Comment un algorithme d'apprentissage profond peut-il améliorer la détection des fractures sur les rayons X aux urgences ?

As evidências já sugerem que a IA pode melhorar significativamente o desempenho diagnóstico e, ao mesmo tempo, reduzir a carga de trabalho. À medida que os algoritmos aprendem com dados mais diversificados dos pacientes, espera-se que a precisão atinja um valor preditivo negativo quase perfeito, permitindo que os médicos se concentrem em julgamentos complexos que as máquinas não conseguem replicar.

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Europe’s hospitals are ageing along with their citizens. The share of people over 65 has already reached 21% and is forecast to approach 30% by 2050 [1]. Chronic diseases rise in tandem, while staff shortages deepen. Health-care professionals spend more than half of the time they devote to patient care working inside electronic health records rather than with patients [1].

AI, the study argues, is no longer optional. Even “low-hanging-fruit” applications—triage queues, smart worklists, ambient clinical scribes—can reclaim hours, cut burnout, and widen access to expertise beyond city hospitals [1]. Yet uptake crawls, hamstrung by fragmented rules, outdated servers and, above all, mistrust.

Those obstacles are precisely what Brussels has started to clear.

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The MHRA’s July 2025 “international reliance” pathway lets FDA‑, Health Canada‑ and TGA‑authorised devices, especially AI radiology software, enter Great Britain without repeat assessment, while reserving full UKCA review for first‑in‑class algorithms. This article unpacks each legal change, the expanded AI Airlock sandbox and indefinite CE‑mark recognition, outlining their practical impact on hospitals, developers and policymakers across diagnostic imaging.