"To build trust, there needs to be robust evaluation frameworks for AI. These tools also "cannot be a black box," so providing "insights and additional real-time information behind those outputs and algorithms is critical," he said. In addition, it's essential to automate the monitoring of AI models."
""Once you open up the gate, you're going to end up with dozens of different AI models, and the question I'm asking is, 'How am I going to keep up with monitoring this consistently as an organization?' So we've been focusing on developing algorithms and methods to actually be able to monitor the performance of these models in real time, ... and that actually builds trust with the team, knowing that somebody or a group is continuously looking at those and alerting if something goes off track,""
AI tools such as ambient scribing and clinical decision support are increasingly integrated into healthcare workplaces. Adoption depends on worker trust, requiring robust evaluation frameworks, transparency, and explanation of outputs. Organizations must automate model monitoring and develop algorithms to track dozens of models in real time, enabling alerts when performance drifts. Continuous monitoring and visible oversight help build team confidence. Frontline staff express concern about job loss, creating resistance to new tools. Clinician-facing strategies should prioritize maximizing patient-facing time and equipping clinicians with appropriate AI-enabled tools while addressing operational and workforce challenges.
Read at MedCity News
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