The Future of Collaboration: What Interprofessional Training Includes AI and Why It Matters for Digital Health
The rapid and pervasive integration of Artificial Intelligence (AI) is fundamentally reshaping professional landscapes, with its impact being particularly transformative in the field of digital health. AI is moving beyond a specialized tool for data scientists and is becoming an essential component of daily clinical and administrative practice. This shift necessitates a corresponding evolution in how healthcare professionals are educated. Interprofessional Education (IPE), defined as occasions when "learners from two or more professions learn about, from, and with each other to enable effective collaboration and improve health outcomes," must now explicitly include AI competencies [1]. The question is no longer if AI should be included, but what specific elements of AI are being integrated into interprofessional training curricula to prepare a collaborative, AI-fluent workforce.
Foundational AI Competencies for Collaborative Practice
Effective collaboration in an AI-driven environment requires a shared, foundational understanding of the technology across all professional boundaries. This training focuses on establishing shared AI competencies that transcend individual professional silos. All team members—from physicians and nurses to pharmacists and social workers—must possess a common, non-technical grasp of AI principles, including how models are trained, their capabilities, and, crucially, their inherent limitations [2].
A critical component of this training is the focus on ethical and legal frameworks. Interprofessional teams must be trained together on the ethical implications of using AI in patient care, covering areas such as data privacy, algorithmic bias, and accountability when an AI system makes an error. Furthermore, AI's influence on traditional professional roles and responsibilities is a key training area. Studies indicate that training must address and foster acceptance of AI's role in the team, ensuring that all professionals maintain clarity on their respective roles while collaborating with intelligent systems [3].
AI-Enhanced Simulation and Communication Training
One of the most significant advancements in AI-integrated IPE is the use of AI-enhanced simulation to improve team communication and decision-making. Traditional IPE often relies on simulated patient encounters, but AI introduces a layer of complexity and realism. For instance, AI-enabled Virtual Reality Simulation (VRS) is being utilized to allow interprofessional teams to practice complex scenarios, such as managing a patient whose care plan is heavily influenced by an AI-generated risk score [4].
These advanced simulations often incorporate AI tools that coach and score communication, providing objective, real-time feedback on interprofessional interactions. This is particularly valuable for training in areas like motivational interviewing or conflict resolution, where subtle communication cues are critical. The goal is to train teams to collaboratively interpret AI-generated data—whether it is a diagnostic support output or a predictive model—and synthesize it into a unified, human-validated care plan. This ensures that AI acts as a powerful augment to the team, not a replacement for human judgment and collaboration.
The Role of Expert Commentary and Continuous Learning
The landscape of AI in digital health is characterized by its rapid evolution. New models, ethical challenges, and regulatory guidelines emerge constantly, making continuous professional development an absolute necessity. For professionals, relying solely on initial training is insufficient; they must actively seek out and engage with expert commentary and up-to-date resources to stay current.
For professionals seeking to deepen their understanding of these complex topics and stay abreast of the latest developments in digital health and AI, the resources and expert commentary available at www.rasitdinc.com provide an invaluable resource. This platform offers professional insights and analysis that bridge the gap between academic research and practical application, helping practitioners navigate the complexities of AI integration in collaborative care.
Conclusion
The inclusion of AI in interprofessional training marks a pivotal moment in the evolution of professional education. The focus is clearly on developing shared competencies, fostering ethical awareness, and utilizing advanced simulation techniques to prepare a workforce capable of effective collaboration in a digital-first world. By moving beyond siloed, profession-specific training and embracing a truly interprofessional approach to AI education, we can ensure that the next generation of professionals is equipped to ethically and effectively harness the power of AI. The future of digital health depends on interprofessional teams trained to collaborate not only with each other but also with the intelligent systems that are rapidly becoming integral members of the care team.
References
[1] Nexus Interprofessional Education. Artificial Intelligence (AI) and Interprofessional Practice and Education. Available at: https://nexusipe.org/informing/resource-center/artificial-intelligence-ai-and-interprofessional-practice-and-education
[2] Ng, F. Y. C., et al. (2023). Artificial intelligence education: An evidence-based framework for curriculum development. BMC Medical Education, 23(1), 785. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC10591047/
[3] Wiss, A. (2025). Development of the AI Acceptance Scale for Health Professions Students. ScienceDirect. Available at: https://www.sciencedirect.com/science/article/abs/pii/S2405452625000151
[4] Liaw, S. Y., et al. (2023). Artificial intelligence in virtual reality simulation for interprofessional communication training: mixed method study. Nurse Education Today, 120, 105632. Available at: https://www.sciencedirect.com/science/article/pii/S0260691723000126