arXiv:2607.00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost. We show that this blind spot misranks models: across 11 architectures, models with comparable pointwise error diverge by up to 53{\deg} in phase accuracy, equivalent to roughly 123 ms for a 1.2 Hz cardiac rhythm and invisible to standard metrics. To enable development of models that escap
The proliferation of AI in health-signal interpretation necessitates more robust evaluation frameworks to ensure fidelity and accurate medical decision-making.
Improving the fidelity of AI models for health signals is critical for the reliable development and deployment of digital twins in healthcare, impacting diagnostic accuracy and patient outcomes.
The proposed 'Timesynth' framework introduces a new standard for benchmarking health-signal AI, moving beyond inadequate pointwise metrics to assess fundamental physiological properties.
- · AI developers in healthcare
- · Medical device manufacturers
- · Digital health platforms
- · Patients receiving AI-assisted care
- · AI models relying solely on pointwise metrics
- · Healthcare providers using unreliable AI diagnostics
- · Companies with less sophisticated AI validation methods
More accurate and reliable AI models for physiological signal analysis will emerge.
Accelerated adoption of AI digital twins in clinical settings due to increased trust and accuracy.
Personalized medicine strategies become more effective and widespread, driven by high-fidelity AI signal analysis.
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Read at arXiv cs.LG