Enhancing Clinical Workflows and Optimizing Efficiencies - with Patricio La Rosa at Bayer
🎯 Summary
Podcast Episode Summary: Enhancing Clinical Workflows and Optimizing Efficiencies - with Patricio La Rosa at Bayer
This 29-minute episode of the AI and Business Podcast, featuring Patricio La Rosa, Head of N2N Decision Science in Seed Production Innovation at Bayer Crop Science, explores the intersection of AI, clinical workflows, and the broader implications for drug development and healthcare delivery. The conversation draws parallels between agricultural innovation (where La Rosa has expertise) and pharmaceutical R&D, focusing on the practical deployment and economic viability of AI-driven sensing technologies.
1. Focus Area: The primary focus is on enhancing clinical workflows within drug development and clinical trials using AI, specifically addressing the deployment of scalable sensing technologies and probabilistic modeling. Key themes include the transition of AI-validated metrics from R&D into scalable clinical practice, the economic constraints of advanced computational models, and the necessary evolution of regulatory processes to keep pace with technological advancement.
2. Key Technical Insights:
- R&D to Practice Scalability: A critical challenge is ensuring that sensing modalities (like advanced imaging or biomarker detection) developed for R&D—which often require high computational power—are industrialized, cost-effective, and scalable enough for routine clinical practice post-drug approval.
- Probabilistic Foundation of AI: From an academic and modeling perspective, nearly all modern AI, including deep learning and generative AI, is fundamentally built upon the law of probability (chance and organized chance), contrasting with deterministic phenomena governed by physical laws (like ordinary differential equations).
- AI Deployment Layers: AI is being deployed in clinical trials for process automation (low-hanging fruit) but also for ingesting critical metrics that improve future trial design—a form of continuous, data-driven optimization.
3. Business/Investment Angle:
- Cost vs. Time Stalemate: Despite AI integration, the industry average for drug development cost ($1.5B–$2B) and time (10–15 years) remains stubbornly high, suggesting that while AI speeds up marker discovery, regulatory and recruitment bottlenecks persist.
- Cloud Economics and Vulnerability: The high upfront cost of AI infrastructure necessitates reliance on cloud providers who can maintain the economy of scale for compute power while guaranteeing hyper-regulation and data protection (a key vulnerability point).
- Monetization of Patient Data: A future market opportunity lies in the potential monetization of patient information that contributes to successful drug development. If patient data helps solve major problems, there is a philosophical and economic argument for patients to participate in the resulting revenue share, treating data as a new form of currency.
4. Notable Companies/People:
- Patricio La Rosa (Bayer Crop Science): Guest, expert in clinical trial design, drawing connections between agriculture and healthcare innovation.
- Matthew Damello (Emerge AI Research): Host, guiding the discussion on AI adoption, ROI, and technological differentiation.
- Cloud Providers: Mentioned as essential partners for maintaining the necessary computational infrastructure and regulatory compliance.
5. Future Implications: The industry is moving toward a future where the regulatory system must mature and advance in parallel with AI technology to avoid becoming the primary bottleneck for drug delivery speed. Furthermore, the conversation suggests a significant future shift toward data ownership and incentive structures, where patients might become active financial stakeholders in the cures their data helps create. Cybersecurity must advance concurrently with AI capabilities, as smarter analytical tools also imply smarter potential threats.
6. Target Audience: This episode is highly valuable for Life Sciences Executives, Clinical Operations Leaders, AI Strategists in Pharma/Biotech, and Healthcare Investors who need to understand the practical, economic, and regulatory hurdles facing AI deployment in clinical research workflows.
🏢 Companies Mentioned
💬 Key Insights
"Think about what is going on today with the development of AI or LLM models that are large and now that are using maybe information that is protected under copyright, right? And they're asking at least that. What does it mean in practice for the patient? And my data is helping and contributing to the development of the drug. What opportunities do I have to participate in the revenue chart?"
"cybersecurity will continue to technically play a huge role in the way on how we use AI. In the way on how AI could be used, actually, against cybersecurity infrastructure to accessing information."
"The outcome of mature learning and our random variables is because the paradigm that we're using for everything that we have built is that we're coming from the probability space."
"we've held this audience's hand trying to get them to differentiate between deterministic and probabilistic technologies. Now we have GenAI coming down the pike, which blurs that line even further."
"...we've held this audience's hand trying to get them to differentiate between deterministic and probabilistic technologies. Now we have GenAI coming down the pike, which blurs that line even further."
"The speed to identify markers has increased because you're able to analyze information in ways that you couldn't do before... However, if that means that we're able to accelerate the discoveries of markers that are more robust and reliable, the regulatory process has not changed. And so we might be faster today or feel that we are faster, but also that needs to go along the lines of understanding what are we going to do with the regulatory system in our intention to ensure that we can increase that speed. Otherwise, that will become the bottleneck."