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Found 6 papers, 2 papers with code
Updating the Minimum Information about CLinical Artificial Intelligence (MI-CLAIM) checklist for generative modeling research
In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the "Minimum information about clinical artificial intelligence modeling" (MI-CLAIM) checklist.
Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models
Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations.
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
In this study, we explored whether recent LLMs can reduce the need for large-scale data annotations.
CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference
Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in clinical notes.
Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes
Our findings indicate that significant disparities exist among breast cancer patients receiving different types of therapies based on social determinants of health.
Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors
We used word frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion.
