Cybersecurity in Telemedicine: A Multi-Layered Framework with Quantum Encryption and AI-Based Intrusion Detection

Telemedicine has changed the face of healthcare delivery by allowing consultations to be conducted remotely and monitoring of patients to be done around the clock. Nevertheless, this online revolution makes confidential medical information susceptible to advanced hacking attacks. As a measure against these risks, a multi-layered security model is suggested that incorporates the use of quantum encryption to enable secure communication, the use of CNN-LSTM to detect intrusions, a context-sensitive access control system based on dynamic trust scores, and a layered homomorphic encrypted cloud storage. The telehealth network was simulated to test the framework. The results indicate a threat detection rate of 98.6%, data integrity of 99.3% and a reduction of more than 92% in unauthorized entries with low system latency of less than 8 milliseconds. The intrusion detection system incorporates AI, achieving an F1-score of 94.8% across a range of attack types, including DDoS, brute force, and SQL injection. The practice provides effective safeguarding against current and upcoming cyberattacks, with a flexible and smart method of securing telemedicine setups.

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