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Privacy-Preserving Generation of Structured Lymphoma Progression Reports from Cross-sectional Imaging: A Comparative Analysis of Llama 3.3 and Llama 4.

단면연구 2/5 보강

Journal of imaging informatics in medicine 📖 저널 OA 40.6% 2024~2026 2026 Vol.39(2) p. 1868-1878 OA Radiomics and Machine Learning in Me

TL;DR Evaluating two of the most recent Llama models for generating structured lymphoma progression reports from cross-sectional imaging data in a privacy-preserving, real-world clinical setting suggests their potential utility in streamlining documentation and highlight areas requiring optimization before clinical implementation.

PICO 자동 추출 (휴리스틱, conf 3/4)

유사 논문

P · Population 대상 환자/모집단

65 patients included in this study.

I · Intervention 중재 / 시술

cross-sectional imaging and treatment between July 2023 and July 2024

C · Comparison 대조 / 비교

추출되지 않음

O · Outcome 결과 / 결론

In conclusion, privacy-preserving LLMs can effectively extract clinical information from lymphoma imaging reports.

OpenAlex 토픽 · Radiomics and Machine Learning in Medical Imaging Lung Cancer Diagnosis and Treatment Global Cancer Incidence and Screening

Prucker P, Bressem KK, Kim SH, Weller D, Kader A, Dorfner FJ

📖 무료 전문 🟢 PMC 전문 PMC13103223 🔓 OA PDF unpaywall · cc-by

PubMed ↗ DOI ↗ BibTeX ↓ RIS ↓

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