Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.
Akhil VaidSon Q DuongJoshua LampertPatricia KovatchRobert M FreemanEdgar ArgulianLori CroftStamatios LerakisMartin GoldmanRohan KheraGirish N NadkarniPublished in: Journal of the American Medical Informatics Association : JAMIA (2024)
The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.
Keyphrases
- big data
- decision making
- electronic health record
- randomized controlled trial
- systematic review
- artificial intelligence
- machine learning
- case report
- autism spectrum disorder
- high resolution
- emergency department
- fluorescent probe
- sensitive detection
- health information
- data analysis
- living cells
- single molecule
- drug induced