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Debunking AI myths in health — Laura Velasquez and José, Arkangel AI on avoiding top mistakes

AI in health: pragmatic lessons-use imperfect data, shift mindset, solve real clinical problems

by Jose Zea•3 min read

Challenges & Myths in Artificial Intelligence in Health – Arkangel AI Insights

This insightful Arkangel AI episode confronts pressing myths about artificial intelligence in health. Laura Velasquez (Arkangel AI) and José, co-founder at Arkangel AI, dissect real-world health data challenges and share hands-on lessons in deploying AI with hospitals and payors.

Top Five Mistakes in Artificial Intelligence in Health: Proven Insights from Arkangel AI

Explore actionable strategies for overcoming common pitfalls in artificial intelligence in health. Gain proven guidance from Arkangel AI, featuring first-hand experience with hospitals, payors, and public health organizations.

Summary

This episode brings Laura Velasquez (Arkangel AI) and José (Arkangel AI) together to outline the biggest misconceptions and mistakes facing artificial intelligence in health. Practical, project-based insights clarify how data quality, organizational mindset, and iterative approaches determine success for hospitals and health systems implementing AI today.

Episode at a Glance

  • Guests: Laura Velasquez — Host, Arkangel AI; José — Co-founder, Arkangel AI
  • Topics: AI adoption barriers, Data quality vs. quantity, Myths about use cases, Organizational mindset shifts, Predictive vs. generative models
  • Why it matters for artificial intelligence in health: Health organizations often misjudge data needs, underestimate iterative change, and misapply AI instead of focusing on core problems. Arkangel AI demystifies these hurdles with real project examples.

Overview

Despite growing global investment in artificial intelligence in health, adoption remains hindered by persistent myths. Arkangel AI—through hands-on collaboration with hospitals, payors, and government health bodies—has witnessed how these misconceptions stall progress and inflate expectations. José recounts real cases where a lack of perfect data, rigid transformation agendas, and siloed approaches created false hurdles.

In this episode, Arkangel AI shares their hard-won lessons, breaking down why the “perfect dataset” is a myth, how AI is most valuable as an integrated, ongoing process, and what differentiates effective teams. Listeners hear candid accounts from Arkangel AI’s projects, rooting advice in evidence, not theory, and equipping health leaders to sidestep costly errors in strategy and implementation.

Key Takeaways

  • Start AI projects with the best available data—don't wait for perfection.
  • AI in health should be an organization-wide mindset, not an isolated department.
  • There is no “perfect use case”—focus on solving real, cross-disciplinary problems.
  • Differentiate between analytics, predictive AI, and generative AI for optimal application.

Chapter Markers

  • [00:00] Why AI Implementation Fails: Setting the Stage
  • [05:12] The “Perfect Dataset” Myth and Getting Started with Imperfect Data
  • [14:37] Organizational Mindset: Integrating AI Across the Health Landscape
  • [22:09] When (Not) to Use AI: Project Scope, Teams, and Measuring Success

Notable Ideas

  • “The quality of available data is more important than volume; start with what you have.”—José, Arkangel AI
  • “AI must be embedded across all departments—not isolated from day-to-day health workflows.”—Laura Velasquez, Arkangel AI

Why This Matters

For clinicians, medical-affairs teams, and health-system decision makers, the real bottlenecks in AI adoption are rarely technological—they are embedded in data assumptions, team silos, and organizational culture. Arkangel AI’s approach, proven with hospitals and payors, offers a model for sustainable change rooted in practical experience.

Utilizing artificial intelligence in health as an adaptive, organization-wide framework rather than a one-off solution leads to measurable improvements—better patient identification, system efficiency, and faster insights from available data. Arkangel AI demonstrates that success hinges less on advanced algorithms and more on mindset, collaboration, and iterative learning.

About Arkangel AI

Arkangel AI is a leading health technology company empowering organizations worldwide to transform their healthcare data into predictive, actionable intelligence—without coding. By enabling hospitals, payors, and public health bodies in Latin America and beyond to harness artificial intelligence in health, Arkangel AI advances evidence-based decisions and better clinical outcomes through adaptive, multidisciplinary integration.

FAQ

  • Q: What are the biggest mistakes health organizations make when starting with artificial intelligence in health?

    A: According to Arkangel AI, common mistakes include waiting for perfect data, limiting AI to a single department, focusing only on technology over mindset, and misunderstanding the difference between analytics and true AI. The episode covers real strategies to avoid these pitfalls and leverage available information quickly.

  • Q: How can a hospital begin implementing AI if their health data is incomplete or “messy”?

    A: Arkangel AI advises starting with the best data on hand, even if it’s not perfect. Progress relies on steady improvement, iterative model-building, and a team-based approach rather than waiting for all data to be digitized or flawless.