods in dealing with ASD for improving the rate of accuracy in its analysis and treatment.

The inclusion criteria were that research articles on AI and mental health or AI and its role as an intervention, from peer-reviewed journals published in PubMed/ Medical Literature Analysis and Retrieval System Online (MEDLINE), Embase, Cochrane Central, Google Scholar and MedRxiv were taken.

The exclusion criteria were that studies conducted prior to 2010, and articles not written in English or full articles unavailable online were not considered for the analysis.

Results and Discussion

The aim of this narrative study was to understand the basis of ASD and ways to deal with it through AI diagnostic methods. With the growing application of AI in various sectors such as healthcare, education and business, interventions and therapies are also seen to benefit from it. Therefore, the need of the hour is to integrate AI diagnostic tools into clinical practice to ensure continuous workflow and better decision-making.

Numerous studies have demonstrated encouraging outcomes when AI is used to diagnose ASD, with potential gains in precision, effectiveness and early detection. AI’s ability to process complex, multifaceted data related to ASD, such as visual, motor function, behavioural patterns, microbiome, genetic and neuroimaging data, is demonstrated by this expanding body of research. The use of AI in ASD diagnostics offers the possibility of more objective evaluations as well as the ability to spot minute patterns that conventional diagnostic methods might miss.11

By using AI with technologies such as eye shadowing, facial recognition and neuroimaging, experimenters are creating a precise way to handle humans. Further categorising ASD and forecasting its severity and progression over time, these tools seek to provide a more comprehensive understanding of each child’s requirements. Many studies have looked into how to use technologies such as machine learning (ML), deep learning (DL), eye-tracking and facial recognition to improve the classification and early detection of ASD. A summary of important research aimed at strengthening the diagnostic procedure and boosting prediction results can be found below.

Khullar et al.12 presented a portable AI-powered tool that diagnoses ASD using ML algorithms, such as long short-term memory (LSTM). Using exemplifications, the study discovered that the LSTM model was 100% accurate in diagnosing ASD. The system did remarkably well in two distinct testing situations: Evaluating children with ASD and children without ASD. They were trained on a set of data. The device has the potential to improve early detection. There are many advantages to this tool, including the ability to assess the severity of ASD, portability for use in different contexts and prompt diagnosis, all of which help children receive better long-term results and earlier intervention. ⁸

To identify ASD, electroretinography (ERG) was analysed using ML. Spectral analysis of ERG waveforms provided higher classification accuracy than conventional time-domain features, according to the study, which examined ERG signals from children with ASD and control subjects. The researchers demonstrated the potential of ERG as a tool for diagnosing ASD by achieving an accuracy of 86% and a sensitivity of 98% using an ML approach with automatic feature selection. Compared to conventional techniques, this approach provides a quicker and easier diagnostic procedure. Also, the study indicated that the individual delicacy could be further enhanced by combining ERG with other physiological measures such as pupil response or electrodermal exertion. The goal of the study was to increase the detection accuracy of ASD by using data from various tasks and datasets. Although issues such as data imbalance are still in the process of resolution, this method outperformed conventional single-task approaches and showed how ML can improve clinical practice for ASD diagnosis. To guarantee that AI-driven diagnostic tools are dependable and useful in clinical settings, more research into these technologies will be essential to overcoming present obstacles such as algorithm bias and data quality. ³

These studies in total demonstrate the variety of methods and noteworthy advancements in improving predictive outcomes for diagnosing ASD with AI, ML and other cutting-edge technologies. For children to receive early interventions that can greatly improve their quality of life and developmental prospects, improved predictive outcomes are essential.

Further diagnostic tools for assessment in ASD were identified under two categories, that is conventional techniques and contemporary diagnostic techniques. Conventional techniques such as comprehensive evaluations of behaviour and developmental history are key components of traditional ASD diagnostic techniques. Specialised medical professionals such as paediatricians, neuropsychologists or psychiatrists typically perform these evaluations.13 To know more about the child’s social interactions, communication abilities and behavioural patterns, the diagnostic process includes both in-depth interviews with parents or other caregivers and direct observation of the child. The Autism Diagnostic Observation Scale, the Autism Diagnostic Interview-Revised and the Childhood Autism Rating Scale are a few examples of diagnostic instruments. ¹⁴

These instruments are intended to detect the primary signs of ASD, including repetitive interests or behaviours and difficulties with social communication. Furthermore, the physician might carry out a number of cognitive or developmental tests to rule out additional conditions, such as language disorders or other NDDs, that could account for the child’s behaviour. Even though these conventional diagnostic techniques are very good at identifying ASD, they may be somewhat inconsistent because they depend on professional judgement and subjective evaluations.

