About the Lab

Applied AI for Pharma & Healthcare

PharmaTools.AI is an applied AI lab founded by Nick Lamb, PhD — exploring ways GenAI can make medical communication clearer, safer, and more human. Each experiment blends science, design, and language to show how people and AI can work together more effectively.

Nick's background in medical writing, creative technology, and AI research shapes the lab's approach — from prototypes like BiomarkerFinder to full tools like Patiently AI and MedCheckr®.

Award-winning AI experiments Advisory & strategy Open to collaborations R&D briefs welcome
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Experimental Demos - PharmaTools Lab

Early-Stage Experiments

Beta

MedCheckr

MedCheckr compliance checking interface

AI-powered compliance tool that checks promotional claims against global pharma codes using RAG-based AI for real-time regulatory feedback.

Beta

SideEffectViz

SideEffectViz interactive visualization interface

Interactive visualization and clustering of medication side effects using FDA adverse event data and machine learning.

Beta

BiomarkerFinder

BiomarkerFinder cancer biomarker discovery interface

Discover key biomarkers in cancer and their role in diagnosis, prognosis, and treatment - powered by Open Targets and AI insights.

Beta

Case Study Creator

Case Study Creator interface

AI-powered business case study generator for pharma and healthcare teams. Ideal for commercial, medical and training use cases.

Beta

AI Jargon Buster

AI Jargon Buster interface

Turn AI gobbledygook into words humans understand!

Coming Soon

MedTermTracker

Analyzing how medical terminology for specific conditions changes over time using NLP and trend analysis.

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Publications - PharmaTools Lab

Behind the Experiments: Research & Reflections

AI data scarcity illustration

What Happens When AI Runs Out of Data?

AI Advances December 2025

Exploring the implications of data scarcity for AI — and why the future of machine learning depends on models that learn from the world, not just the web.

AI Training Experiential Learning Machine Learning
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medRxiv preprint validation study

Validation of an AI-powered mobile application for personalizing medical note explanations

medRxiv September 2025

Three-phase validation of Patiently AI showing high expert ratings for accuracy (4.49/5) and clarity (4.53/5).

Health Literacy Patient Engagement Clinical Validation
Read on medRxiv
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