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However, only around 20% have implemented comprehensive programs with frameworks, governance, and guardrails to oversee AI model development and proactively identify and mitigate risks. Given the fast pace of AIdevelopment, leaders should move forward now to implement frameworks and mature processes.
This collaboration is crucial for aligning our AIstrategy with the specific needs of our customers, which are constantly evolving. Given the rapid pace of advancements in AI, I dedicate a substantial amount of time to staying abreast of the latest developments and trends in the field.
These encompass a holistic approach, covering data governance, model development, ethical deployment, and ongoing monitoring, reinforcing the organization’s commitment to responsible and ethical AI/ML practices. Stay tuned as we continue to explore the AI/ML CoE topics in our upcoming posts in this series.
For example, our algorithms trained to detect specific cancers benefit from validation against laboratory histology data, while AI predictions for treatment regimens can be cross compared with real-world clinical survival outcomes.
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