Elsevier Medical Education presents an interview with Dr Ximena Alvira, Clinical & Research Manager at Elsevier Health, exploring how AI is reshaping health education and clinical practice, and why developing critical thinking in future clinicians is more urgent than ever.
How can AI help build the bridge between education and clinical practice, and better prepare future clinicians?
Building virtual AI scenarios that reflect real patients
I want to highlight two aspects here. One of them would be the creation of virtual scenarios, either through AI virtual patients or simply through scenario design that emulates the challenging situations students are going to face as clinicians. As students, we learn from ideal scenarios, and we prepare for tests based on ideal scenarios, but these rarely incorporate the realities of patients’ lives. By real life, I mean patients with various personal, emotional, financial, biological, psychological, geographical, and ethical determinants that really impact how they get better, become healthier, and recover from a specific condition.
So, I think creating these virtual AI scenarios is going to be really important and interesting because they can draw on evidence from an enormous corpus of data from around the world, allowing us to curate truly diverse settings that reflect real-world clinical practice.
Critical thinking: how AI can sharpen or erode it
The second aspect where I think AI can help bridge the gap between education and clinical practice is critical thinking. However, its impact can go in either direction: it can strengthen critical thinking or, if used incorrectly, gradually weaken it. AI can help guide us through the clinical reasoning process that leads to a particular decision, and it can highlight the potential benefits and drawbacks of each option. But then there is the risk that we start to overly rely on AI and forget about our critical thinking. So, hopefully it will be the first outcome.
"Exercising and fostering critical thinking needs to become a renewed priority among students because, although this skill has always been important, it must be prioritized further to avoid overreliance on AI."
Dr Ximena Alvira, Clinical & Research Manager, Elsevier | Elsevier Faculty Hub, June 2026
What core competencies related to the use of AI should students have when entering the clinical environment?
Fostering critical thinking against over-reliance on AI
As I mentioned before, critical thinking can be enhanced by AI because it guides us through reasoning processes before we apply them to patient care, but this can also be used in the wrong way. We could become overly reliant on AI and forget that we are ultimately responsible and accountable for everything we do for our patients and within the healthcare settings in which we work.
Fostering critical thinking is of utmost importance and is becoming increasingly important because AI can inspire such confidence and trust that we rarely question it or consider that it might simply be wrong. Exercising and fostering critical thinking needs to become a renewed priority among students because, although this skill has always been there, it needs to be prioritized even further to avoid overreliance on AI.
AI literacy as a non-negotiable core competency
Secondly, I believe that AI literacy is a core competency. Understanding its limitations and challenges, what different AI models are being used in healthcare, and which ones have proven to be safer for our patients, among other things, is essential.
Having a strong level of AI literacy is no longer negotiable, and it should be incorporated very early into our education and careers because these are the tools that students are going to use once they enter clinical practice. So, I would say that these are the two main core competencies, but of course there are many others, such as legal aspects and privacy issues, which can be grouped under these two broader competencies.
From a broader perspective, how is AI currently being implemented and used in hospitals by residents and clinicians?
Two decades of AI in clinical decision support
AI has been used in hospitals for many years now, approximately more than two decades. So, it’s nothing new. It is used for clinical decision support by retrieving information in a fast, efficient, and safe way. It is also used to support diagnosis across a range of specialties, including Radiology, for example, as well as in the early detection of conditions such as diabetic retinopathy and stroke. These are predictive models that use machine learning to ultimately improve patient care.
The next step: truly personalized patient care
I would say that the latest development has been the integration of real-world patient data with already established and validated AI models built on evidence-based research, combining these two sources of information to propose a care approach that is highly personalized to a specific patient, context, and scenario. That has the potential to become the ultimate delivery of personalized care, which I am very much looking forward to seeing in practice.
So yes, AI is being widely used. We know that large language models are also being used, albeit not always safely, for information retrieval as well as diagnostic support. AI tools are becoming great allies for healthcare professionals, supporting them in their daily work.
How can medical education ensure continuity between how AI is taught during training and how it is actually used in clinical practice?
Aligning training with real clinical workflows
I can think of several ways that medical education can ensure continuity here. One of the most important is aligning training with the real clinical workflows that healthcare providers are going to encounter during their clinical training and professional development.
This is one of the most important areas because I still see a gap between what we learn in medical school and what we are forced to learn in a very fast, and sometimes painful, way when we begin practicing as clinicians. So, ensuring that continuity is essential because we will be using similar, or even the same, tools in clinical practice, and this helps ensure that we do not experience that gap.
Teaching students to evaluate AI tools critically
We should also be fostering critical thinking, as I previously mentioned. Not necessarily critical thinking about the AI tools themselves, because they will continue to evolve so quickly that we may not be able to keep up, but rather about the skills students will need as clinicians to recognize when an AI-generated output is worth applying and when it should be disregarded.
Students need to be taught how to evaluate these AI tools, almost using a Health Technology Assessment approach. This is what they will face when they move into clinical practice, and whether they make the right or wrong choice will depend on how well they can evaluate the associated risks and benefits.
For me, critical thinking is about ensuring continuity and early exposure throughout a student’s education to the AI tools that clinicians will be using at a professional level.
"Students need to know not only what AI tools are being used in clinical practice and how, but also what their challenges are and what the ethical and accountability implications behind them may be."
Dr Ximena Alvira, Clinical & Research Manager, Elsevier | Elsevier Faculty Hub, June 2026
In your opinion, what are the main challenges or gaps that still exist in integrating AI effectively across the continuum from education to clinical care?
The gap between training and clinical reality
I would say there are two very important challenges and gaps. One is the gap between training and practice. Most medical students are being taught about AI, but they are not being taught how AI is implemented in hospitals or clinical practice.
So, there is this gap where students think the use of AI tools is mainly about answering test questions or essays. However, when they enter clinical practice, they see that these tools are being used for diagnostic support, image recognition, and clinical decision support. Students need to know not only what AI tools are being used and how, but also what their challenges are and what the ethical and accountability implications behind them are.
AI adoption is outpacing policy and accountability
This brings me to the second problem, which is that AI tools are being adopted faster than policies and governance frameworks can be developed. There needs to be a high degree of accountability before we use AI tools, but once we know which tools should be used, we can better teach how they should be applied in practice.
It is not about teaching the technology behind the tools, but rather teaching what their limitations are, what their appropriate uses are, when they are being used effectively, when they are being used poorly, what the challenges are, and what the consequences can be.
To give a concrete example, students learn from books and other sources that we trust. But when we are faced with AI tools in clinical practice, what are we going to trust, and how are we going to evaluate them? So, for me, one of the existing gaps is understanding how to evaluate and trust the information we receive.
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