On the other hand, contemporary diagnostic techniques first include genetic examination whereby investigation of inheritable variations in a person’s DNA. Inheritable testing for ASD can identify pitfalls related to the complaint. Variants in particular genes have been found to significantly affect the risk of ASD, despite the fact that the genetic background of ASD is incredibly complex, involving multiple genes and the interaction of genes with environmental factors. For instance, Phelan-McDermid syndrome is linked to variations in the SH3 and multiple ankyrin repeat domains protein (SHANK3) gene and individuals with this syndrome frequently display characteristics of ASD. Fragile X syndrome, the most prevalent single-gene cause of ASD known to science, is caused by variations in the FMR1 gene, which is responsible for the connection between fragile X syndrome and ASD. Rett syndrome has been linked to mutations in the MECP2 gene, and individuals with Rett syndrome frequently have ASD. Furthermore, it has been discovered that variations in the NRXN1 and NLGN3/4 genes raise the risk of ASD. ¹⁵

The second technique is neuroimaging, which offers a non-invasive means of examining alterations in brain structure and function. These methods include positron emission tomography, diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI). Structural and functional differences in particular brain networks and regions in people with ASD can be identified with neuroimaging techniques.16 To better understand the social, linguistic and cognitive deficits in people with ASD, fMRI is used to map patterns of brain activity during particular tasks. The connections between bundles of nerve fibres are revealed by DTI, which focuses on the white matter’s microstructure. ¹⁷

The third technique is Inquarter Screening for ASD, designed to ameliorate the delicacy and convenience of webbing. Fourth and the latest method used and found to be effective, is AI and ML techniques to analyse children’s behavioural videos and biomarkers. By training algorithms to identify specific behavioural patterns and physical signals associated with ASD, these technologies can help doctors and researchers identify potential ASD symptoms. ¹⁸

Artificial Intelligence (AI)

Since the 1950s, when AI emerged as a field of study. AI refers to the ability of computer systems to accomplish tasks that typically require human intelligence. These machines are able to mimic human behaviour or write computer programmes. This process is ‘that of making a machine behave in ways that would be called intelligent if a human were so behaving,’ according to John McCarthy, the discipline’s founding father. He claimed that creating machines that act as if they are intelligent was the aim of AI. Due to varying definitions of intelligence, AI has developed with a more flexible definition than computer science. It was designed as a computer system that shares many characteristics with the human mind. ¹⁹

The most widely used approach to treating a variety of psychological disorders at the moment is screening for cognitive deficits or impairments and early intervention. Comprehensive evaluations are used to make the diagnosis, which may also aid in the comprehension of cognitive pathophysiology. However, improper standardised screening and guidelines frequently result in cognitive impairment that goes undiagnosed, which accelerates the progression of the disease and cognitive decline. ²⁰

The secret to prompt diagnosis and treatment is automating the evaluation and prediction process. The development of AI has led to automated evaluation methods that increase diagnosis precision. Some of the best results have come from ML and AI-based strategies such as Support Vector Machine (SVM), neural networks and ensemble techniques such as Convolutional Neural Network, AlexNet, GoogLeNet and LeNet5.21

Significance of AI interventions

Early Detection of ASD

Most recent breakthrough is in the area of ASD Over time, AI has been used for a number of ASD-related issues, such as behavioural intervention, early screening and detection and even therapeutic settings to enhance social communication abilities.

Accordingly, ML, a subfield of AI and AI itself have become revolutionary technologies that have the potential to greatly improve the diagnosis of ASD. Computational systems created to carry out operations that normally call for human intelligence—such as pattern recognition and decision-making—are referred to as AI. ML is a branch of AI that focuses on creating algorithms that let systems make decisions and predictions based on data, with the potential to get better with practice. DL, a more complex subset of ML, stimulates the brain’s processing mechanism through intricate neural networks, allowing for more intricate analysis and interpretation. These days, AI includes a wide range of interrelated models and methodologies that can be gruelling to comprehend.

AI enables real-time monitoring and therapy adjustment, ensuring that treatments adjust to the patient’s changing needs. It also enables personalised interventions by customising treatments based on individual patient data. AI also combines various data sources, providing a thorough picture of the patient and facilitating better-informed and successful treatment plans. ²²

Enhances Mental Health

AI is transforming the realm of mental health care by enabling tailored treatments and the emergence of virtual therapists and chatbots. These developments herald a significant change towards possibly more efficient, accessible and scalable mental health services. Some important methods of AI. First one is ML and DL method, which is a supervised algorithm, such as logistic regression, SVM and gradient boosting is employed on structured or textual features to categorise diagnoses, forecast outcomes or determine service flow, typically involving backend analytics.

