Long Short-Term Memory–GPT-4 Integration for Interpretable Biomedical Signal Classification: Proof-of-Concept Study

BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in remote and resource-constrained settings. Limited access to specialists and the complexity of biomedical signal interpretation (eg, ECG and EEG) contribute to delays in recognizing cardiovascular and neurological conditions. OBJECTIVE: To develop and evaluate a technical framework integrating Long Short-Term Memory (LSTM) networks with GPT-4 to provide automated biomedical signal classification and human-readable interpretations, suitable as a foundation for future deployment in resource-constrained environments. METHODS: The two-layer LSTM architecture (128→64 units) was selected based on preliminary experiments comparing configurations ranging from single-layer networks (64, 128 units) to deeper architectures (128→64→32 units). The chosen configuration balanced model capacity against overfitting risk and computational efficiency. Preliminary validation showed that deeper networks (≥3 layers) provided negligible performance gains (