Answer: Transfer Learning

## Explanation **Transfer Learning** is the most suitable technique for this scenario because: 1. **Definition**: Transfer learning involves taking a pre-trained model (trained on one task/dataset) and adapting it to a new, related task with minimal retraining. 2. **Why it fits this scenario**: - The team already has a trained AI model for X-ray image analysis - They want to adapt it to MRI-based anomaly detection - They want to avoid retraining from scratch - Both tasks involve medical image analysis, so the learned features (edges, textures, patterns) from X-rays can be transferred to MRI analysis 3. **How it works**: - Use the pre-trained X-ray model as a starting point - Keep the early layers (which learn general image features) - Replace or fine-tune the final layers for the new MRI task - Train only the modified layers or the entire model with a low learning rate 4. **Benefits**: - Faster training (don't start from random weights) - Requires less labeled MRI data - Leverages knowledge from the X-ray domain - More efficient than training from scratch **Why other options are incorrect**: - **A) Reinforcement Learning**: Used for sequential decision-making problems where an agent learns through trial and error with rewards/penalties, not for adapting pre-trained models to new image analysis tasks. - **C) Few-shot prompting**: Typically used in large language models where you provide a few examples to guide the model's response, not for adapting computer vision models to new medical imaging tasks. - **D) Zero-shot inference**: The model makes predictions on new classes it hasn't seen during training without any examples, which is less suitable when you have a related pre-trained model that can be fine-tuned. Transfer learning is a fundamental technique in machine learning for efficiently adapting models to new but related tasks, making it the ideal choice for this healthcare analytics scenario.

Author: Ritesh Yadav