Leveraging Pretrained Models for Multimodal Medical Image Interpretation: An Exhaustive Experimental Analysis.

The article examines the effectiveness of pretrained machine learning models in interpreting various medical images, highlighting their potential to enhance diagnostic accuracy despite challenges in generalizability. Topics discussed include the evaluation of different convolutional neural networks (CNNs), their performance across multiple imaging modalities, and future directions for improving model generalizability and integration with patient data.