AI is making waves in healthcare, assisting in everything from predicting patient outcomes to streamlining administrative tasks. However, maintaining these AI systems is not as simple as setting them up and letting them run. As users deepen their understanding of AI, new insights emerge about its applications, accuracy and overall value. Over time, these systems can waver in accuracy, highlighting the need for ongoing oversight and updates.  

In this blog series, we have explored AI’s potential across various healthcare specialties, including emergency medicine, radiology, and anesthesiology. Be sure to check out our previous blog posts to see how AI is making an impact in these specialties.

Despite the promise of cost savings, implementing AI in healthcare often requires substantial human involvement to monitor and manage these systems. Recent reports indicate that this human oversight has, in some cases, led to unexpected increases in operational costs, raising questions about AI’s long-term financial impact on healthcare.  

Another major challenge is the absence of standard evaluation methods for AI tools. Without universal benchmarks, physicians and healthcare facilities may struggle to assess and trust the performance of these technologies. This points out the need for clear guidelines to ensure AI’s effectiveness and reliability. We will be closely monitoring both state and federal policy developments this year, reviewing any new AI regulations that may emerge.