Limitations

Risks & contraindications

Evidence quality

Highlighted claims

• The experiments were simulations rather than live deployments in hospital networks. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
• The backdoor proxy does not cover every kind of memorized patient information, distributional influence, or privacy leakage. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
• The study does not establish performance on larger modern medical imaging architectures or multimodal clinical systems. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
• The current input embedding does not explicitly model multi-granularity temporal shifts or frequency-specific features. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
• The augmentation function Phi is insufficiently specified, which may affect reproducibility and domain transfer. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
• The disentanglement validation may not capture the complexity of real physiological signals. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
• Theoretical justifications rely on assumptions that are not direct clinical validation of tokens as biomarkers. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
• The embedding may be less sensitive for pathologies requiring micro-structure analysis. — Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization