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.
Loading...
Related Quiz
- Which application of AI in banking is primarily focused on enhancing customer service?
- In an e-commerce recommendation system powered by ML, users are consistently being recommended irrelevant items. How would you troubleshoot and resolve this issue?
- How does AI enhance predictive maintenance in manufacturing industries?
- Which of the following is an application of AI in improving the supply chain in e-commerce?
- You are developing an NLP model to monitor and analyze social media mentions for a brand. How would you account for sarcasm and implicit meanings in the messages?