ASU Shufeldt School of Medicine (MD) Medicine InterviewFormat, Questions & Prep Tips

Why ASU Shufeldt — specifically, what draws you to an MD programme that integrates medical engineering into the core curriculum?

Be concrete. Reference the engineering-integrated curriculum, ASU's biomedical innovation ecosystem, and how your specific interests in technology, health systems, or device design align with the programme.

Describe a time you solved a problem by thinking across disciplines — not just using medicine or science alone. What did you learn?

This is a key differentiator at Shufeldt. Draw on engineering, design, computing, or systems thinking. Show that cross-disciplinary thinking is natural for you, not just aspirational.

An AI diagnostic tool recommends a treatment your clinical judgement disagrees with. The AI has a higher published accuracy rate than you for this condition. What do you do?

Address the physician's accountability, the limits of algorithmic accuracy (edge cases, training data bias), informed consent for AI-assisted diagnosis, and the importance of human oversight in clinical decision-making.

You are asked to design a triage algorithm for a rural clinic that must allocate limited ventilators during a respiratory disease surge. What principles guide your design?

Apply utilitarian and equity frameworks. Address QALY vs. first-come-first-served vs. lottery approaches, community input in algorithm design, and the physician's role when the algorithm recommends against personal clinical judgement.

A patient declines a recommended medical device (e.g., an implantable glucose monitor) because they distrust technology in their body. How do you respond?

Respect patient autonomy. Explore the specific concern — religious, cultural, privacy-related, or general distrust. Provide information without coercion and document the informed refusal.

Phoenix has a large and growing Spanish-speaking and Indigenous population with significant healthcare access gaps. How does your background or training prepare you to serve these communities?

Show cultural humility and specific awareness of Arizona's demographic health landscape. Be honest about limitations in your experience while demonstrating genuine commitment to addressing them.

Tell me about a project — academic, professional, or personal — where you had to iterate and redesign after your first approach failed.

Engineering design thinking values iteration. Show that failure is part of your problem-solving process, not something to hide. Emphasise what the iteration taught you.

Should medical students be required to learn basic programming or data science skills? Why or why not?

Argue a position and defend it. Acknowledge counter-arguments (cognitive load on medical training, cost, differential baseline). Connect to the growing role of AI and data in clinical decision-making.

How would you explain to a sceptical patient why their electronic health data might be shared with an AI research consortium?

Address HIPAA, de-identification, the concept of informed consent for secondary data use, the patient's right to opt out, and the societal benefit of health data research.

As a brand-new medical school, ASU Shufeldt does not yet have residency match data. How do you think about that risk in your decision to apply here?

Show maturity and self-awareness. Acknowledge the uncertainty, articulate the programme's distinctive value proposition, and demonstrate that you have weighed the trade-offs thoughtfully.

MMI station: A patient declines a recommended implantable cardiac monitor, saying they don't trust 'computers inside my body' after reading about device hacking online. Talk with them.

Respect autonomy and explore the specific fear — security, privacy, or general distrust. Provide balanced information on device safety and data protection without coercion, and document an informed refusal. Shufeldt's engineering-integrated identity makes calm, literate device communication a natural assessment target.

MMI station: You are shown validation data for a clinical decision-support algorithm that performs well overall but was trained almost entirely on data from one large urban hospital. How do you interpret its readiness for use in a rural Arizona clinic?

Address dataset shift, external validity, and the risk of degraded performance on populations unlike the training set. Recommend local validation and human oversight. This blends the data competency with Shufeldt's engineering and health-systems orientation.

MMI station: A classmate on your team has built most of a design-thinking project and now wants to put only their own name on the submission, sidelining two quieter teammates. Respond to them.

Address fair attribution, teamwork, and integrity directly but collegially. Advocate for the contributors and propose an equitable solution. Engineering-integrated programmes value collaborative credit and professionalism under real team friction.

MMI station: Describe a technical or scientific concept from outside medicine — in engineering, computing, or design — that you think every future physician should understand. Why?

Show genuine cross-disciplinary fluency and the ability to teach. Connect the concept to clinical relevance — for example, how iterative design or basic data literacy improves care. Shufeldt looks for applicants who think naturally across boundaries.

MMI station: Explain to a sceptical older patient, in plain language, why their de-identified health records might help train a medical AI system, and what protections are in place.

Cover de-identification, HIPAA, the right to opt out, and the societal benefit of health-data research — without jargon and without dismissing the concern. Confirm understanding. Technology-ethics communication is distinctive to this programme.