NCA-GENM Question 96
Select 3A healthcare company is using a multimodal model combining medical imaging data and patient text records to predict potential diagnoses. They want to improve the explainability of the AI model's predictions for regulatory compliance and clinician trust. Which of the following approaches would best leverage the multimodal architecture to enhance explainability?
- A
Generate visual saliency maps for the imaging data to highlight regions of interest that influenced the prediction.
- B
Use attention mechanisms to identify key phrases in the patient text records that were prioritized by the model.
- C
Combine the outputs of the multimodal model into a single confidence score without additional breakdown.
- D
Implement post-hoc interpretable visualizations to separately analyze contributions from each modality.
- E
Train the multimodal model with only imaging data for simplicity, as it is easier to explain predictions from a single modality.
Show answer and explanation
Correct answers: A, B, D
Explanation
To improve explainability in multimodal models, it is crucial to provide insights into how each modality contributes to the predictions. Methods like saliency maps, attention mechanisms, and post-hoc visualizations make the model's decision-making process more transparent and understandable for end-users, such as clinicians.
- A. Correct.
Generating saliency maps for the imaging data directly showcases which areas of the image contributed most to the prediction, improving explainability for clinicians.
- B. Correct.
Attention mechanisms can be used to highlight specific phrases or words in the text data that influenced the decision, making the textual contribution more understandable.
- C. Incorrect.
Combining outputs into a single score without further breakdown reduces explainability, as it does not provide insights into how different modalities contributed to the prediction.
- D. Correct.
Post-hoc visualizations allow the analysis of each modality’s contribution after the prediction, improving transparency and explainability.
- E. Incorrect.
Training the model with only one modality sacrifices the advantages of multimodal models and does not effectively improve explainability.