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AI in health improves evidence synthesis and patient identification — Juan Carlos Alandete, Sanofi

Arkangel & Sanofi: AI-driven evidence and patient ID, building trust to scale impact

by Jose Zea•3 min read

Improving Health Outcomes with Arkangel AI & Sanofi

Discover how Arkangel AI and Sanofi are advancing artificial intelligence in health to transform evidence generation, patient identification, and real-world implementation in pharmaceuticals.

Redefining Access: Artificial Intelligence in Health with Arkangel AI & Sanofi

This episode explores how artificial intelligence in health, driven by real-world lessons from Sanofi, is optimizing evidence, confidence, and patient outcomes—all in conversation with Arkangel AI’s Laura Velasquez.

Summary

Host Laura Velasquez (Arkangel AI) speaks with Juan Carlos Alandete from Sanofi about harnessing artificial intelligence in health to improve patient identification, evidence consolidation, and process efficiency. The discussion surfaces practical challenges and actionable approaches for pharma, payers, and providers, especially in Latin America.

Episode at a Glance

  • Guests: Juan Carlos Alandete — Market Access Lead, Sanofi
  • Topics: Measuring real-world value, AI-driven evidence generation, Patient identification, Building trust in digital partnerships, Scaling use cases in pharma
  • Why it matters for artificial intelligence in health: Real-world collaboration between Arkangel AI and Sanofi highlights immediate opportunities and cultural barriers to adopting AI tools that improve outcomes and operational efficiency.

Overview

Artificial intelligence in health is now central to delivering the right therapy to the right patient at the right time—especially as health systems struggle to process vast clinical data. In this episode, Laura Velasquez of Arkangel AI and Juan Carlos Alandete from Sanofi dissect how AI models are already accelerating evidence synthesis and supporting clinicians, using practical global and Latin American examples.

Their conversation examines persistent industry barriers—such as distrust, competing priorities, and limited data skills—and contrasts U.S. and LatAm adoption. Using Sanofi’s real strides in AI-driven evidence generation, they identify actionable strategies for healthcare leaders seeking to improve access, showcase value, and make AI tools sustainable for institutions of every size.

Key Takeaways

  • Artificial intelligence in health can compress months of evidence review into hours, freeing critical time for clinical decisions.
  • Successful AI implementation relies on building trust between technology providers and healthcare institutions, as seen in Sanofi collaborations.
  • Pilot programs focused on specific conditions, like oncology, rapidly clarify what works and foster team alignment for broader scaling.
  • Balancing day-to-day healthcare pressures with investment in AI training and evaluation is essential for sustainable returns on innovation.

Chapter Markers

  • [00:00] The role of AI and value challenges in health systems
  • [05:12] Evidence consolidation: systematic review meets generative AI
  • [14:37] Patient identification: predicting risk and matching therapies
  • [22:09] Lessons from global vs. regional adoption, trust, and scaling

Notable Ideas

  • “AI models serve as a bridge between real-world needs and innovative therapies by consolidating massive medical data.” — Juan Carlos Alandete, Sanofi
  • “The main obstacle to AI adoption isn’t technology, but building genuine trust across the system.” — Juan Carlos Alandete, Sanofi
  • “Institutions not seriously considering AI now will risk being left behind.” — Juan Carlos Alandete, Sanofi

Why This Matters

Health systems worldwide face mounting complexity in matching therapies to patient needs in real time. Artificial intelligence in health, as discussed by Arkangel AI and Sanofi, directly addresses these pain points by enabling faster evidence processing and patient identification—yielding measurable impacts on care quality, efficiency, and resource allocation.

For clinicians and medical-affairs teams, these insights spotlight practical steps to improve workflows, gain value from data already collected, and foster trust with pharma partners like Sanofi. By aligning AI adoption with clearly defined outcomes and transparent collaboration, even resource-limited institutions in Latin America can drive operational and clinical advancements at scale.

About Arkangel AI

Arkangel AI is an innovator in artificial intelligence in health, focused on empowering healthcare organizations across Latin America and beyond. By creating AI solutions for evidence synthesis, patient monitoring, and process optimization, Arkangel AI supports health leaders, medical teams, and payers in accelerating access, improving outcomes, and driving sustainable change.

FAQ

  • Q: How does artificial intelligence in health improve evidence generation?

    A: As explained by Juan Carlos Alandete (Sanofi), AI tools streamline the review of vast clinical literature, consolidating evidence for decision-makers. Systems like those discussed with Arkangel AI can reduce review time from months to hours, making data-driven outcomes more accessible and actionable.

  • Q: What barriers slow AI adoption in Latin American healthcare?

    A: According to this episode, obstacles include heavy day-to-day demands on health leaders, lack of training in AI’s benefits, and widespread distrust. Approaches highlighted by Arkangel AI and Sanofi include targeted education and careful pilot projects to build confidence and demonstrate value before scaling.