Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Introducing the CARES Model: Integrating Artificial Intelligence, Medical Education, and Patient-Centered Care
0
Zitationen
2
Autoren
2025
Jahr
Abstract
Artificial Intelligence (AI) is transforming the delivery of patient-centred healthcare in Canada and around the globe. As AI becomes mainstream in daily clinical practice, it is increasingly critical to equip physicians and medical trainees with the skills to effectively integrate AI into patient-centered care. In Canada, medical education is guided by the CanMEDS framework, which is structured around seven CanMEDS roles: Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar, and Professional. Despite the growing influence of AI in healthcare, there is a notable absence of AI-specific competencies within medical education for critically evaluating AI tools, interpreting AI-generated outputs, and safely and ethically integrating AI into clinical decision-making. To bridge this gap, we suggest a new model for physicians and medical trainees to critically evaluate the use of AI in clinical practice, based on patient-centered principles. This model is based on the core concepts of Communication, Autonomy, Respect, Equity, and Safety, which together form the CARES model. Integrating the CARES model into medical education should adopt a constructivist approach, leveraging active learning, case-based scenarios, simulations, and real-world experiences to prepare learners for the complexities of AI in clinical practice. Our research suggests that the CanMEDS framework offers an ideal foundation to explore the core domains of the CARES model, which can be adopted and integrated into daily clinical practice to promote digital literacy. Importantly, the CARES model can be adapted to fit existing medical curricula and tailored to align with global efforts to integrate AI into medical education. Additionally, we have found that central to this approach is the incorporation of feedback loops from both learners and instructors to ensure a sustained focus on patient-centered care. Our findings highlight the opportunities presented by the CARES model to promote digital literacy among physicians and medical trainees in a novel way using the existing CanMEDS framework. By leveraging the flexibility of the CanMEDS framework, we hope to increase digital literacy among physicians and medical trainees. The CARES model represents a novel approach to prepare the next generation of healthcare providers to use AI safely and effectively in their practice while maintaining a patient-centered focus.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.493 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.377 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.835 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.555 Zit.