2025
Emerging Trends in Artificial Intelligence in Neuro-Oncology
Chadha S, Sritharan D, Hager T, D’Souza R, Aneja S. Emerging Trends in Artificial Intelligence in Neuro-Oncology. Current Oncology Reports 2025, 1-12. PMID: 40504358, DOI: 10.1007/s11912-025-01688-w.Peer-Reviewed Original ResearchArtificial intelligenceNeuro-oncologyLeverage natural language processingExtract actionable insightsNatural language processingTreatment response evaluationOptimal treatment planHardware efficiencyLanguage processingComputational pathologyModel generalizabilityActionable insightsAutomated tumor segmentationTumor segmentationRisk stratificationDiagnostic accuracyMolecular classificationTreatment planningClinical reportsComputational techniquesResponse evaluationReviewThis articlePatient outcomesClinical workflowAccelerating drug discovery"Show Your Mind": Unveiling User Experience on an AI-based Mental Health Assessment System with Symptom-based Evidences
Won H, Kang M, Kim M, Lee D, Choi H, Kim Y, Choi D, Ko M, Han J. "Show Your Mind": Unveiling User Experience on an AI-based Mental Health Assessment System with Symptom-based Evidences. 2025, 1-11. DOI: 10.1145/3706599.3719735.Peer-Reviewed Original ResearchUser experienceMental health assessment systemUser-generated textNatural language processingUsers' understandingLanguage processingUsage intentionUsersHealth assessment systemMental healthAssessment systemModel accuracyMental health conditionsWithin-subject studyEarly-stage evaluationDetect mental health conditionsHealth conditionsSocial stigmaSymptom-based approachAnonymitySystemBenchmarking large language models for biomedical natural language processing applications and recommendations
Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, Singer M, Ai X, Lai P, Wang Z, Keloth V, Raja K, Huang J, He H, Lin F, Du J, Zhang R, Zheng W, Adelman R, Lu Z, Xu H. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nature Communications 2025, 16: 3280. PMID: 40188094, PMCID: PMC11972378, DOI: 10.1038/s41467-025-56989-2.Peer-Reviewed Original ResearchConceptsLanguage modelNatural language processing applicationsBiomedical natural language processingMedical question answeringLanguage processing applicationsNatural language processingGrowth of biomedical literatureMissing informationFew-shotQuestion AnsweringZero-ShotKnowledge curationLanguage processingProcessing applicationsBioNLPBART modelPerformance gapBiomedical literatureGeneral domainTaskBenchmarksBERTInformationPerformanceLLMUsing natural language processing to identify emergency department patients with incidental lung nodules requiring follow‐up
Moore C, Socrates V, Hesami M, Denkewicz R, Cavallo J, Venkatesh A, Taylor R. Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow‐up. Academic Emergency Medicine 2025, 32: 274-283. PMID: 39821298, DOI: 10.1111/acem.15080.Peer-Reviewed Original ResearchNatural language processingIncidental lung nodulesFollow-upChest CTsCT reportsF1 scoreLung nodulesEmergency departmentLanguage processingFollow-up of incidental findingsIncidental findingNatural language processing developersAbsence of malignancyMetrics of precisionNatural language processing pipelineNatural language processing metricsChest CT reportsRecommended follow-upEmergency department patientsFollow-up rateLanguage modelLung cancerReduce errorsMalignancyDepartment patients
2024
Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.
Aronson P, Kuppermann N, Mahajan P, Nielsen B, Olsen C, Meeks H, Grundmeier R. Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation. Hospital Pediatrics 2024, 15: e1-e5. PMID: 39679596, PMCID: PMC12163744, DOI: 10.1542/hpeds.2024-008051.Peer-Reviewed Original ResearchElectronic health recordsEmergency departmentNatural language processing algorithmsElectronic health record dataPediatric Emergency Care Applied Research Network RegistryFebrile infantsNatural language processingCross-sectional studyTrauma-related diagnosesPositive predictive valueHealth recordsHealth systemDocumented feverClinical notesPre-EDNetwork registryCohort identificationVisitsLanguage processingNLP algorithmsPredictive valueInfantsFeverResearch studiesApplication of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination
Garbey M, Lesport Q, Girma H, Öztosun G, Abu-Rub M, Guidon A, Juel V, Nowak R, Soliven B, Aban I, Kaminski H. Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination. Frontiers In Neurology 2024, 15: 1474884. PMID: 39697445, PMCID: PMC11652356, DOI: 10.3389/fneur.2024.1474884.Peer-Reviewed Original ResearchNatural language processingMyasthenia gravisArtificial intelligenceArtificial intelligence methodsSeries of algorithmsComputer visionSignal processing methodsExamination of patientsConventional clinical settingsLanguage processingIntelligence methodsNon-physician healthcare providersVideoControl subjectsClinical trialsPatientsRespiratory metricsMyastheniaBreath countClinical settingAlgorithmBody motionVideos of patientsTelemedicine evaluationIntelligenceNatural language processing in mixed-methods evaluation of a digital sleep-alcohol intervention for young adults
