The recent developments in the biomedical and healthcare research domain have seen a significant shift towards leveraging large language models (LLMs) to address various challenges, particularly in data interoperability, fairness, and automation of complex tasks. A notable trend is the integration of social determinants of health (SDoH) into knowledge graphs, which aims to mitigate biases and improve fairness in healthcare predictions. Additionally, there is a growing interest in using synthetic data and rephrased electronic health records (EHRs) for pretraining clinical language models, addressing privacy concerns while enhancing model performance. The field is also witnessing advancements in the extraction and synthesis of medical knowledge using LLMs, with a focus on improving evidence synthesis and data extraction from biomedical documents. Furthermore, there is a push towards developing compact LLM frameworks for local, high-privacy EHR data applications, which aim to balance performance with privacy and computational constraints. These developments collectively highlight the potential of LLMs to revolutionize healthcare by improving data accessibility, reducing biases, and automating complex clinical tasks.
Noteworthy papers include 'Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare,' which introduces a novel fairness formulation for graph embeddings, and 'Rephrasing Electronic Health Records for Pretraining Clinical Language Models,' which demonstrates the potential of synthetic clinical text to improve language modeling and downstream tasks.
