{"id":1440,"date":"2026-08-04T15:11:59","date_gmt":"2026-08-04T15:11:59","guid":{"rendered":"https:\/\/facultyhub.elsevier.com\/en\/?p=1440"},"modified":"2026-08-07T15:01:10","modified_gmt":"2026-08-07T15:01:10","slug":"ai-in-health-education-your-questions-answers","status":"publish","type":"post","link":"https:\/\/facultyhub.elsevier.com\/en\/ai-in-health-education-your-questions-answers","title":{"rendered":"AI in Health Education: Your Questions Answers"},"content":{"rendered":"\n

In June 2026,\u00a0Elsevier\u00a0hosted\u00a0the\u00a0webinar,\u00a0AI in Health Education:\u00a0Preparing Students for Real-World Clinical Readiness<\/a>,\u00a0bringing together three leading voices in health education to explore the growing role of\u00a0AI\u00a0in clinical training and practice.\u00a0\u00a0David Game<\/strong> examined the impact of AI on students academically and socially, Dr Philip Xiu<\/strong> explored how curricula and assessment must evolve in the age of AI, and Dr Ximena Alvira<\/strong> addressed AI’s role in clinical settings.\u00a0\u00a0<\/p>\n\n\n\n

The session generated a wealth of interesting questions from attendees \u2013 more than we could address in the time available. Our panelists have since taken the time to respond to those we didn’t get to, and we’re pleased to share their answers here. Responses are attributed to each panelist individually, as each contributed where they felt best placed to do so. <\/p>\n\n\n\n

Please note that some of the questions have been lightly edited for clarity and concision, while preserving the original intent.<\/em> <\/p>\n\n\n\n

Q1: Can you give an example of a teaching activity that would enhance students\u2019 persistence and grit in the world of AI? <\/h2>\n\n\n\n
David Game responds:<\/em><\/h5>\n\n\n\n

We know that using AI as an answer delivery solution has the most corrosive effects on persistent learning. Therefore, AI activities should challenge students to bring knowledge to the activity. Any activities which require students to deconstruct, challenge, or reframe AI output would develop the grit and persistence that accompanies deep learning, which could be valuable. <\/p>\n\n\n\n

Dr Philip Xiu responds:<\/em><\/h5>\n\n\n\n

Grit and persistence are exactly the same qualities that can get eroded when students become overly dependent on AI giving them instant answers. This is part of the mis-skilling that David was referring to in the webinar.  <\/p>\n\n\n\n

In the teaching activity sense, the AI could be used as a Socratic method of discourse with the student. In this activity, students are presented with a complex case, they work through it independently without the use of any AI tools and commit to a diagnosis and management plan in writing. Then they run the same case through an AI tool. <\/p>\n\n\n\n

Their task is not to compare the notes passively but to identify where they disagree with AI and why and to defend their reasoning process. The aim is to not defer to the AI without justification, while also not dismissing it without evidence. <\/p>\n\n\n\n

This discomfort of sitting with that disagreement and not immediately knowing who is right and who is wrong is precisely the thing that builds clinical persistence. There is always going to be uncertainty within medicine but students can learn that good clinical reasoning is about defending your position under uncertainty in shades of gray. That habit of mind will serve them long after any particular AI tool is superseded. <\/p>\n\n\n\n

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\"AI activities should challenge students to bring knowledge to the activity. Any activities which require students to deconstruct, challenge or reframe AI output would develop the grit and persistence that accompanies deep learning.\"<\/h2>\n\n\n\n

David Game, SVP<\/strong> in Medical Education Partnerships, Elsevier <\/strong>| Elsevier Faculty Hub, August 2026<\/p>\n<\/section>\n\n\n\n

Q2: In my understanding, AI is a valuable tool that can support healthcare professionals in solving clinical problems and improving efficiency in day-to-day practice. However, many senior clinicians and academics appear reluctant to adopt or utilize AI in their practice and teaching. What would you recommend to address this resistance and promote the responsible integration of AI into healthcare and medical education?<\/h2>\n\n\n\n
Dr Philip Xiu responds:<\/em><\/h5>\n\n\n\n

We do see a lot of resistance from senior clinicians and academics, and this is a rational response in the face of such novel technology. The reason is the fact that they’ve spent decades honing their own global judgement with evidence-based medicine. They look at an AI tool that produces a recommendation without a clear explanation of why, and their understandable skepticism is simply good medicine.  <\/p>\n\n\n\n

I think we need to stop seeing this resistance as a problem to be overcome, and instead treat it as a prompt for better education and understanding. <\/p>\n\n\n\n

With any new technology, one of the single most powerful leaders is a clinical champion. This is not an external technology enthusiast from the outside but a peer within the specialty who can say, \"I’ve used this and here’s what it did for my patients and here is where I can still override it.\" This kind of social credibility is irreplaceable. As technology has moved on (for example, electronic prescribing, electronic health records, and clinical decision-making support), I’ve seen the teach-back effect in place. This is where senior clinicians are teaching trainees and engaging with the technology, which can deepen their own trust considerably. And when senior clinicians do that with AI (i.e., training trainees how to critically evaluate, spot bias, and maintain independent reasoning), it reframes AI from a threat to their own clinical authority into an extension of their own teaching role. <\/p>\n\n\n\n

