Olasz egyetemi kutatók és orvosok egy csoportja kódolási tudás nélkül, kizárólag természetes nyelvi utasításokkal hozott létre egy működő lélegeztetőgép-szimulátort. Az Open Vent Sim nevű webes alkalmazás a fizikai oktatóeszközök hiányát hivatott pótolni az orvosképzésben. Az első működő prototípust Google AI Studio környezetben, a Gemini 2.5 Pro modell segítségével mindössze 20 óra alatt fejlesztették ki, majd újabb 20 órányi klinikai finomhangolást végeztek rajta.
A fejlesztés során az orvosok képernyőképeket és valós kórházi berendezések grafikonjait is megosztották a modellel, a hibás hullámformákat pedig szöveges visszajelzésekkel javíttatták ki. A létrejött React TypeScript kód ugyan működőképes volt, de szerkezetileg rendezetlen és egyetlen, több mint 2300 soros fájlból állt. A hosszú távú karbantarthatóság érdekében a Masaryk Egyetem szoftverfejlesztői 15 órányi munkával refaktorálták a rendszert, modularizálták a komponenst és letisztázták az architektúrát.
A véglegesített alkalmazás Google Cloud Run infrastruktúrán keresztül lett elérhető a hallgatók számára, így tetszőleges böngészőből, helyi telepítés nélkül használható. A szimulátorban különféle tüdőbetegségek (például ARDS vagy COPD) paraméterei és a gázáramlási dinamikák is beállíthatók, ami a drága fizikai berendezések hiányában is segíti a gyakorlati oktatást.
Az eredeti szöveg (Bioengineer.org)
Clinicians Use Natural Language Artificial Intelligence to Develop Mechanical Ventilation Simulator
A multidisciplinary team of medical educators and technical researchers has demonstrated that clinicians can design and implement a functional mechanical ventilation simulator by directing generative artificial intelligence tools through natural language prompts. Reported as an innovation development study conducted across two Italian simulation centers at the University of Eastern Piedmont and the University of Trieste, the initiative produced Open Vent Sim, an open-source web application designed to support medical training where specialized physical equipment is scarce. The project addressed a recognized clinician-developer gap, wherein healthcare instructors understand clinical learning objectives but lack the software programming skills required to build or adapt digital training tools, leaving institutions dependent on costly, closed-architecture commercial platforms.
The study sought to evaluate the feasibility, physiological fidelity, and usability of an educational simulator developed through natural language-driven development, a paradigm where software is built via conversational prompting rather than traditional manual coding. Conducted between February and June 2025, the effort focused specifically on overcoming local institutional bottlenecks, notably the absence of physical anesthesia workstations and the restriction of having access to only a single-unit commercial ventilation program. Rather than assessing learner knowledge gains or serving as a clinical trial of educational efficacy, the study was structured as a proof-of-concept innovation evaluation wherein the primary unit of assessment was the software itself, its underlying physiological algorithms, its interface, and its operational usability.
The primary development platform utilized for initial prototyping was Google AI Studio, which offered a synchronized three-panel interface comprising a conversational natural-language chat console, a live code editor, and an immediate application preview display. The core development group included a lead anesthesiology and intensive care physician, three anesthesiologists, ten anesthesiology and intensive care residents who provided iterative feedback, one intensive care research nurse, and one biomedical engineer. Software engineering collaborators at Masaryk University in the Czech Republic subsequently joined the team to conduct code reviews, architectural analyses, and code optimization using standard TypeScript environments and repository management tools.
During the initial development phase, the clinical lead utilized Google AI Studio along with the Gemini 2.5 Pro model to generate React TypeScript code exclusively through natural language prompts, tracking changes via an integrated version-control plugin. The process initiated with a baseline instruction asking the model to generate a new web application for a ventilator simulator with fundamental controls and waveform displays. Development then proceeded through structured cycles consisting of clinical requirement articulation, artificial intelligence interpretation, code generation, immediate browser previewing, and clinical review. Multimodal inputs, such as uploaded graphical traces and display screenshots from real hospital equipment, were provided to assist the model in replicating real-world diagnostic patterns.
Iterative clinical feedback played a vital role in refining physiological accuracy beyond the initial code generations. In one detailed development sequence involving Pressure Support Ventilation, resident reviewers observed that the AI-generated tracings lacked the subtle negative flow deflection that clinicians expect to see when a patient makes an inspiratory effort to trigger a mechanical breath. The clinical team provided specific quantitative feedback, instructing the model that the triggering deflection needed to drop approximatel
