Edge-AI enabled secure IoT framework for real-time patient monitoring and anomaly detection in smart healthcare systems

Hospitals desire knowledge of bedside sensors in real time but they do not wish to send everything to the cloud. We propose an Edge-AI framework used in the IoT environment that maintains intelligence as near as possible to the patient, and ensures privacy and a trusted audit trail. A quantized CNN-LSTM is run on-device to detect anomalies and protects data in transit and at rest (TLS plus lightweight homomorphic encryption), logs notable events to a private block chain to give an auditable, tamper-proof history, and improves models off-device using federated learning to ensure that raw patient data never reaches the cloud. Reply The framework was tested against a cloud-only baseline on the MIT-BIH Arrhythmia dataset and a live multi-sensor synthetic stream, beating it across all metrics: accuracy 94.7% versus 83.1% accuracy, median inference latency 118 ms versus 246 ms (a 52% reduction), daily communication overhead 36.2 MB versus 56.3 MB (a savings of 38.1%), and energy usage 1.21 mW/sample (near 22% energy efficiency improvement). Block chain logging endured 75 events/s, which helped medico-legal tractability. Federated rounds provided ~ 1.5–2.3 accuracy points per round across five rounds, whereas INT8 quantization reduced model size by ~ 74% with only a ~ 0.4 accuracy degrade-practical in Jetson-class edge devices. SHAP explanations accompany alerts to enable the establishment of clinical trust in the model by demonstrating why a case was flagged. In general, the framework provides a safe, decipherable, and standard-aligned course to hospital-scale implementation (> 1,000 nodes) with quantifiable gains in responsiveness and bandwidth as well as power.