Which process ensures that AI models adapt to changes in medical coding practices over time?

The key to ensuring that AI models remain effective and relevant in a constantly evolving field like medical coding lies in continuous learning and adaptability. This process allows AI models to incorporate new data, update their algorithms, and refine their understanding based on the latest coding practices, regulations, and trends.

As medical coding practices change—due to factors such as updates in coding standards, the introduction of new diagnostic codes, or shifts in healthcare regulations—AI models that engage in continuous learning can adapt to these changes more efficiently. This involves not just initially training the model on a static dataset but also enabling it to learn from new data incrementally. By doing so, the model can improve its accuracy over time, provide better predictions, and maintain compliance with current practices.

In contrast, the other options relate to important processes in the development and maintenance of AI systems but do not specifically address the ongoing adaptation aspect. Data collection is essential for building a model but does not inherently ensure that the model can adapt over time. Model training establishes the initial capabilities of an AI system but may become outdated without mechanisms for continuous updates. Data validation is crucial for maintaining the quality and reliability of data being used but does not directly influence the model's ability to learn and adapt continually. Thus, continuous