Large language models for biomedicine: foundations, opportunities, challenges, and best practices
Sahoo S, Plasek J, Xu H, Uzuner Ö, Cohen T, Yetisgen M, Liu H, Meystre S, Wang Y. Large language models for biomedicine: foundations, opportunities, challenges, and best practices. Journal Of The American Medical Informatics Association 2024, 31: 2114-2124. PMID: 38657567, PMCID: PMC11339493, DOI: 10.1093/jamia/ocae074.Peer-Reviewed Original ResearchNatural language processingPrompt tuningNLP applicationsLanguage modelState-of-the-art performanceNLP practitionersNatural language processing applicationsBiomedical NLP applicationsPre-training datasetNatural language understandingNeural network architecture modelNatural language generationBiomedical informatics communityNetwork architecture modelAmerican Medical Informatics Association (AMIAPrompt-tuningFew-shotZero-ShotNLP challengeNLP tasksReinforcement learningHuman feedbackLanguage generationLanguage understandingEvaluation metricsEnsemble pretrained language models to extract biomedical knowledge from literature
Li Z, Wei Q, Huang L, Li J, Hu Y, Chuang Y, He J, Das A, Keloth V, Yang Y, Diala C, Roberts K, Tao C, Jiang X, Zheng W, Xu H. Ensemble pretrained language models to extract biomedical knowledge from literature. Journal Of The American Medical Informatics Association 2024, 31: 1904-1911. PMID: 38520725, PMCID: PMC11339500, DOI: 10.1093/jamia/ocae061.Peer-Reviewed Original ResearchNatural language processingNatural language processing systemsLanguage modelExpansion of biomedical literatureZero-shot settingManually annotated corpusKnowledge graph developmentTask-specific modelsDomain-specific modelsZero-ShotEntity recognitionBillion parametersEnsemble learningLocation informationKnowledge basesBiomedical entitiesLanguage processingFree textGraph developmentBiomedical conceptsAutomated techniqueBiomedical literatureDetection methodPredictive performanceBiomedical knowledge