Welcome to AI in Healthcare
AI in Healthcare covers what’s working and what isn’t, as AI moves into clinical care, medical operations, and life sciences. Each edition looks at new research, real deployments, the methods behind them, and the safety and regulatory questions that come with putting AI into practice. More than 100,000 people subscribe, including clinicians, data scientists, researchers, policymakers, informatics engineers, and the leaders responsible for AI across health systems, payers, and pharma.
It assumes you know the field. The point isn’t to explain what a large language model is: it’s to look closely at whether a given approach holds up in production and under regulatory scrutiny.
It’s written for people who build, deploy, govern, or study AI in healthcare and want a clear-eyed read on it, no hype and no doom. If you work in or near this field and want to keep up with what’s real, you’re in the right place.
Who writes it
David Talby is CEO of John Snow Labs and Pacific AI, helping companies apply regulatory-grade AI to solve real-world problems in healthcare and life sciences. John Snow Labs’ medical language models are used by many of the world’s largest medical centers, pharmaceutical companies, and payers to extract, de-identify, and harmonize clinical data at scale. Pacific AI is the AI validation, monitoring, and governance platform for healthcare.
David has extensive experience building and running web-scale software platforms and teams, in startups, open-source projects, and previously at Microsoft and Amazon. He holds a PhD in computer science and master’s degrees in both computer science and business administration.
What you’ll get
New research, read for what it means in practice: studies, benchmarks, and methods, weighed for whether they survive contact with real data.
Real deployments: case studies and lessons from production projects, including the parts that didn’t go to plan.
Validation and oversight: efficacy, bias, safety, robustness, monitoring, governance, laws and regulations.
Open source: new libraries, models, and tools worth knowing about.
The people doing the work: conversations with researchers, clinicians, and builders.
An open invitation
We’re early in figuring out how AI can make healthcare better, and there’s a lot we don’t know yet. Treat every article as an invitation to add your own knowledge and experience, and to teach me and the rest of the readers something. The comments are open and I read them.
Best,
David
