The field of active learning is experiencing significant growth, with a focus on improving the accuracy and efficiency of machine learning models in healthcare and natural language processing. Researchers are exploring new methods to integrate human expertise with active learning, enabling the identification of high-risk patients and the prediction of clinical outcomes. The use of large language models and active attention networks is becoming increasingly popular, allowing for more accurate predictions and reduced annotation efforts. Furthermore, the development of frameworks for active text generation and human-in-the-loop AI is simplifying the implementation of active learning strategies and providing more accurate estimates. Noteworthy papers include:

• A study introducing an Active Attention Network to predict clinical risk and identify progression events related to Post Acute Sequelae of SARS-CoV-2.
• A framework for Active Text Generation, which enables the application of state-of-the-art active learning strategies to natural language generation tasks.