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mujeeb
727 posts

mujeeb
@__mujeeb__
MD | Exploring prod fit AI systems | F.E dev @mono_hq (YC W21) | cant hoop
Katılım Ekim 2024
395 Takip Edilen387 Takipçiler

@Tancrededib Kinda know the right person for this, insanely resourceful and curious, I’ll send him this tweet
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@levilian1 AI-generated summaries from PubMed abstracts, seeded with figures from SUSTAIN-6, SELECT, LEADER etc. The agent reasoned over structured synthetic data, not raw PDFs. The limitation is provenance: I was the curation step between the real source and the eval cases.
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@__mujeeb__ Exactly! Not to mention labs buy data from bench providers and post train… What datasets did you use?
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Real burnout numbers:
• 63% of US physicians report burnout (AMA 2025)
• #1 cause: Administrative work (not clinical complexity)
• Average doc spends 2 hours on EHR for every 1 hour with patients
Fix *that* and you print money. (3/7)
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This is the exact problem with LLM-as-judge evals. The judge scores the output, not the consequence. I built an adversarial eval tier for a clinical trial agent specifically because passing 28/30 cases meant nothing if the 2 failures were confident wrong citations. The benchmark hides what matters.
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@dereckwpaul The citation layer is what makes or breaks clinical AI in practice. Confident answers with wrong sources are worse than no answer. Curious how you handle retrieval failures when the query doesn't map cleanly to any guideline.
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Evidence-based clinical intelligence is becoming a critical infrastructure for many healthcare technology companies.
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@_ayoobami so strict yo, I forgot I set it to be straight like that with me
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I want to found an AI eval framework for healthcare. B2B. Eval tooling today is generic. Healthcare needs domain-specific evaluation where clinical accuracy matters.
I'm an MD who coded through 6 years of med school. I understand both sides. 2 production agent systems with eval pipelines built. Currently at Mono (YC W21). Lots of energy and agency to burn.
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Yup, I was thinking along these lines today. I’ve been learning a lot with AI over the past few months, and I genuinely think it’s the best way.
A personalized curriculum: after every module, I’m asked detailed, first-principles-esque questions about what I’ve learned, and I’m corrected on my shortcomings.
Detailed notes are created after major phases
All of this happens while I’m building an actual project.
It’s so refreshing.
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Been thinking about this:
A SaaS that auto-generates learning roadmaps.
Not generic ones.
Based on what's actually working right now.
You want to learn AI engineering?
It shows you what people are shipping today.
Not what was hot 6 months ago.
The roadmap updates itself.
Because trends move faster than any course can keep up.
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Twitter is cool but it’s 10x better when you #connect with people in your niche.
If you’re into:
> LLMs + RAG
> AI Agents
> AI Automation
> System Design
> Product Thinking
> AI Driven Development
> OpenClaw automations
> SaaS & Product distribution
Drop a +1 & let’s connect.
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