arXiv:2509.05238v2 Announce Type: replace-cross Abstract: Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times. Yet, the numerical uncertainty inherent to DL training remains largely underexplored despite its potential to significantly impact the reliability of model outcomes. We show that training the FastSurfer segmentation model introduces substantial numerical uncertainty that exceeds its non-DL counterpart (FreeSurfer 7.3.2) in cortical regions, potentially impacting downstream clinical results. We also characterize this
The increasing deployment of deep learning models in sensitive applications like neuroimaging makes understanding their inherent uncertainties critical for practical adoption and reliability.
Sophisticated readers should care because unaddressed numerical uncertainty in AI models can undermine decision-making, particularly in critical sectors like healthcare, impacting trust and adoption.
This research highlights that while AI can improve efficiency, its outputs may carry greater numerical uncertainty than traditional methods, necessitating new approaches to validation and risk assessment.
- · AI Validation & Assurance Services
- · Robust AI Development Teams
- · Regulatory Bodies
- · AI Models with High Undocumented Variability
- · Healthcare Providers Relying Solely on Black-Box AI
- · Early Adopters of Untested AI Solutions
The finding exposes a tangible area of risk for current and future AI applications, particularly in medical diagnostics.
Increased scrutiny and demand for uncertainty quantification techniques will emerge, influencing AI development and deployment standards.
New certification processes and regulatory frameworks for AI systems in critical domains may become necessary, impacting market entry and operational costs.
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Read at arXiv cs.AI