Inspiration

Healthcare & Clinical Workflows The sources highlight healthcare as a major sector where documentation is being automated continuously in real-time:

What it does

• AI Medical Scribes: AI systems are deployed to listen to patient visits and faithfully transcribe encounters directly into Electronic Health Records (EHR) as legitimate patient data. This automates the documentation of patient histories and interactions. • Clinical Note Drafting: Domain-Specific Language Models (DSLMs) allow doctors to instantly draft clinical notes from consultations and cross-reference patient history against medical guidelines, significantly reducing administrative paperwork. • Discharge Summaries: Hospitals, such as the Chelsea and Westminster NHS Trust, are piloting AI tools that automatically generate patient discharge summaries by extracting diagnoses and test outcomes from medical records to reduce delays

How we built it

We built this app on Google Cloud Platform and GitLab Duo

Challenges we ran into

There is no information available regarding GitLab Duo or how to build workflows specifically using that tool. Tthe security risks associated with autonomous AI agents (often referred to as "Agentic AI") differ significantly from those of static models. Because agents act on goals, execute workflows, and integrate with external tools, their vulnerabilities can lead to tangible operational damage rather than just incorrect text generation.

Accomplishments that we're proud of

We managed to build proof of concept prototype

What we learned

Human Oversight: The philosophy favors "agent-like" workflows that are reliable and auditable, rather than fully autonomous agents that operate without control

What's next for CuraFlow

From "Time-to-Market" to "Time-to-Trust": As AI agents begin taking actions (e.g., processing refunds), governance becomes non-negotiable. Systems now require immutable audit trails and "trace-level" visibility to debug why an agent made a specific decision.

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