Healthcare AI Systems Face Data-Poisoning Risks

A 2026 study by Abtahi et al. in Journal of Medical Internet Research synthesizes 41 security studies (2019–2025) to analyze data-poisoning threats across healthcare AI architectures, constructing eight technical attack scenarios spanning CNNs, LLMs, federated learning, resource-allocation systems, and supply chains. It finds attackers with 100–500 poisoned samples can achieve ≥60% success and detection commonly delays 6–12 months, recommending ensemble monitoring, adversarial testing, auditable privacy-preserving mechanisms, and strengthened governance.