21st Century Flexner Moment: Charting our Course through Medicine's AI Revolution
Walking through the AMA ChangeMedEd 2025 meeting, I felt the familiar tug of a Flexner moment. So much of the agenda, workshops, and posters leaned into AI as a force reshaping how we teach, assess, and practice. The message was unmistakable: if AI is now better at facts, pattern recognition, and organizing knowledge, then the uniquely human work of medicine must come into sharper focus.
The most promising future isn't one where AI replaces physicians, but where human-AI collaboration outperforms either working alone. This "human-in-the-loop" approach ensures quality and safety while leveraging AI's analytical power alongside human expertise, empathy, and ethical reasoning. In anesthesiology, this might mean AI systems continuously monitoring patient status and alerting us of subtle changes, while we focus on complex decision-making, patient communication, and leading the perioperative team. AI could handle routine documentation and administrative tasks, which are major sources of physician burnout, freeing us to focus on the aspects of care that require human judgment and connection.
In this evolving role, doctors will be collaborators with technology. Envision the anesthesiologist as an augmented diagnostician, integrating AI-derived insights from complex physiological data into real patients' contexts. We will be expert interpreters, translating multimodal data into shared decisions grounded in patient values. We become strategic leaders, freed by automation to better coordinate complex care teams, and empathetic communicators who carry the human relationship through moments of profound uncertainty. Our educational models must evolve accordingly.
Much of what we presently reward is solo performance on recall-heavy assessments. But this is exactly what AI already excels at. Meanwhile, the skills our daily work really demands are team-based coordination, crisis communication, ethical discernment, and adaptive leadership. Yet, these are under-assessed and under-taught. If our jobs are team-based, why are our assessments still so individualistic? The challenge is integrating AI tools to enhance rather than replace human-centered learning experiences. We need AI to amplify our teaching effectiveness while preserving the critical conversations, bedside teaching moments, and collaborative problem-solving that define excellent medical education.
The ChangeMedEd conference reminded me that we are not passive recipients of technological change, we are active shapers of medicine's future. The question is not whether AI will transform medical education, but how we will guide that transformation to align with our professional values and patient care goals.
Recognizing these challenges, SEA has launched the Precision Education Task Force to help our community move from rhetoric to the practical architecture of change. At our August kickoff, facilitated by Drs. Lawrence Chu and Viji Kurup, we organized into five working groups: learning analytics, AI and adaptive learning, faculty development, learning personalization, and assessment innovation. These groups are working to address foundational challenges, including establishing common data standards, designing privacy-protecting federated learning models for multi-institutional collaboration, and ensuring equitable access to these tools across institutions with varying resources. By working together, we can shape this transformation and create a brighter future for anesthesiology education.
If you’re hungry to explore these ideas in community, join us at the SEA Fall Meeting with the theme: From Novice to Expert: Cultivating Excellence in Anesthesia Training. We’ve built the program to translate this moment into concrete practice. You’ll find a dedicated workshop on “Precision Education in Anesthesiology: Personalizing the Path from Novice to Expert,” a workshop on “Smarter Teaching: Leveraging Artificial Intelligence to Create High-Impact Educational Content,” and sessions on debriefing critical events, mastery learning, didactics enhanced by educational technology, and microfeedback that can transform resident growth.
As we move forward in this new era of medicine, we should stop competing with AI on what it will always do better: speed, recall, and pattern detection. Instead, we must double down on what only humans can do at the bedside and in the OR. It’s essential that we prioritize the human aspects of our profession. This means redesigning curricula to emphasize team-based learning, complex decision-making, and ethical reasoning. It means creating assessment methods that value collaboration and communication alongside technical knowledge. And it means preparing our trainees to be both skilled users and thoughtful critics of AI systems.
We can shape this transformation, rather than be shaped by it. The Flexner Report fundamentally transformed medical education by establishing scientific rigor and standardized training. Our challenge is equally profound: preparing physicians who can harness AI's capabilities while preserving medicine's human heart.
