Healthcare AI security and adoption

Initial reports highlighted that the rollout of artificial‑intelligence tools in health care was constrained by inadequate technical infrastructure and the presence of unregulated, “shadow” AI systems. Subsequent analysis by the MIT AI Risk Initiative identified a range of emerging threats—including AI‑enabled cyberattacks, misinformation, and the persistence of proprietary model artifacts—particularly in the information and finance sectors but with clear implications for health‑care data. A breach at British vendor Craneware exposed records from thousands of clinics, illustrating concrete supply‑chain vulnerabilities. In response, U.S. agencies such as CISA and the FBI are shifting toward identity‑centric defenses, while European regulators are issuing vendor‑contract guidelines that require explicit handling, deletion, and oversight of patient data and AI model components after a contract ends. The situation is evolving from broad adoption obstacles to targeted policy actions aimed at securing the health‑care AI ecosystem.