arXiv:2512.11095v2 Announce Type: replace Abstract: Label ambiguity is an inherent and largely unaddressed challenge in real-world electrocardiogram (ECG) diagnosis, arising from overlapping conditions and diagnostic disagreements. However, current ECG models are trained assuming clean and non-ambiguous annotations, limiting both the development and meaningful evaluation of models under real-world conditions. Although Partial Label Learning (PLL) frameworks are designed to learn from ambiguous labels, their effectiveness in medical time-series domains, ECG in particular, remains largely undere
The increasing deployment of AI in sensitive domains like healthcare mandates robust solutions for real-world data ambiguities, like those inherent in ECG diagnostics.
This development can significantly improve the accuracy and trustworthiness of AI models in medical diagnosis, expanding their practical applicability and reducing diagnostic errors.
AI models can now be trained more effectively on real-world medical data with inherent ambiguities, moving beyond idealized clean datasets and improving clinical relevance.
- · Medical AI developers
- · Healthcare providers
- · Patients with complex conditions
- · AI models relying solely on clean data
Improved diagnostic accuracy in AI-assisted cardiology.
Faster adoption and regulatory approval for AI in broader medical diagnosis, especially in areas with subjective interpretations.
Enhanced trust in AI systems could lead to a shift in diagnostic workflows, augmenting or potentially replacing some human diagnostic tasks.
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Read at arXiv cs.LG