arXiv:2606.15910v1 Announce Type: new Abstract: A vision-language model can answer a question about a medical image fluently and confidently while barely using the image, leaning instead on language priors. In medicine this is the failure that matters most, because the answer looks trustworthy and is not, and the only protection is a confidence score reliable enough to tell the system when to abstain. We ask a deployment question rather than an accuracy one: how much imaging work a model can safely handle alone, and which confidence signal makes that possible. We evaluate seven confidence esti
As AI models become more ubiquitous and powerful, the critical need for reliable confidence estimation, especially in high-stakes fields like medicine, is becoming evident to prevent dangerous over-reliance.
This research addresses a core safety and trust challenge for AI deployment in sensitive areas, highlighting that accuracy alone is insufficient without robust methods to identify and manage model uncertainty.
The focus shifts from solely maximizing AI accuracy to ensuring calibrated confidence and safe abstention, fundamentally altering how AI systems will be designed, evaluated, and regulated for practical use.
- · AI safety researchers
- · Healthcare AI developers
- · Patients
- · Regulators
- · Developers of uncalibrated AI
- · Trust-based AI deployment strategies
Increased investment in confidence estimation and explainability for AI models in critical applications.
New regulatory frameworks and certification processes for AI requiring proof of robust abstention capabilities.
The development of 'AI guardians' or 'triage systems' specifically designed to monitor and manage the outputs of other AI for safety.
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Read at arXiv cs.CL