Read this week’s #EAPCblog by Charlotta Lindvall to find out more about AI and contributing to the upcoming special edition of Palliative Medicine on Digital Health in Palliative Care! bit.ly/3Wkllal Lindvall Lab Amara Nwosu - Palliative Care & Technology @COstgathe Catherine Walshe Palliative Medicine

🚨Preprint🚨 Open-source LLMs outperform proprietary models in extracting clinical note sections! 📄 HPI, Interval History, Assessment/Plan 🏆 Llama 3.1 8B: F1=0.92 (internal), F1=0.85 (external) ✅ Cost-effective, private, accessible. #AI #LLM 🔗 arxiv.org/abs/2501.14105

Perspective by E. Pierson (Emma Pierson) et al.: Using Large Language Models to Promote Health Equity nejm.ai/40gbwvn #AIinMedicine

Great work! Our lab is actively exploring how LLMs can enhance palliative care. We recently demonstrated that LLMs can capture ACP domains directly from the EHR jpsmjournal.com/article/S0885-…

Editorial by Adam Rodman and colleagues: When It Comes to Benchmarks, Humans Are the Only Way nejm.ai/4hMrAM9 #AIinMedicine

Feasibility Study for Using #LargeLanguageModels to Identify Goals-of-Care Documentation at Scale in Patients With Advanced Cancer: brnw.ch/21wS5cT Authored by Lindvall Lab et al.

A locally deployable, open-source LLM-Anonymizer can remove personal identifiers with high accuracy, offering a scalable and accessible solution for secure medical data processing. Learn more: nejm.ai/4iFTASJ

Excited to be representing @danafarber at #ASCO25 to share our findings on "Using large language models to assess adherence to ASCO patient-oncologist communication standards" Learn more at Poster #125 today at 1:30 pm!

Very excited to share the release of BioClinical ModernBERT! Highlights: - biggest and most diverse biomedical and clinical dataset for an encoder - 8192 context - fastest throughput with a variety of inputs - sota results across several tasks - base and large sizes (1/8)

You can just continue pre-train things ✨ Happy to announce the release of BioClinical ModernBERT, a ModernBERT model whose pre-training has been continued on medical data The result: SOTA performance on various medical tasks with long context support and ModernBERT efficiency

Clinical encoders are joining the ModernBERT family ☺️

🚀Announcing BioClinical ModernBERT, a SOTA encoder for healthcare AI, developed by Thomas Sounack Thomas Sounack for Dana-Farber Cancer Institute in collaboration with Harvard University, LightOn, Massachusetts Institute of Technology (MIT), McGill University, Albany Med Health System, Microsoft Research. Seamless continued pre-training enables SOTA

BioClinical ModernBERT is out! Built on the largest, most diverse biomedical/clinical dataset to date ‼️Delivers SOTA across the board Thrilled to be part of this effort led by Thomas Sounack

🚨New Paper: mdpi.com/2072-6694/17/1… MDPI #delirium #neurooncology

Few rigorous studies of large language models have been done in cancer care. Off-the-shelf models developed on general patient populations may need significant tuning. nature.com/articles/s4174…