arXiv:2508.04457v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains underexplored. In this study, we provide an extensive uncertainty quantification benchmark for multi-lab
The increasing deployment of AI in sensitive fields like healthcare necessitates robust uncertainty quantification to build trust and ensure reliability.
Reliable uncertainty quantification is critical for the safe and ethical deployment of AI in medical imaging, impacting diagnostic accuracy and patient outcomes.
This research provides a benchmark for understanding and disentangling different types of AI model uncertainty in real-world medical diagnosis, moving beyond synthetic data.
- · Healthcare AI developers
- · Medical imaging software companies
- · Patients receiving AI-assisted diagnoses
- · AI models lacking robust uncertainty quantification
- · Healthcare providers relying on black-box AI
Increased trust and adoption of AI in medical diagnosis due to better understanding of model limitations.
Development of new regulatory frameworks and certification processes for AI in medical imaging that incorporate uncertainty metrics.
Shift in medical liability and accountability models as AI decision-making becomes more transparent about its confidence levels.
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Read at arXiv cs.LG