Skip to content Skip to footer
Join our LinkedIn Group and be part of the conversation on AI in life sciences
AI-Powered Evidence Generation Using Real-World Data

AI-Powered Evidence Generation Using Real-World Data

Overview Real-World Data (RWD) to Real-World Evidence (RWE) conversion stands at the center of modern life-sciences decision-making. Randomized controlled trials remain essential for establishing causality under controlled conditions, yet they leave gaps in understanding how treatments perform across diverse patients, care settings, and longer time horizons. RWD, drawn from electronic health records, claims, registries, wearables,…

Read More

Agentic AI Driving the Next Phase of Continuous Evidence Generation in Pharma

2026 Prediction #4: Agentic AI Driving the Next Phase of Continuous Evidence Generation in Pharma

For years, the pharmaceutical industry has followed a familiar evidence cycle. Teams design a clinical trial, execute it, lock the database, analyze the results, and wait for another study to address remaining questions. They wait for post-market surveillance to reveal real-world outcomes and for regulators or payers to request additional data before reimbursement decisions. However,…

Read More

MadeAi | 2026 Prediction #3: Role-Based Tool Isolation: The Gold Standard for Secure Pharma AI

2026 Prediction #3: Role-Based Tool Isolation: The Gold Standard for Secure Pharma AI

The pharmaceutical industry is currently at a pivotal moment in the AI landscape. After years of cautious AI experimentation limited to isolated labs and pilot programs, something fundamentally different is unfolding in 2026. The central question has shifted from “Does AI work?” to “How do we deploy it safely and at scale?” especially in organizations…

Read More