Enterprise AI Analysis

Case Study: Q-MedAI: Quantum-Enhanced Medical Diagnosis

Q-MedAI is an AI system enhanced with quantum algorithms, designed to improve diagnostic accuracy in the analysis of radiological images (e.g., CT scans and MRIs). The system is integrated into a distributed hospital network and receives data from multiple connected clinics. An external attacker attempts to exploit a network vulnerability to launch a MITM attack during the data collection phase. By intercepting medical images and patient metadata, the attacker violates the principles of privacy and security, as the intercepted data contain sensitive personal health information that may be exposed or altered without patient consent. This undermines confidentiality—a core component of data privacy—and compromises data integrity. Both are fundamental to ensuring secure and trustworthy AI systems, particularly in healthcare contexts where the consequences of misuse or misdiagnosis can be severe. At the same time, the attacker launches a data poisoning attack during the training phase by injecting manipulated images to confuse the model's ability to correctly identify tumor patterns. The system is supported by an intelligent monitoring framework that analyzes risks and proposes specific countermeasures.

In this case, the MITM attack was unsuccessful, as post-quantum encryption had been implemented. Even with access to a quantum computer, the attacker was unable to decrypt communications protected by PQC. As a result, the framework did not flag an ethical violation of the principles of privacy and security. The data poisoning attack was also unsuccessful, as the system had been trained using quantum adversarial training, which increased its resilience to manipulated inputs. The monitoring module confirmed that the model maintained its reliability and did not produce clinically significant alterations in diagnostic outcomes. Therefore, the risks to privacy, security, and reliability were effectively mitigated, thanks to robust, by-design defensive mechanisms integrated into the system. The principle of non-maleficence was preserved, as the model did not generate incorrect or harmful diagnoses.