IEEE International Conference on Big Data (BigData) · pp. 8598–8600 · 2024
Enhancing Early Diagnosis of Autism Spectrum Disorder Using Multimodal Data and Explainable AI Models
- autism
- multimodal
- explainable AI
- healthcare
Summary
Early diagnosis of Autism Spectrum Disorder (ASD) is critical — earlier intervention leads to substantially better developmental outcomes — yet traditional diagnosis is slow, subjective, and resource-intensive.
This work explores combining multimodal data with explainable AI (XAI) models to support earlier, more reliable ASD detection. The emphasis on interpretability is deliberate: for AI to be trusted in a clinical setting, practitioners need to understand why a model flags a given case, not just receive a black-box prediction. Presented at IEEE BigData 2024.
Authors
Y. Abdollahinejad, M. F. Kabir
Venue
2024 IEEE International Conference on Big Data (BigData), pp. 8598–8600.
Links
- Google Scholar record — direct IEEE link coming soon