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…
If Prediction #1 marked AI’s move into the core of life sciences operations, Prediction #2 defines how that AI will operate: not as passive copilots, but as agentic systems that reason, plan, and execute work autonomously. In CapeStart & MadeAi’s 2026 Predictions for AI in Life Sciences webinar, Angeline Dhas, Head of Product Management for…
Every professional in the life sciences knows the feeling: literature review is challenging, time-consuming, and above all, exhausting. This essential step in research can feel like a massive hurdle, slowing down innovation and consuming countless hours. Recognizing this frustration, MadeAi developed MadeAi-LR, a powerful GenAI-enabled solution designed to transform the literature review process. It provides…
In an era where scientific publications double every nine years and over 2.5 million new research papers are published each year, keeping your literature review up to date is nearly impossible. Enter the Living Literature Review, a significant shift that turns static research snapshots into continually evolving knowledge ecosystems. Why Traditional Literature Reviews Are Becoming…
Understanding FDA Literature Review Requirements The FDA requires literature reviews to ensure that regulatory decisions are based on solid scientific evidence. This isn't just about compiling papers; it's about systematically searching, appraising, and synthesizing published data to demonstrate safety, efficacy, and compliance. These requirements span multiple domains, from drug approvals to medical devices and health…
Though many organizations see the value of AI in literature reviews and evidence generation, not all can adopt AI in the same way. Some teams are ready for a web-based SaaS solution. While others, especially large pharma and regulated enterprises, face internal AI policy barriers, data residency requirements, and IT constraints that make SaaS adoption difficult,…
