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To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.

To develop indices of US hospital interoperability to capture the current state and assess progress over time.

To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.

We evaluate the effectiveness of large language models (LLMs), specifically GPT-based (GPT-3.5 and GPT-4) and Llama-2 models (13B and 7B architectures), in autonomously assessing clinical records (CRs) to enhance medical education and diagnostic skills.

Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone. This paper presents an ablation study exploring how different embedding models and pooling methods affect information retrieval for the clinical domain.

To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.

We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example.