blackbox ai
Artificial intelligence models that interpret medical images hold the promise to enhance clinicians’ ability to make accurate and timely diagnoses, while also lessening workload by allowing busy physicians to focus on critical cases and delegate rote tasks to AI.
But AI models that lack transparency about how and why a diagnosis is made can be problematic. This opaque reasoning — also known “black box” AI — can diminish clinician trust in the reliability of the AI tool and thus discourage its use. This lack of transparency could also mislead clinicians into overtrusting the tool’s interpretation.
In the realm of medical imaging, one way to create more understandable AI models and to demystify AI decision-making have been saliency assessments — an approach that uses heat maps to pinpoint whether the tool is correctly focusing only on the relevant pieces of a given image or homing in on irrelevant parts of it.
Sécurité des mots de passe : bonnes pratiques pour éviter les failles La sécurité des…
Ransomware : comment prévenir et réagir face à une attaque Le ransomware est l’une des…
Cybersécurité et e-commerce : protéger vos clients et vos ventes En 2025, les sites e-commerce…
Les ransomwares : comprendre et se défendre contre cette menace En 2025, les ransomwares représentent…
RGPD et cybersécurité : comment rester conforme en 2025 Depuis sa mise en application en…
VPN : un outil indispensable pour protéger vos données Le VPN, ou « Virtual Private…
This website uses cookies.