A pediatric neurodevelopmental clinic network in North America focused on early detection and diagnosis of ASD and ADHD in children. Their goal was to reduce assessment delays, clear backlogs, and ensure consistent diagnostic standards.
The clinic faced long, manual assessment processes that took weeks and involved multiple clinicians. Limited access to specialists caused diagnosis delays. Important inputs from doctors, caregivers, and behavior tracking were scattered and not connected. Reports also varied between clinicians, making it hard to maintain consistent records and track a child’s development over time.
CloudifyOps leveraged Agentic AI with LangChain and Amazon Bedrock to automate healthcare workflows. AI agents parsed caregiver forms, analyzed behavioral videos, and interpreted doctorpatient sessions. Using Amazon Transcribe Medical and LLaMA 2, they extracted insights and generated structured reports. The solution cut manual tasks, boosted diagnostic accuracy, and ensured HIPAA-compliant, scalable patient care.
CloudifyOps halved ASD/ADHD diagnosis time, boosted accuracy by 25%, doubled clinician productivity, enabled remote screenings, and standardized reports for easier review and care planning.
After implementing AI, the clinic automated assessments by analyzing both structured data like questionnaires and unstructured data like behavior videos. GenAI tools interpreted clinical conversations to identify signs of ASD or ADHD and suggest diagnoses. AI also generated clear, standardized reports with patient history and insights. High-risk cases were flagged early, helping doctors prioritize care and improve early intervention.
Amazon SageMaker, LangChain, Amazon Bedrock, LLaMa 2 (HuggingFace), Amazon Transcribe Medical, AWS Comprehend Medical, Amazon ECS and EFS and Open source behavioral datasets
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