The agency launched Elsa 4.0, an upgrade to the agency’s internal AI tool available to all FDA staff, from scientific reviewers to investigators.
Health systems globally face higher utilization, tighter scrutiny, and less operational slack. As devices become more connected and service events more consequential, the limitations of generic service technology become harder to ignore.
Without formal policies and governance, MedTech organizations face risks to intellectual property, product quality, and ultimately patient safety. Thoughtful AI governance enables development teams to capture efficiency gains while maintaining the rigor that the industry demands.
In healthcare, a cyber vulnerability is not just an IT problem. It can quickly become a patient-care problem.
Healthcare environments are dynamic with patient populations, clinical practices and data collection methods continuously evolving. Similarly, effective AI systems depend on more than performance at initial deployment. They must be monitored and managed throughout their lifecycle to remain reliable, clinically relevant, and safe.
No longer a horizon technology, Artificial Intelligence in healthcare has reached a level of real-world performance that makes clinical value demonstrable at a critical time in healthcare – a time of clinician shortages, backlogs, and rising costs that have made access to treatment a challenge.
Protect patient data with medical IoT security. Learn how AI, Zero Trust, and encryption can prevent cyber threats and secure healthcare IoT devices.
Many challenges of designing and validating pediatric digital health devices are over-looked across developmental stages. Regulatory strategy, human factors, software architecture, and algorithm performance are critical consideration in dynamic patient populations.
A long-overdue push to reduce administrative friction, improve access to patient data, and move the system away from workflows that continue to waste time for both patients and providers.
As generative AI becomes embedded in clinical research, concerns around hallucinated citations and unverified outputs are growing. These inaccuracies are already entering scientific literature, raising questions about reliability, compliance, and patient safety. Addressing this challenge requires more evidence-grounded AI and disciplined implementation.