"Donut of truth" is an often-heard metaphor for a CT scanner: "We didn't know what was going on so we sent the patient to the donut of truth for the answer."

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I don't know where the term originated, but it isn't hard to imagine how it came about. Modern CT gantries look an awful lot like big donuts and the "truth" portion of the metaphor describes the unfortunate practice of diagnostic testing supplanting clinical thought.

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The "donut of truth" metaphor now represents something broader for me—it captures how we often look to technology for simple answers to complex human problems.  I study how we can harness artificial intelligence and data science not to replace human judgment, but to enhance the deeply human work of medicine.

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My research sits at the intersection of AI, medical education, and emergency medicine. Rather than seeking technological "truth," I focus on understanding healthcare as it actually happens—studying team dynamics, communication patterns, and the cognitive burdens providers face. Whether developing AI-powered tools for personalized medical education or analyzing clinical experiences through data science, the goal remains the same: supporting the humans at the heart of healthcare.

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The "donut of truth" reminds me that meaningful transformation requires understanding human experience first. Technology only creates impact through its effect on human systems. My work in reproductive healthcare in emergency medicine, AI-assisted educational tools, and human-centered implementation research all stem from this core belief—that we must design systems that work better for everyone by deeply understanding and supporting how care is actually delivered.

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The metaphor still guides my teaching: helping learners ask the right questions, interpret answers thoughtfully, and remember that in medicine, truth emerges not from any single source, but from the complex interplay of evidence, experience, and human judgment.