Griffith F, Ash G, Augustine M, Latimer L, Verne N, Redeker N, O’Malley S, DeMartini K, Fucito L. Natural language processing in mixed-methods evaluation of a digital sleep-alcohol intervention for young adults. Npj Digital Medicine 2024, 7: 342. PMID: 39613828, PMCID: PMC11606959, DOI: 10.1038/s41746-024-01321-3.Peer-Reviewed Original ResearchYoung adultsLanguage processingEvaluating young adultsControl participantsHeavy drinkingSleep factorsImprove sleepNatural language processingPersonalized feedbackParticipation motivationParticipantsSleepAdultsMixed-methods evaluationControl interventionsInterventionConvergent mixed methodsMotivationIncreased awarenessAlcoholArtificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model
Alexiou V, Sumpio B, Vassiliou A, Kakkos S, Geroulakos G. Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model. Annals Of Vascular Surgery 2024, 111: 260-267. PMID: 39586530, DOI: 10.1016/j.avsg.2024.11.014.Peer-Reviewed Original ResearchNatural language processingAI modelsArtificial intelligenceMachine learning algorithmsLanguage modelLearning algorithmsVascular surgery patientsRelevant answersLanguage processingAI chatbotsIntroduction of artificial intelligenceStandalone solutionMedical classification systemsTest scenariosSurgery patientsMedical informationClinical scenariosComplex problemsIntelligenceScientific fieldsComplex clinical scenariosScenariosStatistically significant differenceClinically relevant answersPerformance variationEarly‐ and Late‐Stage Auditory Processing of Speech Versus Non‐Speech Sounds in Children With Autism Spectrum Disorder: An ERP and Oscillatory Activity Study
Edgar E, McGuire K, Pelphrey K, Ventola P, van Noordt S, Crowley M. Early‐ and Late‐Stage Auditory Processing of Speech Versus Non‐Speech Sounds in Children With Autism Spectrum Disorder: An ERP and Oscillatory Activity Study. Developmental Psychobiology 2024, 66: e22552. PMID: 39508446, DOI: 10.1002/dev.22552.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderNon-speech soundsSpeech soundsTD childrenSpectrum disorderCortical activityResponses to speech soundsProcessing stagesSensitivity to speechAtypical auditory processingTheta phase coherenceAuditory speech processingP3a amplitudeAuditory speechIntra-individual variabilityDevelopment of cortical networksASD childrenCortical networksSpeech processingAuditory processingElectroencephalography sessionLanguage processingCortical responsesAutismSpeechAscle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study
Yang R, Zeng Q, You K, Qiao Y, Huang L, Hsieh C, Rosand B, Goldwasser J, Dave A, Keenan T, Ke Y, Hong C, Liu N, Chew E, Radev D, Lu Z, Xu H, Chen Q, Li I. Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study. Journal Of Medical Internet Research 2024, 26: e60601. PMID: 39361955, PMCID: PMC11487205, DOI: 10.2196/60601.Peer-Reviewed Original ResearchConceptsNatural language processingNatural language processing toolkitQuestion-answering taskLanguage modelText generationText processingDomain-specific language modelsNatural language processing functionsMinimal programming expertiseText generation tasksMedical knowledge graphMachine translation tasksROUGE-L scoreDomain-specific challengesAll-in-one solutionROUGE-LText summarizationBLEU scoreKnowledge graphMachine translationUnstructured textQuestion-answeringHugging FaceProcessing toolkitLanguage processingWhen grammaticality is intentionally violated: Inanimate honorification as a politeness strategy
Kwon N, Lee Y. When grammaticality is intentionally violated: Inanimate honorification as a politeness strategy. Journal Of Pragmatics 2024, 232: 167-181. DOI: 10.1016/j.pragma.2024.08.012.Peer-Reviewed Original ResearchSelf-paced reading experimentsReading experiencePolitical strategiesReal-time language processingNative Korean speakersKorean speakersGrammatical normsLinguistic phenomenaInanimate subjectsLinguistic functionsGrammatical irregularitiesHonorificsLanguage processingSpeakersInterpersonal relationsPositive evaluationAddresseeContemporary useSocial nuancesPoliticsQuestionnaire resultsNegative perceptionsNational campaignGrammaticalityInterlocutorsA Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation
