ScreeningPaL: LLM-NLP Enabled Early Autism Detection Method from Caregiver's Free-Text Input.

Abstract

Identifying autism traits and detecting the spectrum of disorder early can substantially improve quality of life. We present a text-driven approach for early autism risk detection that analyzes caregiver-reported behavioral descriptions using advanced natural language processing techniques. Synthetic free-text generated from validated screening items is used to train multiple language models, which are then evaluated on an external benchmark dataset (TASD) to assess generalization under domain shift. Fine-tuned transformer models achieve the highest performance, reaching 90% accuracy, outperforming GPT and Gemini models, and conventional NLP baselines. Augmenting training datasets with noisy, realistic text further improves model performance, specifically recall in traditional pipelines, demonstrating the potential of noise-aware data augmentation for free-text screening. This methodology enables translational and low-cost early assessment without requiring structured questionnaires or speech samples. This approach provides early cues that may assist specialist evaluation, promote accessible and proactive developmental health monitoring.