AI Literacy: Knowing How to Use It Is Not the Same as Knowing When to Trust It

As artificial intelligence becomes part of medicine, knowing how to use it may be the least demanding part of becoming competent.

We are increasingly being encouraged to become AI literate. Doctors are attending workshops, learning to construct effective prompts and exploring ways to incorporate artificial intelligence into clinical practice.

The assumption is that greater familiarity will lead to safer, more effective use.

But familiarity and competence are not necessarily the same thing.

A clinician can become remarkably proficient at obtaining sophisticated answers from AI without developing a corresponding ability to evaluate them. They may understand how to phrase questions, request supporting evidence and refine responses, yet remain unable to recognise when the information is incomplete, misleading or simply wrong.

This is an important distinction because the technical barriers to using generative AI are extraordinarily low.

We describe a problem and receive an answer. We ask for clarification and obtain an explanation. With practice, the responses become increasingly tailored to our needs.

The experience can create the impression that we are becoming more capable.

Perhaps we are. But capable of what?

Consider a doctor using AI to investigate an unfamiliar diagnosis. The system produces a comprehensive explanation, identifies relevant investigations and recommends a management approach. The response is coherent, clinically plausible and supported by references.

The doctor reviews it carefully.

But suppose an important contraindication has been overlooked, or the recommendation is based on evidence from a different patient population.

How would the doctor know?

A sophisticated prompt cannot compensate for the absence of the knowledge required to evaluate the response. Nor does requesting references guarantee that those references are genuine, accurately represented or applicable to the patient.

This exposes a contradiction in how we often discuss human oversight.

We insist that clinicians remain responsible for verifying AI-generated recommendations, while simultaneously encouraging them to use the technology in areas where their own knowledge may be limited.

We are, in effect, asking people to verify information precisely because they do not already know it.

Medicine has always confronted this problem. Doctors routinely seek advice outside their expertise, consult specialists and rely on resources they cannot independently reproduce. The difference is that these sources exist within established, if imperfect, systems of professional accountability, evidence appraisal and clinical governance.

AI-generated explanations do not automatically carry the same assurances.

There is also a fundamental distinction between general-purpose language models and clinical AI systems validated for specific tasks. Their capabilities, evidence bases and limitations differ. Treating all AI-generated recommendations as equivalent is itself a failure of AI literacy.

Australian regulators have begun addressing these issues. Ahpra's guidance reinforces the clinician's responsibility to understand the intended use and limitations of AI tools, while the Australian Commission on Safety and Quality in Health Care emphasises appropriate evaluation, governance and monitoring.

These expectations are necessary. But they require more than attendance at an AI workshop.

Meaningful literacy involves understanding what a system was designed to do, what evidence supports its performance and when its recommendations fall outside those boundaries. It requires recognising the difference between a plausible explanation and a validated conclusion.

Perhaps most importantly, it requires intellectual humility.

There will be circumstances in which we cannot independently determine whether an AI-generated answer is correct. Recognising that limitation is not a failure of competence. Pretending it does not exist may be.

The solution is not to distrust every recommendation or insist that clinicians independently reconstruct every answer. It is to understand when additional evidence, specialist advice or a different decision-making process is required.

Artificial intelligence will almost certainly become an increasingly familiar part of medicine. We should welcome efforts to help clinicians use it effectively.

But we should be cautious about confusing confidence in operating a system with competence in judging its outputs.

AI literacy is not demonstrated by how effectively we can obtain an answer. It is demonstrated by whether we understand the limits of our ability to trust it.