Journal Article

DZNE-2026-01012

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Locally deployed large language model for real-world lecanemab eligibility pre-screening.

Miklitz, C. (First author)DZNE* ; Shrestha, M.DZNE* ; Wegner, P.DZNE* ; Henk, N.DZNE* ; Sprinkart, A. M. ; Block, W. ; Luetkens, J. A. ; Radbruch, A.Extern* ; Lehnen, N. C. ; Nowak, S.

2026
Wiley Hoboken, NJ

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Please use a persistent id in citations: doi:10.1002/dad2.70495

Abstract: The introduction of disease-modifying Alzheimer's therapies requires complex, labor-intensive patient screening. Cloud-based large language models (LLMs) could support this task but are often unsuitable for routine care due to data protection constraints.We evaluated an on-premises, open-weights LLM (gpt-oss-120b) for automated extraction of therapy-relevant variables for lecanemab eligibility from German memory clinic reports. In a two-stage design, LLM-based extraction prompts, a deterministic rule-based extractor, and a shared downstream rule-based classifier were optimized on a development set (n = 97) and evaluated on an independent hold-out set (n = 99), with expert consensus as ground truth.The LLM-based pipeline achieved 94% accuracy and a Cohen's kappa of 0.90 on the hold-out set, significantly surpassing the rule-based comparator (80% accuracy) and demonstrating performance comparable to human experts.A locally deployed, on-premises LLM may assist eligibility screening as a triage support tool, potentially facilitating access to novel therapies without compromising patient data privacy.

Keyword(s): Alzheimer's disease ; disease‐modifying therapy ; large language models ; lecanemab ; secure clinical AI deployment ; treatment eligibility

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Contributing Institute(s):

1. Patient Studies (Bonn) (Patient Studies (Bonn))
2. Clinical Research Platform (CRP) (Clinical Research Platform (CRP))

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2026

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Database coverage:
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