Safety-Constrained Agentic AI for Autism Screening: A Multimodal, Clinician-Guided Architecture.

Autism spectrum disorder (ASD) diagnosis often encounters substantial delays due to several reasons, such as shortages of trained specialists and limited access to care in rural and underserved communities. Moreover, it is very difficult to perform behavioral assessments within a single clinical visit, as it is significantly dependent on the child's behavior. Delayed diagnosis can postpone early intervention, which is important for improving developmental outcomes in children with ASD. Although artificial intelligence (AI) is increasingly explored in healthcare, its adoption in ASD screening remains limited due to concerns about reliability, governance, consent management, bias, and clinical trust. In this work, we propose a conceptual, governance-driven, clinician-augmented AI framework designed to assist clinicians during the ASD screening process rather than replace them. The proposed architecture collects various inputs such as text, audio, and video of a child from parents, schools, or caregivers, and then it runs through multiple specialized agents who are responsible for consent validation, bias monitoring, model selection, confidence-based abstention, and providing a structured report which will help clinicians in their assessment. Caregivers receive only non-diagnostic guidance, while clinicians receive structured decision-support information designed to aid clinical evaluation. The main goal of this article is not to validate model performance. We are mainly trying to design an agentic framework where governance and safety rules can be managed properly through multiple specialized agents. Although we have performed a single model training using a ResNet-50 facial-image classification model on a publicly available dataset, our main goal was to validate the governance and multi-agent system. The Stage 1 governance validation was done using more than a hundred scenarios. It is very important to highlight that our article should be viewed as a conceptual governance-driven agentic framework with Stage 1 validation, and it is definitely not a fully workable clinical solution. In the next phases, we plan to collect clinically validated data and focus more on model training, multimodal integration, and real-world validation.