Lindvall Lab
@lindvalllab
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Physician-Investigator, Psychosocial Oncology and Palliative Care @DanaFarber
Boston, MA
Joined April 2018
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
npj Digital Medicine - Responsible Artificial Intelligence governance in oncology
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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 @tsounack
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)
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🧠 Long context (8192 tokens) 📚 Trained on 53.5B tokens, the largest biomedical + clinical corpus ever used for an encoder 📈 SOTA on biomedical and clinical tasks ⚡ Fastest inference & fine-tuning 🔓 Released in base & large sizes with training checkpoints (2/3)
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We’re proud to announce BioClinical ModernBERT, led by our team at the @lindvalllab and collaborators! (1/3)
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Clinical encoders are joining the ModernBERT family ☺️
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)
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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
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)
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🚀Announcing BioClinical ModernBERT, a SOTA encoder for healthcare AI, developed by Thomas Sounack @tsounack for Dana-Farber Cancer Institute in collaboration with @Harvard, @LightOnIO, @MIT, @mcgillu, @AlbanyMed, @MSFTResearch. Seamless continued pre-training enables SOTA
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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)
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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!
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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: https://t.co/XYkZNIVMl4
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Feasibility Study for Using #LargeLanguageModels to Identify Goals-of-Care Documentation at Scale in Patients With Advanced Cancer: https://t.co/swsocaC3Ef Authored by @lindvalllab et al.
ascopubs.org
PURPOSEThe purpose of our study was to (1) use a large language model (LLM) to identify goals-of-care (GOC) conversations in a large volume of notes, and (2) explore the potential of LLMs for a novel...
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Editorial by @AdamRodmanMD and colleagues: When It Comes to Benchmarks, Humans Are the Only Way https://t.co/YTbOyGrS8o
#AIinMedicine
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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 https://t.co/lCV3hLynWg
jpsmjournal.com
Efficiently tracking Advance Care Planning (ACP) documentation in electronic heath records (EHRs) is essential for quality improvement and research efforts. The use of large language models (LLMs)...
@NEJM @ASCO @oncologyCOA @JCO_ASCO @ramsedhom @realbowtiedoc @NCCN @miteshspatel @kevin_volpp @CassSunstein @CAPCpalliative 🌍 Implications: Algorithm-driven PC referrals scale in comm onc. Need better PC eligibility algorithms (esp incorporating symptom/psychosocial distress). Key area for #LLMs! @lindvalllab @kenlkehl @dbittermanmd @layerhealth Defaults can improve care beyond #oncology. (9/)
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Perspective by E. Pierson (@2plus2make5) et al.: Using Large Language Models to Promote Health Equity https://t.co/trP4cqyDdZ
#AIinMedicine
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🚨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
Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. While proprietary...
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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! https://t.co/lxwQ9CGA7j
@lindvalllab @amaranwosu @COstgathe @cewalshe @PalliativeMedJ
eapcnet.wordpress.com
How can we measure high quality and effective palliative care? Traditional research methods have always struggled to do this… but artificial intelligence (AI) might be able to add a new dimension. …
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Non-Hispanic white patients experience less asymmetry (73%) than other racial/ethnic groups (82%). Bridging this gap is essential #HealthEquity. #OncologyResearch @PECthejournal
https://t.co/qXBcS2LoyZ 2/2
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🚨 New findings 🚨 : 77% of illness understanding discussions between oncologists and advanced cancer patients are clinician-dominated. 1/2
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