⚡ Quick Summary

This scoping review explores the implementation of generative artificial intelligence (GenAI) in the assessment and treatment of autism spectrum disorders (ASD). The findings indicate promising applications in screening, diagnosis, and intervention, while also highlighting significant methodological and ethical challenges.

🔍 Key Details

• 📊 Dataset: 553 records screened, 10 studies included
• 🧩 Domains of application: Screening and diagnosis, assessment and intervention, caregiver education
• ⚙️ Technologies used: Transformer-based classifiers, GANs, multimodal emotion recognition, LLM-based chatbots
• 🏆 Performance metrics: Improvements noted, but limited comparative analyses

🔑 Key Takeaways

• 🤖 GenAI shows potential for enhancing ASD care through automation and personalization.
• 📈 Most studies reported performance improvements, yet faced limitations like small sample sizes.
• ⚖️ Ethical concerns regarding data biases and model hallucinations were prevalent.
• 🔍 Comparative analyses were sparse, indicating a need for standardized evaluation metrics.
• 🌐 Future research should focus on ethical, transparent, and clinically validated GenAI applications.
• 📚 Study conducted across multiple databases including PubMed and Scopus.
• 🗓️ Timeframe: Studies published from January 2014 to February 2025.

📚 Background

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent deficits in social communication and the presence of restrictive, repetitive behaviors. Traditional diagnostic and intervention pathways often rely heavily on clinician expertise, which can lead to delays in care and limited scalability. The advent of generative AI presents new opportunities to enhance the assessment and treatment of ASD, potentially transforming how care is delivered.

🗒️ Study

This scoping review systematically searched multiple databases, including Embase, PsycINFO, PubMed, Scopus, and Web of Science, to identify empirical studies that reported on the application of GenAI in ASD care. The review aimed to map the landscape of GenAI applications, compare them with traditional methods, and highlight both methodological and ethical challenges faced in this emerging field.

📈 Results

Out of 553 records screened, 10 studies met the inclusion criteria, focusing on three main domains: screening and diagnosis, assessment and intervention, and caregiver education. While most studies indicated potential performance improvements through the use of GenAI technologies, they also pointed out significant limitations, including small sample sizes, data biases, and limited validation of results. The comparative performance against baseline methods was often lacking standardized metrics, making it difficult to draw definitive conclusions.

🌍 Impact and Implications

The findings from this review underscore the emerging potential of GenAI in enhancing autism care. By automating and personalizing various aspects of assessment and intervention, GenAI could significantly improve the quality of care for individuals with ASD. However, addressing the identified methodological and ethical challenges is crucial for the successful integration of these technologies into clinical practice. The implications of this research extend beyond ASD, as the principles of GenAI could be applied to other areas of healthcare.

🔮 Conclusion

This scoping review highlights the transformative potential of generative AI in the assessment and treatment of autism spectrum disorders. While the technology shows promise, it is essential to navigate the associated ethical and methodological challenges to ensure its effective and responsible implementation in clinical settings. Continued research in this area is vital to unlock the full benefits of GenAI in healthcare.

💬 Your comments

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