Today's graduates will practise in a profession that looks very different from the one their curriculum was written for.
They will work alongside AI scribes, decision-support tools, wearable data streams and patients who arrive having already consulted a chatbot.
Yet in many health programs, digital health and AI are still treated as electives, guest lectures or afterthoughts.
That gap is becoming a professional risk.
The Skills Gap Is Not About Coding
When people hear "AI literacy", they sometimes picture programming.
For clinicians, that is not the point.
Practitioners do not need to build models. They need to be able to use them well, question them, and know when not to trust them. In practice, that means being competent in several areas:
- Understanding what AI is and isn't. How these systems generate output, why they can be fluent and wrong, and what "training data" means for bias.
- Evaluating tools. Asking about validation, scope, data handling and regulatory status before adopting anything.
- Verifying outputs. Checking claims, references and recommendations against authoritative sources.
- Protecting privacy. Knowing what happens to patient information when it enters a tool.
- Maintaining accountability. Understanding that professional responsibility does not shift to a vendor or an algorithm.
- Communicating with patients. Explaining, honestly and simply, how AI is being used in their care, and discussing what they have found themselves.
None of these require technical backgrounds. All of them require deliberate teaching.
Why Early Practitioners Are Especially Exposed
Experienced practitioners have a reference point. They know what good clinical reasoning feels like, so they can often sense when something is off.
Early-career practitioners are still building that reference point.
If they learn to lean on AI before developing strong foundations in reasoning, there is a real risk of what might be called skill substitution rather than skill support. Tools could end up shaping judgement before judgement has fully formed.
The goal is the opposite. Students should be learning to reason first, then to use AI as a way of extending that reasoning, not replacing it.
What Good Education Looks Like
Effective AI literacy is practical, case-based and integrated into core clinical training, not isolated in a single module.
Some approaches that work:
- Compare AI output with expert reasoning. Let students see where AI is helpful, where it is incomplete, and where it is wrong.
- Practise verification. Make checking sources and outputs a routine part of assessment.
- Work with realistic, de-identified data. Longitudinal cases with biochemistry, biometrics and notes teach interpretation in a way that single-snapshot cases cannot.
- Teach professional obligations alongside the tools. Regulatory guidance and ethical duties belong in the same conversation.
- Involve practitioners and industry. Students learn best when tools and expectations reflect what practice actually looks like.
That last point matters. Curricula move more slowly than technology, and the gap closes fastest when educators, practitioners and technology builders work together.
A Role for Experienced Practitioners
This is not only a job for universities.
Experienced practitioners shape the next generation every day, as mentors, supervisors and colleagues. The habits they model with new technology, including healthy scepticism, careful verification and openness to learning, will matter as much as anything taught in a lecture.
Mentoring in this era increasingly includes how to use powerful tools wisely.
Preparing Practitioners for Augmented Practice
Throughout this series, the argument has been that the future of healthcare is not automation, but augmentation: skilled practitioners supported by systems that help them interpret complex data.
That future depends on practitioners who are prepared for it.
Augmented practice works best when the practitioner understands what the system is doing, why it is doing it, and where its limits lie.
Digital health capability is becoming a core professional competency.
The sooner education treats it that way, the better prepared the next generation will be.
Robbie is a Sydney-based Accredited Practising Dietitian (APD) and Exercise Scientist with over 17 years of experience. He holds a Bachelor of Exercise Science (Nutrition) and a Master of Nutrition and Dietetics, and is the Director and Co-founder of HealthBank.