The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Reading a document is easy. Understanding its meaning is the real challenge.
While building AI-powered medical document intelligence systems, I’ve been learning that extracting words is only one part of the problem.
Consider this sentence:
“Patient denies chest pain.”
A system might recognize the words correctly but still extract the wrong information if it ignores the word “denies.”
The same challenge appears in phrases like:
“No history of asthma”
“Mother had breast cancer”
“Rule out pneumonia”
“Aspirin discontinued”
Each sentence requires more than text recognition. It requires understanding context, negation, uncertainty, relationships, and whether information is current.
This is one of the challenges I’m exploring while building MDIS (Medical Document Intelligence System) at Cognate AI.
My focus is on developing systems that go beyond extracting information—toward structured outputs, validation, evidence traceability, and human review.
Because in healthcare AI, extracting the right words is not enough. The system must preserve what those words actually mean.
I’m continuing to learn, build, test, and improve this system one step at a time.
What do you think is the biggest challenge in making AI understand clinical language accurately?
#AIEngineering #HealthcareAI #MedicalAI #DocumentIntelligence #BuildingInPublic
negation like that is exactly where naive extraction falls apart, great example
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Then: 1 month, 1 template.
Now: 1 week, 15 templates.
Same designer, different workflow.
I wrote up how I built no-code.supply: fifteen website templates, each with its own brand, in HTML and React, and some in Framer too. Claude Code did most of the typing. I did the directing.
no-code.supply
How I built 15 website templates with an AI coding agent in a week
How I built 15 AI-ready website templates with Claude Code in a week: the workflow, the quality bar, and why a template is only the starting point.
Going from one template a month to fifteen a week is wild. How much of the design direction did you still do by hand?
Your website isn't only being read by people anymore. AI agents are reading it too.
When someone asks an AI assistant to find a dentist, compare prices or book an appointment, that assistant visits websites and tries to make sense of them. It doesn't see your design. It reads your structure.
A page can look perfect to a person and still be a puzzle to a machine. What makes the difference:
→ Real headings, navigation and buttons
→ One clear content hierarchy
→ Form fields with proper labels
→ Buttons that name the action
→ Prices, hours and services in a consistent format
Build for both audiences and your site stays clear for people and readable for machines.
Is your website ready for both?
Buttons that name the action is the one most sites still get wrong. Clean split in the carousel too.
I-Sale (demo shopping webapp)
A dynamic online store optimized for discovery and interaction, featuring a real-time live search interface and a streamlined checkout mockup for seamless navigation from cart to completion.
Lovely 😍