transfer learning in healthcare

Transfer learning in healthcare involves using a machine learning model trained on one large dataset, often from general or related tasks, and applying it to a specific medical problem. Instead of building a new model from scratch, the pre-trained model’s knowledge helps improve accuracy and efficiency, especially when medical data is limited. For example, a model trained to recognize tumors in general images can be adapted to detect specific cancer types. This approach speeds up development, saves resources, and often results in better performance in diagnosing diseases compared to training models from zero.