Evidence map›Paper›PMID 40277428›Full record

ArticleAnnals of neurology2025

Detecting New Lesions Using a Large Language Model: Applications in Real-World Multiple Sclerosis Datasets.

Shane Poole, Nikki Sisodia, Kanishka Koshal, Kyra Henderson, Jaeleene Wijangco, Danelvis Paredes, Chelsea Chen, William Rowles, Amit Akula, Jens Wuerfel and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. AI for clinical neurology: Comparing five large language models in neurological diagnosis and lesion localization.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Article
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shane PooleUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Nikki SisodiaUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.ORCID 0009-0006-7270-595X
Kanishka KoshalUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Kyra HendersonUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Jaeleene WijangcoUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Danelvis ParedesUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Chelsea ChenUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
William RowlesUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Amit AkulaUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Jens WuerfelF. Hoffmann-La Roche, Basel, Switzerland.
Vishakha SharmaF. Hoffmann-La Roche, Basel, Switzerland.
UCSF Multiple Sclerosis and Neuroinflammation Center cliniciansUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Andreas M RauscheckerUCSF Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical imaging, University of California, San Francisco, CA, USA.
Roland G HenryUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-8232-7562
Riley BoveUCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.

Funding

F. Hoffmann-La Roche
6 · The paper itself

Abstract

objectiveNeuroimaging is routinely utilized to identify new inflammatory activity in multiple sclerosis (MS). A large language model to classify narrative magnetic resonance imaging reports in the electronic health record (EHR) as discrete data could provide significant benefits for MS research. The objectives of the current study were to develop such a prompt and to illustrate its research applications through a common clinical scenario: monitoring response to B-cell depleting therapy (BCDT).

methodsAn institutional ecosystem that securely connects healthcare data with ChatGPT4 was applied to clinical MS magnetic resonance imaging reports in a single institutional EHR (2000-2022). A prompt (msLesionprompt) was developed and iteratively refined to classify the presence or absence of new T2-weighted lesions (newT2w) and contrast-enhancing lesions (CEL). The multistep validation included evaluating efficiency (time and cost), comparison with manually annotated reports using standard confusion matrix, and application to identifying predictors of newT2w/CEL after BCDT start.

resultsAccuracy of msLesionprompt was high for detection of newT2w (97%) and CEL (96.8%). All 14,888 available reports were categorized in 4.13 hours ($28); 79% showed no newT2w or CEL. Data extracted showed expected suppression of new activity by BCDT (>97% monitoring magnetic resonance images after an initial "rebaseline" scan). Neighborhood poverty (Area Deprivation Index) was identified as a predictor of inflammatory activity (newT2w: OR 1.69, 95% CI 1.10-2.59, p = 0.017; CEL: OR 1.54, 95% CI 1.01-2.34, p = 0.046).

interpretationExtracting discrete information from narrative imaging reports using an large language model is feasible and efficient. This approach could augment many real-world analyses of MS disease evolution and treatment response. ANN NEUROL 2025;98:308-316.

Indexed as

Electronic Health RecordsMagnetic Resonance ImagingMultiple SclerosisAdultFemaleHumansLarge Language ModelsMaleMiddle AgedNeuroimaging

Identifiers

PMID40277428
PMCPMC12278027

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.