Lindvall Lab

Physician-Investigator, Psychosocial Oncology and Palliative Care @DanaFarber

[npj Digital Medicine @npjDigitalMed]

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. https://t.co/yQUo9hNWiL

[Lindvall Lab @lindvalllab]

🚨New Paper: https://t.co/QfMm0rxKYw
@MDPIOpenAccess #delirium #neurooncology

[Josh Davis @joshp_davis]

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

[Lindvall Lab @lindvalllab]

ArXiv: https://t.co/tgAC3KZxLl
HF Collection: https://t.co/wlVwE88kX0
(3/3)

[Lindvall Lab @lindvalllab]

We’re proud to announce BioClinical ModernBERT, led by our team at the @lindvalllab and collaborators!
(1/3)

[Lindvall Lab @lindvalllab]

🧠 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)

[Ben Clavié @bclavie]

Clinical encoders are joining the ModernBERT family ☺️

[Antoine Chaffin @antoine_chaffin]

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

[LightOn @LightOnIO]

🚀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 performance on various clinical benchmarks, with the usual long context support and efficiency of ModernBERT

👉 Discover BioClinical ModernBERT now: https://t.co/1b3NgDwWqA

Kudos to @tsounack, @joshp_davis, @DurieuxBrigitte, @antoine_chaffin, @tompollard, @lehmer16, @alistairewj, @MattBMcDermott ,@TristanNaumann, @lindvalllab on this major leap for biomedical NLP!

[Thomas Sounack @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)

[Lindvall Lab @lindvalllab]

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!

[NEJM AI @NEJM_AI]

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

[JCO Oncology Practice @JCOOP_ASCO]

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.

[NEJM AI @NEJM_AI]

Editorial by @AdamRodmanMD and colleagues: When It Comes to Benchmarks, Humans Are the Only Way https://t.co/YTbOyGrS8o

#AIinMedicine

[Lindvall Lab @lindvalllab]

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

[Ravi B. Parikh @ravi_b_parikh]

@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/)

[NEJM AI @NEJM_AI]

Perspective by E. Pierson (@2plus2make5) et al.: Using Large Language Models to Promote Health Equity https://t.co/trP4cqyDdZ

#AIinMedicine

[Lindvall Lab @lindvalllab]

🚨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
🔗 https://t.co/U0O8eROuC1

[EAPC VZW @EAPCvzw]

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

[Lindvall Lab @lindvalllab]

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

[Lindvall Lab @lindvalllab]

🚨 New findings 🚨 : 77% of illness understanding discussions between oncologists and advanced cancer patients are clinician-dominated. 1/2