Artificial intelligence (AI) has ceased to be a laboratory promise to become an essential element of the hospital environment. Aware of this, the European University has launched an ambitious educational program to integrate AI in medical training from the first moment. The next course, the degree of medicine will include a specific subject on clinical decisions assisted by AI, and the institution has also announced a new master’s degree in sanitary artificial intelligence, in addition to reinforcing its ‘online’ transverse module for all health science degrees.
“The AI is no longer optional: it must be validated at the Royal Hospital, not in the laboratory, and the university has the obligation to prepare those who will implement it,” says Juan José Beunza, Professor of Public Health and director of the IA-Salud program of the European University.
The strategic plan of the academic center goes beyond imparting content: seeks to completely transform the clinical approach. The objective is to train all health professionals as competent users of AI tools and, at the same time, train a small group as developers and validators, capable of adapting technological solutions to the real hospital needs.
Future health professionals will have to live with algorithms, sensors and virtual assistants. The European University is clear: either they are formed or will not be prepared for a clinical reality that has already begun to change
This approach is based on automating the complete cycle of medical data: from the monitoring and early detection of alterations to the generation of alerts and the decision -making assistance. “The technological rhythm gives vertigo; if the university does not react with the same speed, the AI will never really land in the consultation,” Beunza warns.
Technology that is tested with patients, not on paper
The European University has woven a network of collaboration with biomedical Madrid and Start-up hospitals to create “living units” where technologies are tested in real conditions. Among the projects underway, the portable sensors for chronic patients, ‘Machine Learning’ platforms to prioritize radiological images or clinical chatbots that speed up the triage in the emergency room stand out.
“The technology industry can bring the solution, but if we do not validate it in the plant with clinicians, engineers and teachers at the same time, it will never reach the patient,” says Beunza, who insists on the need to generate scientific evidence in real time.
Evaluations with simulated cases and synthetic data
The training proposal also changes the way to evaluate students. The new academic plans incorporate dynamics based on simulated clinical cases, synthetic data and interactive exercises with AI algorithms. In addition, each postgraduate degree includes a “pill” of express update on the tools that are reaching the market.
In parallel, the institution continues to bet on personalized continuous training, with virtual agents that will allow health professionals to update their “letter” knowledge, according to the specific needs of each service.
