The ESC Congress 2026 will be held from August 28 to 31 in Munich. For four days, researchers, doctors and specialists will present the latest advances in cardiology.
And this year, it's hard to ignore artificial intelligence.
ECG analysis, medical imaging, risk prediction, diagnostic assistance or automation of certain tasks: AI is starting to find its place in many areas of cardiology.
The European Society of Cardiology even made it one of the major themes of this year's conference. The idea is no longer just to showcase impressive algorithms, but to see which ones can actually be useful to doctors and patients.
Why is AI playing such a prominent role at ESC 2026?
Artificial intelligence has been tested for several years now to analyze ECGs, interpret medical images, and exploit the numerous data contained in patient records.
But now we come to a slightly more interesting stage: that of putting it into practice.
Obtaining good results in a study is one thing. Integrating a tool into the daily practice of a cardiologist, with real patients and all the constraints that come with it, is quite another.
The ESC 2026 program focuses in particular on ECG interpretation, image analysis, and cardiovascular risk prediction. In other words, areas where physicians sometimes have to examine a lot of data to spot an abnormality or identify patients who require closer monitoring.
The benefit of AI is quite simple to understand: to draw attention more quickly to certain signals and help the doctor make a decision. Not to decide for them.
Tools that are starting to leave the labs
This is precisely one of the challenges of this edition: to see how these technologies behave when they are taken out of demonstrations and studies and integrated into real care pathways.
The tools in question can take very different forms.
Some automatically analyze an ECG or a medical image. Others seek to detect abnormalities from data collected by connected devices. We are also seeing the emergence of digital stethoscopes capable of using algorithms to locate certain heart sounds that are difficult to identify by ear.
This last example is no longer futuristic. Studies published in 2026 already show that this type of AI-assisted stethoscope can improve the detection of certain valvular heart diseases compared to traditional auscultation.
Another interesting avenue concerns remote monitoring. With a watch, a sensor, or another connected device, it becomes possible to continuously collect certain data and then have it analyzed automatically.
The goal is not to replace medical follow-up, obviously, but possibly to detect more quickly an anomaly that would have gone unnoticed between two consultations.
Demonstrations to see AI in action
ESC does not intend to only talk about it in conferences.
The congress also features practical sessions, live demonstrations, and various formats to show how these tools actually work.
And that's probably one of the most interesting aspects of the subject. A presentation full of graphs and statistics can quickly become abstract. Watching an AI analyze an ECG or a medical image in real time makes it much easier to understand what it can do… and sometimes also what it can't do.
The use of AI in healthcare is not limited to tools designed for doctors. For the general public as well, assistants are starting to proliferate. For example, one can... test greenwhey's health chatbot to ask common questions and access various resources.
And then there are all the much less spectacular uses.
Coding of certain procedures, organization of appointments, processing of documents or automation of part of the administrative tasks: this is probably not what will make the most impressive demonstrations of the congress, but it can save valuable time for medical teams.
But medical AI cannot simply be "installed".
Obviously, using AI to recommend a series on Netflix and using it to guide a medical decision are not quite the same thing.
The first question is that of reliability.
An algorithm may function correctly with the data used for its development but become less efficient when it encounters a different population, a different device, or simply different data. Therefore, it is essential to continue monitoring its performance after deployment.
There is also the question of explanation. If an AI considers that a patient presents a significant risk, the doctor must have enough information to understand the result and decide what to do about it.
In this context, it is impossible to be satisfied with a simple "the computer said that".
And naturally, regulations are following suit. The European AI Act is gradually imposing a framework on different artificial intelligence systems according to their level of risk, with requirements concerning, in particular, data, traceability, human oversight, and system robustness.
Some transparency obligations already came into effect in August 2026, while the rules concerning systems considered high risk follow a specific timetable.
All of this will also change how doctors are trained. Knowing how to use AI will be useful, but knowing how to identify its limitations will probably be even more so.
And after 2026?
Another avenue that should continue to be discussed: digital twins.
The principle is to create a virtual model of a patient from various medical data in order to simulate, for example, the progression of a disease or the response to a treatment.
We are still far from a standard tool found in every hospital. But in the long run, this type of approach could help personalize certain treatments or better anticipate their effects.
Until then, ESC 2026 will mainly allow us to see where artificial intelligence really stands in cardiology.
Because between an impressive demonstration at a conference and a tool that a cardiologist can rely on every day, there are still a few steps to go.
And it is precisely this transition from the lab to practice that will be interesting to follow in Munich.
Thank you for this clear and nuanced article! It's exactly the right angle: AI has real potential to assist cardiologists in triage and analysis (ECG, imaging, stethoscopes), but the transition from the lab to daily practice raises major challenges in terms of reliability, regulation, and training. I'm eager to see what concrete outcomes will emerge from ESC 2026.