MsLesionLLM: A Tool to Extract Key Radiological Metrics from Real-world Multiple Sclerosis Datasets (P2-1.002)
To (1) develop and validate msLesionLLM, an AI-powered prompt to extract information about new multiple sclerosis (MS)-related inflammatory activity from MRI reports and (2) apply the prompt to a real-world use case: MRI activity after starting B-cell depleting therapy.
Authors
- Kanishka Koshal (ORCID: https://orcid.org/0000-0002-0612-0738)
- Amit Akula (ORCID: https://orcid.org/0000-0003-2867-7569)
- Danelvis Paredes (ORCID: https://orcid.org/0000-0002-3525-7696)
- Andreas M. Rauschecker (ORCID: https://orcid.org/0000-0003-0633-9876)
- Jaeleene Wijangco (ORCID: https://orcid.org/0009-0006-1561-6547)
- William Rowles (ORCID: https://orcid.org/0000-0002-5613-0483)
- Jens Wuerfel (ORCID: https://orcid.org/0000-0002-0034-8053)
- Shane T. Poole (ORCID: https://orcid.org/0000-0002-3203-151X)
- Riley M. Bove (ORCID: https://orcid.org/0000-0002-2034-8800)
- Roland G. Henry (ORCID: https://orcid.org/0000-0002-8232-7562)
- Nikki Sisodia (ORCID: https://orcid.org/0009-0006-7270-595X)
- Chelsea Chen (ORCID: https://orcid.org/0000-0001-7776-4509)
- Kyra Henderson
- V. Harish Sharma (ORCID: https://orcid.org/0000-0002-5032-0585)
Institutions
- University of California, San Francisco (US)
- Roche (Greece) (GR)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000212366
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36