The 2024 Change Healthcare breach may prove to be a defining event in the history of digital health infrastructure. With over 190 million individuals affected and core operations across thousands of health systems disrupted, the incident was unprecedented in both scale and systemic impact. But it was not an outlier. Ransomware in healthcare is the dominant breach vector.

As artificial intelligence continues its rapid integration into clinical workflows, a looming liability vacuum threatens to destabilize trust, governance, and accountability in patient care. While AI tools promise greater efficiency, diagnostic power, and operational agility, legal experts are raising alarms about the absence of clearly defined fault lines when outcomes go wrong.

Artificial intelligence in health care is often discussed in terms of automation and pattern recognition, but a new system developed at the Icahn School of Medicine at Mount Sinai signals a more profound shift: AI that can tailor its diagnostic reasoning to individual patients and recognize when it lacks enough information to proceed. The system, called InfEHR, challenges traditional models of clinical support by operating not just as a predictor, but as a dynamic inference engine.

Artificial intelligence is already shaping how care is delivered, how health systems operate, and how patients access services. But the rapid pace of AI adoption is exposing a foundational gap: health care lacks the infrastructure, incentives, and oversight mechanisms to evaluate whether these tools are actually improving health outcomes.