Consider a healthcare scenario where an AI model, trained on data from one hospital, is underperforming when deployed in a different hospital due to variations in data recording practices. How would you improve the model’s interoperability across these diverse data environments?

  • Keep using the same model without modifications.
  • Collect more data from the second hospital to fine-tune the existing model.
  • Train a new model from scratch using data from both hospitals.
  • Stop using AI in healthcare altogether.
Option C is the correct choice. To improve model interoperability, training a new model from scratch using data from both hospitals would ensure that the AI system is better adapted to the variations in data recording practices. Options A and D are not effective solutions, and option B may not fully address the differences in data environments.
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