Path 01

Life Sciences & Healthcare AI

You're operating in one of the most demanding AI environments that exists — regulated, high-stakes, and clinically consequential. This is your starting point.

Life Sciences & Healthcare AI is not enterprise AI with a white coat. The failure modes are different, the regulatory surface is enormous, and the trust bar is set by people whose job it is to say no. Most AI products fail here not because the model was wrong — but because the PM didn't understand the environment.

I've spent years building AI products across clinical trials, pharmacovigilance, drug discovery, and health system operations. I write what I wish existed when I was figuring this out — practitioner-level, specific, and honest about what doesn't work.

Start with the industries below, then move to the resources and reading list. If you're building something and want to trade ideas, the virtual coffee is real.

Where AI is being built

Industry Verticals

The sub-domains where AI is having real, measurable impact — and where the hard problems still live.

Drug Discovery

Target identification, molecular design, and candidate screening. Where foundation models are genuinely changing timelines.

Clinical Trials

Patient recruitment, protocol design, and real-world evidence. RAG systems and NLP are replacing manual chart review at scale.

Pharmacovigilance

Adverse event detection, signal detection, and post-market surveillance. High regulatory exposure, enormous data volumes.

Regulatory Intelligence

FDA guidance parsing, SaMD classification, and regulatory pathway analysis. LLMs are reducing weeks of legal review to hours.

Health Systems

EHR-integrated AI, prior authorization, clinical decision support. The distribution challenge is EHR lock-in; the model problem is secondary.

Diagnostics & Imaging

Computer vision for radiology, pathology, and surgical guidance. FDA 510(k) and De Novo pathways shape every product decision.