Within the medical education curriculum, there is clear evidence that AI education should not be siloed into a standalone isolated module. It should be woven within clinical-decision making and reasoning, medical ethics, straight from the outset. Medicine should involve AI-free reasoning so they are not in actuality producing medical graduates who cannot function without AI support. Just as hands-free cruise control is available to support drivers, the drivers should still need to pass their test without it.<\/p>\n\n\n\n

The end goal of all of this is so that we have graduates who do not trust AI blindly, but are confident and critical users of its outputs. <\/p>\n\n\n\n

Q3: I manage a journal club about pain in my university. Seeing how hard it is already to integrate the human aspect of the biopsychosocial model into conversations when students are taught to think in a biomedical way, how do you recommend introducing a critical conversation about AI without being ignored by students or labelled as the \u2018fun police\u2019?<\/h2>\n\n\n\n

Dr Philip Xiu responds:<\/em><\/h5>\n\n\n\n

This is such an interesting question. Some universities teach students to take history in the biopsychosocial medical model, and some focus more on the biomedical component of it. With a clinical history taken, having a holistic approach to the patient involved in a biopsychosocial approach can give much deeper rapport with the individual longitudinally as opposed to just a purely biomedical approach. And obviously, as you know, the psychosocial impact has as much, if not more, importance compared to the biological side of any medical condition, not least in management and planning discussions with their patients if they have their own health beliefs.  <\/p>\n\n\n\n

One way to introduce a conversation about AI is to put them in the position where they are consulting with an elderly individual with vague medical components, but whose overall problem relates to the psychosocial aspect of environmental health, low mood \u2013 given the context of recent bereavements \u2013 and financial uncertainties. Perhaps ask them to put the case into an AI tool of their choice and bring the relevant outputs to the session. <\/p>\n\n\n\n

It\u2019s possible the AI outputs will all converge into similar thought processes, which will be competent but perhaps clinically relatively thin. The aim is to put the focus back on the individual patient themselves, the impact on their life, their own concerns, and their own expectations. But you don’t need to necessarily point that out. You can just ask what is missing from what is being presented by the AI tools. And in that moment, hopefully the students will find it themselves.  <\/p>\n\n\n\n

What healthcare really requires of them is to recognize that the conversation belongs to the patient – it\u2019s about the individual in front of them, not the AI output. Having that real-life discussion and sitting with shared uncertainty with patients, who are individuals with complex psychosocial overlay, is a more effective approach than deferring to AI.<\/p>\n\n\n\n

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\"[Senior clinicians and academics] look at an AI tool that produces a recommendation without a clear explanation of why, and their understandable skepticism is simply good medicine. I think we need to stop seeing this resistance as a problem to be overcome, and instead treat it as a prompt for better education and understanding.\"<\/h2>\n\n\n\n

Dr Philip Xiu, Hon. Senior Lecturer, University of Leeds <\/strong>| Elsevier Faculty Hub, August 2026<\/p>\n<\/section>\n\n\n\n

Q4: How can we manage public reliance on AI and people increasingly consulting with AI on health concerns instead of visiting a doctor?<\/h2>\n\n\n\n
Dr Philip Xiu responds:<\/em><\/h5>\n\n\n\n

As shown from David’s data during the webinar, we are seeing that self-help and companionship are among the most common ways people are using AI. <\/p>\n\n\n\n

I think it’s actually impossible to turn back the tide on the general public utilizing and consulting AI. What we can do is help the general public become more sophisticated consumers of AI health information and teach them to treat AI outputs the way that they might treat a knowledgeable friend’s advice. It is useful as a starting point but not a substitute for professional assessment. This really is a public health role in terms of AI health literacy and deserves a place in any health communication.  <\/p>\n\n\n\n

One of the reasons why people are turning to AI is because accessibility to healthcare advice, such as GP appointments, is genuinely difficult. If we want people to consult healthcare professionals rather than chatbots, then clinicians need to be equally accessible, which is currently impossible. The AI is partly filling a vacuum that healthcare systems have created. So far, this is a healthcare policy and resourcing problem as much as a technology problem. <\/p>\n\n\n\n

In the UK, we already carry out healthcare screening programs \u2013 such as breast cancer, cervical and bowel cancer screening \u2013  using physical aids. One wonders whether, in the future, there will be AI symptom screeners that can prompt people who would have otherwise ignored their symptoms to seek care earlier. This is because the risk of under-consultation among under-represented groups is, in many of the diverse populations, just as serious as over-reliance on the healthcare system. I wonder whether the AI can then serve as a genuine public health function. <\/p>\n\n\n\n

Q5: Given that large language model (LLM)-based chatbots such as Copilot, Gemini and ChatGPT are constantly ingesting and learning from data, how can we guarantee data security and confidentiality when using these tools?<\/h2>\n\n\n\n

Dr Ximena Alvira responds: <\/em><\/h5>\n\n\n\n

Honestly, with general-purpose standalone chatbots you cannot fully guarantee it. Copilot, Gemini and ChatGPT may retain and learn from what you submit, so entering identifiable patient information risks breaching confidentiality and data-protection law no matter how secure the interface feels. <\/p>\n\n\n\n

My rules of thumb:  <\/p>\n\n\n\n