You Are What You Practice: Embedding FUTURE-AI Principles in XR+AI Healthcare Simulation

First, a call-out to the recent article by Karim Lekadir (et al.) published in BMJ 2025;388:e081554. I highly recommend you go read it.

In healthcare, the quality of care starts long before a provider steps into a clinical environment. It begins with how they’re trained. As someone deeply involved in XR+AI in healthcare simulation, I believe that the frameworks we use in training are just as critical as the tools we deploy at the point of care. One such framework is FUTURE-AI—recently detailed in a BMJ article (BMJ 2025;388:e081554)—which outlines the essential principles for developing trustworthy artificial intelligence (AI) tools in healthcare.

A High-Level Overview of FUTURE-AI

The FUTURE-AI framework is built around six key principles, each designed to ensure that AI systems are ethical, effective, and reliable:

• Fairness: AI tools must work equally well for everyone. This means designing systems that identify and mitigate biases to ensure equitable care.
• Universality: The technology should be applicable across diverse clinical settings, adapting to varying patient populations and local practices.
• Traceability: Every AI decision should be documented and transparent, allowing for accountability and continuous improvement.
• Usability: AI must be user-friendly, fitting seamlessly into the workflow of healthcare providers and enhancing rather than hindering care.
• Robustness: AI systems need to perform reliably even when facing unexpected conditions or data variations.
• Explainability: The rationale behind AI decisions should be clear and understandable, building trust among clinicians and patients alike.

Originally designed for point-of-care applications, these principles ensure that the AI tools used in real clinical settings are safe, transparent, and reliable.

Training Today for a Better Point-of-Care Tomorrow

But here’s a crucial insight: the effectiveness of point-of-care AI starts in our training environments. Think about it—if we build our simulation education on frameworks like FUTURE-AI, we’re setting up the next generation of healthcare providers—nurses, EMS professionals, physicians—to think critically about the tools they will eventually use in practice.

You are what you practice. When trainees experience realistic, robust, and transparent simulation scenarios, they learn to expect—and demand—these qualities in the AI tools they will encounter as professionals. By incorporating FUTURE-AI principles into our XR+AI simulation, we are not just teaching clinical skills. We are teaching a mindset:

• Practice Like It’s Real: Our simulation scenarios need to mimic the complexities and uncertainties of real-life clinical situations. This means embedding fairness, robustness, and explainability into every simulated decision-making process.
• Building Competence Through Exposure: When learners interact with simulation systems developed with FUTURE-AI in mind, they gain a deep understanding of what constitutes a trustworthy tool. They learn to identify when a system is operating outside these principles—when it’s biased, unclear, or simply not robust—and they’re empowered to push back or seek improvement.
• Translating Simulation to Professional Practice: The robust training provided by XR+AI simulation becomes the foundation on which professionals evaluate these tools in their workplaces. As XR and AI become ubiquitous with point of care treatment in the healthcare setting, our professionals will rely on their training to inform their decisions. Exposure ensures that they use the technology effectively but also that they contribute to its evolution by advocating for and identifying systems that truly align with ethical and practical standards.

FUTURE-AI and XR+AI Simulation: A Perfect Partnership

Let’s break down how the FUTURE-AI acronym applies directly to XR+AI simulation in education:

• Fairness and Universality in simulation, ensure that scenarios reflect the diverse realities of unique patient populations and clinical environments.
• Traceability and Explainability are woven into the simulation software so that every decision point and assessment criteria is transparent, fostering critical reflection and learning.
• Usability and Robustness standards drive simulation tools that are intuitive and reliable, providing learners with the unpredictable nature of real-world practice while maintaining consistency in deployment.

By developing systems that incorporate these elements, we are not only enhancing educational quality but also setting the stage for a future where AI+XR tools in clinical practice are continually used and improved upon by informed, competent professionals.

Building the Future of Healthcare Today

The FUTURE-AI framework offers a high-level blueprint for trustworthy AI at the point of care. However, another power of this framework is realized when it is integrated into our training environments. Educators and simulation specialists have the unique opportunity to model what real, effective, and ethical AI-enabled healthcare should look like. This prepares our learners to be not only competent healthcare providers but also savvy users and critics of the technology that will shape their future practice. It is incumbent upon us, technology providers, to build robust systems that engage faculty and learners with hands-on XR+AI tools, and give them ownership over it.

I invite you to consider how these principles can be further integrated into your simulation programs and to share your experiences. After all, the future of healthcare begins with how we train today.

– Devin Marble