MedED-AI Epoch #114 : Enhancing Medical Curriculum with AI-Driven Integration of Social Determinants of Health

As medical education evolves, there is an increasing recognition of the importance of integrating Social Determinants of Health (SDOH) into the curriculum. I recently had the privilege of hosting a thought-provoking webinar on this topic, where Dr. Mohammad Taha expertly walked us through the various determinants of social health that shape healthcare outcomes. The session sparked a dynamic conversation among participants and raised an essential question that has the potential to reshape how we teach and learn in medical education: How can artificial intelligence (AI) be used to map the curriculum and enhance the integration of SDOH?

Understanding the Role of SDOH in Medical Education

SDOH encompasses factors like socioeconomic status, education, neighbourhood environments, access to healthcare, and the broader social and community context that affect health outcomes. For future healthcare professionals, understanding these determinants is crucial to addressing health disparities, promoting health equity, and delivering patient-centered care.

However, embedding SDOH comprehensively into medical curricula is a challenge. Traditionally, these topics may be covered sporadically, with gaps that can leave students underprepared to deal with complex social factors in clinical practice. This leads us to a powerful potential solution: artificial intelligence.

AI’s Role in Mapping SDOH in the Curriculum

During the webinar, I asked the audience to reflect on how AI could be used to identify and address where SDOH might be missing or under-represented in the medical curriculum. The response from participants was compelling. Using AI, we can map and analyze entire curricula to pinpoint gaps, overlaps, and opportunities for more in-depth coverage of SDOH.

For instance, AI tools could analyze course syllabi, lecture materials, and assessment methods to assess the extent to which SDOH topics are addressed and identify areas for improvement. By doing this, institutions could ensure that SDOH is covered in various clinical contexts—from infectious diseases to chronic conditions—ensuring that future doctors understand the broader social factors that influence patient health.

AI-Driven Scenarios for Teaching SDOH

Another exciting application of AI is in the development of interactive learning scenarios. AI has the potential to generate complex, real-world case studies or simulations that incorporate multiple layers of social determinants. These scenarios could allow medical students to engage with virtual patients facing various social challenges such as food insecurity, unemployment, or housing instability and practice developing holistic care plans that take these factors into account.

For example, an AI-driven scenario could simulate a patient presenting with uncontrolled diabetes, but the true challenge lies in the patient’s limited access to healthy food, living in a food desert. The AI system could adapt the case dynamically, changing variables such as the patient’s financial situation or support network, enabling students to explore a range of outcomes based on their clinical decisions.

Enhancing Critical Thinking with AI

What makes AI particularly powerful in this context is its ability to provide personalized feedback and adapt scenarios to the learner’s decision-making process. By exposing students to diverse social situations, AI helps to foster critical thinking and deepen their understanding of how SDOH influence patient care outcomes. This allows students to better anticipate challenges they will face in real-world practice and learn to navigate these effectively, especially when managing patients from underserved communities.

Throughout the webinar, we discussed how different medical educators are approaching the challenge of incorporating SDOH into the curriculum. Several participants shared their experiences and perspectives, particularly on the limitations of traditional educational methods in addressing these complex topics. The consensus was that while current approaches have merit, there is significant room for innovation—and AI offers one of the most promising avenues.

Many experts agreed that, although SDOH is introduced in early clinical training, there is often insufficient integration across disciplines or as part of problem-based learning scenarios. With AI’s ability to curate and deliver rich, multidisciplinary learning experiences, there is an opportunity to expand how and when we address SDOH, embedding them in clinical reasoning, diagnostics, and patient interaction skills.