Monica Agrawal

Journal Article NPJ Digit Med · October 7, 2025 While large language models (LLMs) hold promise for transforming clinical healthcare, current comparisons and benchmark evaluations of large language models in medicine often fail to capture real-world efficacy. Specifically, we highlight how key discrepan ... Full text Link to item Cite

An evaluation framework for ambient digital scribing tools in clinical applications.

Journal Article NPJ Digit Med · June 13, 2025 Ambient digital scribing (ADS) tools alleviate clinician documentation burden, reducing burnout and enhancing efficiency. As AI-driven ADS tools integrate into clinical workflows, robust governance is essential for ethical and secure deployment. This study ... Full text Link to item Cite

Application of unified health large language model evaluation framework to In-Basket message replies: bridging qualitative and quantitative assessments.

Journal Article J Am Med Inform Assoc · April 1, 2025 OBJECTIVES: Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, which i ... Full text Link to item Cite

Using Large Language Models to Promote Health Equity

Journal Article NEJM AI · January 23, 2025 Full text Cite

"What’s Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets

Conference Emnlp 2025 2025 Conference on Empirical Methods in Natural Language Processing Findings of Emnlp 2025 · January 1, 2025 People are increasingly seeking healthcare information from large language models (LLMs) via interactive chatbots, yet the nature and inherent risks of these conversations remain largely unexplored. In this paper, we filter large-scale conversational AI da ... Full text Cite

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

Journal Article J Am Med Inform Assoc · June 20, 2024 OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS AND METHODS: We trained logistic regression models using n ... Full text Link to item Cite

Large language models are few-shot clinical information extractors

Conference Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · 2022 Full text Cite

Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative

Conference Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · May 6, 2021 Full text Cite

Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer.

Journal Article JCO Clin Cancer Inform · May 2021 PURPOSE: Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incid ... Full text Link to item Cite

Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph

Conference Biocomputing 2020 · December 2019 Full text Cite

Modeling polypharmacy side effects with graph convolutional networks.

Journal Article Bioinformatics · July 1, 2018 MOTIVATION: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polyphar ... Full text Link to item Cite

Large-scale analysis of disease pathways in the human interactome.

Conference Pac Symp Biocomput · 2018 Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational ... Link to item Cite