Improving child healthcare through early diagnosis of autism
Cognoa· United StatesDelta LakeCognoa built Canvas Dx, the first FDA-authorized diagnostic device for autism in children ages 18-72 months, using the Databricks Data + AI Platform to run machine learning models over structured questionnaire answers and unstructured home videos. This shortened the average time to diagnose autism by 3 years compared to the historical journey, and enables remote diagnosis starting at 18 months versus the U.S. average diagnosis age of 4.3 years.
Overview
Cognoa built Canvas Dx, the first FDA-authorized diagnostic device for autism in children ages 18-72 months, using the Databricks Data + AI Platform to run machine learning models over structured questionnaire answers and unstructured home videos. This shortened the average time to diagnose autism by 3 years compared to the historical journey, and enables remote diagnosis starting at 18 months versus the U.S. average diagnosis age of 4.3 years.
This entry has 13 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Diagnosing autism has historically been hindered by subjectivity and inconsistency: there isn't a clear process, existing assessments are long and in-clinic, there's a growing shortage of specialists, and there are disparities in diagnosis based on socioeconomic status, race, gender, and geography. Accurately diagnosing a child for autism can be a long and difficult journey, with parents and caregivers spending as much as 3 years seeking answers.
The solution
Cognoa uses the lakehouse to take in both the structured questionnaire answers as well as the unstructured videos, and then runs that data through a machine learning model to reach a diagnosis. When there is insufficient information for Canvas Dx to confidently render a determination, the device abstains from producing either a positive or negative output, an important method of risk control in medical machine learning algorithms.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other healthcare entries in the register.
Less paperwork, more time for people: Azure OpenAI at Landsberg Hospital
Klinikum Landsberg am Lech, a German acute-care hospital, deployed an AI language solution built on Azure OpenAI with partner Pexon Consulting that transcribes doctor-patient consultations in real time via tablet, recognizes medical terms, structures the content, and transfers it directly into the hospital information system.
Novo Nordisk builds AI drug discovery platform on Azure with Microsoft Research
Novo Nordisk partnered with Microsoft Research to build an AI platform on Azure AI and data stacks spanning regulatory affairs, early research, drug discovery and trial design, using Azure OpenAI Service, Azure Cosmos DB and Azure Kubernetes Service, with Power BI and Power Apps for collaboration. The platform includes a copilot for researchers, shared reasoning-chain templates, and governance/auditing of how data and models are used. The teams published early results on predictive AI models for cardiovascular disease risk detection, including an algorithm that Novo Nordisk says predicts patients' cardiovascular risk better than the best clinical standards, drawing on more than 100 years of insulin research data.
Novo Nordisk· DenmarkAzure OpenAI Service · Azure Cosmos DB · Azure Kubernetes Service +2BAYADA Builds a Unified, AI-Ready Platform for Compassionate Care
BAYADA Home Health Care is consolidating three separate legacy data platforms (multiple practice management systems, an on-prem ODS, and Snowflake) and 65+ enterprise data sources into a single Databricks Lakehouse under its Data Modernization program, using medallion tiers, Lakeflow Jobs, Asset Bundles, and Unity Catalog governance. As part of the migration, BAYADA uses an LLM-powered code converter and Databricks Assistant to automate SQL and stored-procedure translation, and applies a machine learning-based data mastering accelerator to create golden Client, Payor, Candidate, and Referrer records. BAYADA reports roughly 30% better workload performance and cost efficiency versus its prior Snowflake environment, and is laying a governed foundation for AI agents supporting payroll validation, compliance monitoring, and operational insights, separately from its roadmap for AI-assisted chart review and risk prediction.
BAYADA Home Health CareDatabricks SQL · Lakeflow Jobs · Unity Catalog +1
Heidi Health fine-tunes NVIDIA Parakeet/Nemotron ASR, cutting clinical transcription latency 75% and costs 64%
Heidi Health, which processes 2.4 million clinical consultations weekly across 110 languages, fine-tuned the open NVIDIA Parakeet V2 speech recognition model on 1,500 hours of curated clinical audio using the NVIDIA NeMo framework and 8x H100 GPUs, replacing its closed-source vendor ASR. The in-house Nemotron-based ASR stack cut end-to-end transcription latency from about 3.0 seconds to 0.7 seconds (75%+ improvement), reduced word error rate from 13% to 9.4%, and cut operating costs by 64% by eliminating per-minute API pricing, while giving Heidi full ownership of model weights for data sovereignty.
Was this helpful?
Your feedback helps us improve our use case database