Earlier this week, I had the privilege of presenting a webinar, “Applying Patient-Level Data & AI in Rare Diseases & Oncology,”. We closed the session by demonstrating how AI can reduce up to 40% workloads for key functions like medical affairs, commercial, market research, and clinical.
I want to thank everyone who joined, and I am deeply encouraged by the engagement and thoughtful discussions around how we can make AI truly transformative in life sciences. I was truly inspired by the interest it received, including from #AI experts representing many top pharmaceutical and biotech companies.
It’s clear to me, now more than ever, AI can reshape our work — not just for efficiency, but for real impact on patient care. In the session, I highlighted some compelling findings from McKinsey & Co. about AI’s potential to reduce workloads in pharma companies.
A few key takeaways from our discussion:
✅ Access to Data is critical: Integrated patient-mediated data is far superior to the traditional tokenization approach. Successful Real-world evidence generation depends on harmonizing longitudinal patient records and unstructured data, which make up over 80–90% of healthcare data.
✅ Context matters: 80% of AI projects fail to show ROI because the data lacks clinical depth or domain-specific context. ⚙️
✅ Human in the Loop: Combining AI-driven insights with expert annotation and clinical validation ensures reliable, real-world relevance.
✅ Patient-first approaches: Patient-mediated data collection enhances transparency, accuracy, and completeness—especially vital when studying rare conditions.
Thanks to my team for organizing this discussion and for demonstrating how our AI tools can quickly answer real-world data questions—like analyzing insights from large patient data sets in seconds.⚡
For those who missed the live session, you can still request access to the full recording by sending us a message.
At Clinakos, we remain committed to advancing responsible, patient-centered AI—transforming how life sciences organizations understand diseases, accelerate discovery, and improve care outcomes.
