Agentic AI is shaking up pharma R&D. In a recent PharmStars’ Exec Ed pharma member roundtable on #agenticAI, pharma innovation leaders picked their top 3 most promising use cases for agentic AI: 🌟 83% Real-time data analysis during clinical trials 🌟 75% Personalized patient monitoring 🌟 67% Clinical trial simulation 🌟 25% Drug target identification and screening 🌟 25% Drug design optimization 🌟 17% Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction Industry-wide adoption is still evolving. During the discussion, one participant reflected… “Sometimes you feel like everybody else is way ahead of you. But, in reality, we're all struggling with a lot of the same things — and we're probably all pretty much in the same spot.” Pharma innovation leaders identified several barriers to implementation of agentic AI: 🌟 58% Interpretation of decisions 🌟 58% Data security/privacy 🌟 42% Ethical concerns (e.g., bias) 🌟 42% Unintended consequences 🌟 17% Data breaches 🌟 8% Implementation costs 🌟 8% Lack of real emotional intelligence 🌟 50% Other What was behind the 50% "other"? One innovation expert said quality of data. “Garbage in, garbage out. While we'd love to have this great future, we need to ensure that the foundations are really strong.” What “other” barriers do you see impacting the adoption of agentic AI in pharma? #AI #genAI #Digitalinnovation #AIinPharma #PharmaInnovation #pharmatech #digitalhealth #pharma #biopharma
