AI Literacy for Life Sciences & Healthcare: Foundations
Kamayani Gupta
Co-Founder + CEO at KAMI Think Tank
Michelle Yi
Co-Founder + CTO at KAMI Think Tank
Evaluate AI with the same rigor you bring to any scientific or clinical claim
AI is already inside your literature reviews, your vendor pitches, and your team's workflows. But the training available right now wasn't built for you because it's either too technical (built for engineers) or too shallow (generic "AI 101" that could apply to any industry).
You already know how to evaluate evidence. You've built a career separating real signal from noise, in a paper, a dataset, or a claim at a conference. And even though most professionals just haven't been taught how to apply AI, doesn't mean AI gets a pass just because it's new. The same scientific rigor applies to it.
In this course, you'll be able to:
Catch where an AI-generated literature summary is wrong before you cite it
Judge whether a model is actually fit for a specific life sciences or clinical use case
Spot exactly where bias enters an AI system, and what to do about it
This class is built on the 50+ sold-out workshops across the Bay Area directly with life sciences companies and healthcare organizations. This course distills what's actually worked in the room, built specifically for life sciences and healthcare, and AI vendor agnostic.
What you’ll learn
Learn to evaluate AI literature, models, and bias with clinical rigor so you catch what others miss and lead AI decisions at work.
Apply a verification checklist to AI-assisted literature reviews and RAG tools like Elicit before trusting results
Practice on real, flawed AI-generated summaries in live case studies
Build the habit of treating AI output as a hypothesis to verify, not a citation to trust
Use a fit-for-purpose framework to evaluate any AI model; in the same way you'd validate an assay before trusting it
Learn when a model's reasoning needs to be explainable, and to whom, before you rely on it
Identify where bias enters an AI system - whether it's the training data, model design, or deployment context
Learn which governance controls meaningfully reduce bias, and which ones are just theater
Leave with a vendor-neutral framework that transfers across whatever AI tools your org adopts next
Use the same evaluation lens whether it's a new vendor, an internal build, or a future model
Learn directly from Kamayani & Michelle
Kamayani Gupta
AI x Life Sciences expert - 15 yrs of experience, starting with Watson Health
Michelle Yi
Early AI researcher - worked on the Jeopardy Watson team!
Who this course is for
Scientists and R&D leads who read AI claims skeptically but haven't been taught a structured way to evaluate AI tools or outputs
Clinical, medical affairs, or regulatory professionals who need to judge whether an AI tool already in use is actually right for the task
Healthcare and clinical informatics leaders exploring AI adoption who need a rigorous, vendor-neutral evaluation framework, not sales talk
What's included
Live sessions
Learn directly from Kamayani Gupta & Michelle Yi in a real-time, interactive format.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Community of peers
Stay accountable and share insights with like-minded professionals.
Real case studies using AI-generated outputs
Sessions rooted in actual use cases, not hypotheticals
Office hours
No need to feel lost alone - come join one of the office hour sessions!
Maven Guarantee
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Course syllabus
Week 1
Sep
11
Week 1: Critically Assessing AI Literature
Sep
11
Optional: Q&A!
Week 2
Sep
18
Week 2: Evaluating Models for Life Sciences Work
Schedule
Live sessions
2-4 hrs / week
Fri, Sep 11
7:00 PM—8:00 PM (UTC)
Fri, Sep 11
8:00 PM—8:30 PM (UTC)
Fri, Sep 18
7:00 PM—8:15 PM (UTC)
Frequently asked questions
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Reimbursement
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