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.
Comment reconnaître une attaque de phishing et s’en protéger Le phishing ou « hameçonnage »…
Qu’est-ce que la cybersécurité ? Définition, enjeux et bonnes pratiques en 2025 La cybersécurité est…
Cybersécurité : les établissements de santé renforcent leur défense grâce aux exercices de crise Face…
L'IA : opportunité ou menace ? Les DSI de la finance s'interrogent Alors que l'intelligence…
Sécurité des identités : un pilier essentiel pour la conformité au règlement DORA dans le…
La transformation numérique du secteur financier n'a pas que du bon : elle augmente aussi…
This website uses cookies.