Models in ML, including SVMs, Random Forests and neural networks, are trained on datasets to discover patterns and features linked to ASD. These models can subsequently classify individuals as either having ASD or not based on their specific characteristics, whereas DL, a branch of ML, employs artificial neural networks with several layers to interpret complex data such as neuroimaging and facial expressions. DL models are capable of automatically identifying significant features, such as neuroanatomical differences from MRI scans or subtle facial cues that may indicate ASD.

Second is data analysis of behavioural aspects such as social interactions, eye-tracking information and various behavioural observations, to detect patterns and behaviours typical of ASD. Further analysis of genetic profiles and electronic health archives is used to reveal patterns and risk factors related to ASD, aiding in early detection and intervention.

Third is multimodal incorporation. AI systems have the capability to combine data from diverse sources (e.g., neuroimaging, behavioural observations and genetic information) to deliver a more thorough and precise evaluation of ASD. Natural language-processing tools include independent language-processing pipelines, tokenisation, topic modelling and emotion detection, utilised either to enhance a conversation or to analyse text collections; Large Language Models (LLMs) are not included. LLMs, on the other hand, are generative models based on transformers that have more than 1 billion parameters (such as GPT-3.5 and GPT-4), can produce free text, maintain contextual memory and perform reasoning without prior examples; they are often fine-tuned for conversations that span multiple turns. The application of AI technology in healthcare is becoming increasingly prevalent, especially in the realm of physical health, while its role in mental health is still restricted. Mental health care depends heavily on the ability to develop mutual understanding, establish relationships with patients and monitor their emotions and behaviours, which makes automating tasks with AI a difficult endeavour.

It has been reported that the effective intervention strategies in contemporary times are applied behavioural analysis (ABA), social skills training and biofeedback.

Applied behaviour analysis is an intervention method based on principles of behavioural psychology widely used in the treatment of children with ASD. ABA works to understand and improve specific behaviour, especially to increase social, communication, educational skills and the skills of daily life, while minimising malnutrition. This helps individuals learn new skills and behaviours by systematically applying reinforcement strategies that encourage and reward the desired behaviour. ABA therapy is highly individual and optimised for each child’s specific requirements and abilities. The treatment plan begins with a detailed behavioural assessment to identify target behaviour and intervention strategies. The behaviour that is learned is then reinforced and cemented through one-to-one teaching sessions using positive reinforcement. ABA also emphasises the importance of data, which is collected and analysed on the basis collected by the doctor to monitor the progress and adjust the treatment plan as needed. Research has shown that ABA is an effective way to improve learning in children with social interaction, communication skills and ASD.

In social skill training for children with ASD an intervention is designed to improve their ability to interact socially in everyday life. This training focuses on their ability to understand social signals, establish effective communication skills and develop friendships. It involves a series of structured directive activities such as role play, social stories, interactive group exercises and modelling. Its intent is to provide practice under the social conditions of the real world to learn and practice new skills in a safe environment.23

Biofeedback helps individuals develop self-regulation skills by providing real-time response to physiological stages, while neuromodulation technology such as transcranial magnetic stimulation (TMS) and temporal difference (TDC) models cortical excitability and neurological plasticity in the deviant circuit stuck in ASD. The current research suggests the potential benefits of these techniques in improving emotional regulation, social function and cognitive performance, but mixed results highlight the need for large, well-controlled tests to validate the effect, safety and optimal protocols. Despite the challenges, these techniques promise as useful therapy in the extensive management of ASD and guarantee further research to direct their translations into clinical practice. Although the treatment of ASD shows biofeedback and neuromodulation ability, research on these techniques is currently in its early stages. Their efficiency, safety and long-term effects and the determination of more clinical testing and studies require that patients may benefit from these interventions. Nevertheless, as a non-pharmacological treatment, the promising additional options for the extensive treatment of ASD provide.

Although there’s no cure for ASD, some specific interventions can be used to handle specific symptoms related to ASD, such as behavioural problems, lack of attention, anxiety and mood swings common in autism. The medicine is frequently used as part of a comprehensive intervention programme, designed to ameliorate the quality of life of the case and daily work. Medicines usually used for ASD symptoms include antipsychotics, antidepressants and stimulants. For example, two antipsychotics substances, risperidone and aripiprazole have been approved by the Food and Drug Administration (FDA) for the treatment of stereotyped and aggressive behaviour in children and adolescents with ASD.

To ensure the efficiency and safety of medication, a physician must carefully monitor the drug as their side effects can occur. The drug’s decision should be based on an individual assessment that considers the patient’s specific requirements, severity of symptoms and potential side effects. At the same time, medical means are often used in combination with non-pharmacological agents such as behavioural intervention and educational support to achieve optimal therapeutic results.

Future Implications

• The use of accurate therapy in the treatment of ASD represents the paradigm of a personal treatment strategy. Its main aim is to tailor the treatment plan for each patient’s genetic information, biomarker, environmental risk history and lifestyle factors. By sequencing the patient’s genome, researchers and doctors can identify specific genetic variants that can affect ASD symptoms so that they can develop targeted treatment with the help of AI-assisted interventions.24

• Research findings suggest that symptoms of ASD are associated with an abnormality in a specific metabolic channel, but it can be modified through specific medications with an approach to dietary adjustment, dietary supplements or symptoms. Depending on possible links seen between nutritional imbalances and ASD symptoms through an AI method, diet and nutritional intervention in the treatment of ASD can be helpful. Specific strategies include limiting certain foods that can increase the symptoms, such as gluten and lactose, as well as increasing the intake of foods that are rich in nutrients required to support the development of the brain and function. The gluten-free, casein-free diet, can help improve behaviour and digestive symptoms in some children with ASD. In addition, omega-3 supplementation with omega-3 fatty acids, vitamins and minerals (e.g., magnesium and zinc) has been suggested to support neurological health and reduce ASD-related symptoms, which potentially is a favourable strategy.

• AI design method will enhance interventions based on technology for ASD. These interventions primarily use computers, tablets, smartphone apps and virtual reality techniques to design interactive learning tools and a series of games designed to improve social skills, communication and cognitive function. A great advantage of technology-assisted interventions is their ability to provide highly individualised learning experiences. Software and applications can be adapted to the child’s specific needs and interests and ensure that the learning material is both attractive and appropriate for the development level of the individual. In addition, the response given by technology is often immediate and consistent and helps children to understand better and treat the process of ASD. The use of virtual reality technology mimics social conditions and provides a safe and controlled environment for children with ASD to practice social interaction and problem-solving skills, which is often difficult to get in traditional educational and therapeutic surroundings. Although technology-assisted interventions have shown considerable capacity, research is still underway on their long-term effects and optimal implementation. To maximise the benefits of these devices, it is often recommended that technology-flapped interventions be used with other medical approaches to provide a comprehensive intervention programme.

• With the growing importance of AI methods in biotechnology within ASD, such as gene-editing, stem cell therapy and biomarker development, opens up new opportunities to treat and understand ASD. Gene-editing technologies, especially the CRISPR-CAS9 system, enable researchers to appropriately modify ASD-related genetic variants, offer insight into how these are modified, promise to explain how specific genetic variants affect the growth and function of the brain, and provide a basis for the development of targeted agents. Stem cell therapy uses the patient’s own induced pluripotent stem cells to study the pathomechanisms of ASD by mimicking the neurodevelopment process in vitro, and to support the discovery of potential cellular alternative agents. In addition, the discovery of a biomarker facilitates the first diagnosis and monitoring of the progression of the disease, which makes personal treatment possible.

Conclusion

In the absence of a specific biomarker, the screening and diagnosis of ASD rely on observing behaviours. To address administrator bias in evaluations, numerous efforts have been made to utilise AI technology to enhance the rate of precise detection. This literature review indicates that research has sought to categorise elements from assessment tools that are most indicative of the diagnosis to streamline the process. Other research has investigated various behavioural traits that might be distinctive to individuals with ASD to employ as indicators for categorisation. Nonetheless, since both ASD and AI research are still quite recent, there are several challenges that must be addressed before utilising these techniques in research or clinical environments. Clinicians can also improve the care they provide for patients with ASD, empower them, and deepen their understanding of this complicated condition by utilising AI.

Limitations of Existing Research

Although significant progress has been made in ASD research, many large boundaries remain.

First, the aetiology of ASD is extremely complex, including genetic, environmental factor and their interactions. Further, AI-linked diagnostic tools work well with the data they are fed upon and beyond that the results can be questioned for their accuracy and generalisability.

Second, there is no single model of AI that accurately describes the full spectrum and severity of ASD, as it is a complex disorder. The asymmetry of ASD is reflected in the extreme variation of symptoms between patients, making it difficult to develop the same clinical criteria and treatment method.

Third, most studies have focused on children and adult patients with ASD, limiting the understanding of the entire life cycle of ASD.

Final, ASD research is uneven throughout the world; more research has been done in resource-rich countries and regions than in resource-limit areas. This imbalance limits a widespread understanding of ASD in different cultural and social contexts. To remove these boundaries, more interdisciplinary, cross-cultural and long-term research, as well as global cooperation, are necessary to elaborate on the understanding of ASD and improve the quality of life of individuals with ASD. Further, many AI models, particularly those based on DL, are considered ‘black boxes’ that lack a clear reasoning basis which clinicians and parents would want to know.