Wen A, Wang L, He H, Fu S, Liu S, Hanauer D, Harris D, Kavuluru R, Zhang R, Natarajan K, Pavinkurve N, Hajagos J, Rajupet S, Lingam V, Saltz M, Elowsky C, Moffitt R, Koraishy F, Palchuk M, Donovan J, Lingrey L, Stone-DerHagopian G, Miller R, Williams A, Leese P, Kovach P, Pfaff E, Zemmel M, Pates R, Guthe N, Haendel M, Chute C, Liu H, Collaborative C, Initiative T. A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation. JMIR Medical Informatics 2024, 12: e49997. PMID: 39250782, PMCID: PMC11420592, DOI: 10.2196/49997.Peer-Reviewed Original ResearchNatural language processingNatural language processing algorithmsNatural language processing toolkitNatural language processing systemsProcessing toolkitNatural language processing tasksUnified Medical Language SystemClinical natural language processingAlgorithm developmentMedical Language SystemLanguage processing systemDevelopment approachNLP tasksTime-critical natureHuman expertsNatural language processing resultsExpert annotationsLanguage processingTraining setClinical narrativesNatural language processing extractsTest setAlgorithmPostacute sequelae of SARS-CoV-2 infectionProcessing systemDeep-Transfer-Learning–Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer
Kim S, Kim S, Kim E, Cecchini M, Park M, Choi J, Kim S, Hwang H, Kang C, Choi H, Shin S, Kang J, Lee C. Deep-Transfer-Learning–Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer. JCO Clinical Cancer Informatics 2024, 8: e2400021. PMID: 39151114, DOI: 10.1200/cci.24.00021.Peer-Reviewed Original ResearchConceptsArea under the receiver operating characteristic curveSurvival of patientsCT reportsPancreatic cancerNatural language processingC-indexPredicting SurvivalOverall survival of patientsTertiary hospitalPredicting 1-year survivalPredicting survival of patientsImproved C-indexSurvival informationPancreatic cancer survivalReceiver operating characteristic curveInternal test data setNLP modelsComputed tomography reportsLanguage processingKorean tertiary hospitalOverall survivalConsecutive patientsActual survivalConcordance indexPatientsMapping the structure-function relationship along macroscale gradients in the human brain
Collins E, Chishti O, Obaid S, McGrath H, King A, Shen X, Arora J, Papademetris X, Constable R, Spencer D, Zaveri H. Mapping the structure-function relationship along macroscale gradients in the human brain. Nature Communications 2024, 15: 7063. PMID: 39152127, PMCID: PMC11329792, DOI: 10.1038/s41467-024-51395-6.Peer-Reviewed Original ResearchConceptsStructure-function correspondenceBrain regionsMacroscale gradientWhite matter connectivityHuman brain regionsStructure-function couplingNeural network propertiesAssociation cortexCognitive functionBridging neuroscienceFunctional coactivationOrganizational axisCortical thicknessHuman brainMotor cortexLanguage processingBrainCortexMotor functionNatural language processingNetwork propertiesMotorNeuroscienceNatural languageData repositoriesExplainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development
Chen L, Qiao C, Ren K, Qu G, Calhoun V, Stephen J, Wilson T, Wang Y. Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development. NeuroImage 2024, 298: 120771. PMID: 39111376, PMCID: PMC11533345, DOI: 10.1016/j.neuroimage.2024.120771.Peer-Reviewed Original ResearchConceptsSpatio-temporal dependenciesSpatial neighborhoodGraph learning methodsBrain network analysisNode representationsEvolution mechanisms of complex networksAdjacency informationDynamic brain network analysisModel explainabilityLanguage processingGraph evolutionEvolution learningLearning methodsLocal informationMechanism of complex networksDynamic evolutionModel dynamic interactionsDynamic functional connectivityNetwork componentsNested subgraphsLearning moduleExperimental resultsNetworkNetwork transitionsBrain development studiesNatural Language Processing for Digital Health in the Era of Large Language Models
Sarker A, Zhang R, Wang Y, Xiao Y, Das S, Schutte D, Oniani D, Xie Q, Xu H. Natural Language Processing for Digital Health in the Era of Large Language Models. Yearbook Of Medical Informatics 2024, 33: 229-240. PMID: 40199310, PMCID: PMC12020548, DOI: 10.1055/s-0044-1800750.Peer-Reviewed Original ResearchConceptsElectronic health recordsLanguage modelFew-shot settingTransformer-based modelsNatural language processingTask-oriented evaluationSocial mediaMedical textsNLP tasksMedical domainIEEE ExploreLanguage processingGeneration taskBiomedical literatureHealth recordsSupervised classificationPrivacyTaskAppli-cationTextDigital healthData sourcesExponential growthLanguageEvolving spacesSpeech and language patterns in autism: Towards natural language processing as a research and clinical tool
Trayvick J, Barkley S, McGowan A, Srivastava A, Peters A, Cecchi G, Foss-Feig J, Corcoran C. Speech and language patterns in autism: Towards natural language processing as a research and clinical tool. Psychiatry Research 2024, 340: 116109. PMID: 39106814, PMCID: PMC11371491, DOI: 10.1016/j.psychres.2024.116109.Peer-Reviewed Original ResearchAutism spectrum disorderNatural language processingLanguage differencesLanguage patternsLanguage processingCharacteristics of autism spectrum disorderQuantitative measures of speechRate of speechMeasures of speechHeterogeneity of findingsLinguistic phenotypeLinguistic researchProsodic differencesLanguage idiosyncrasiesSpectrum disorderLanguage featuresLanguageNatural languageSpeechSubjective ratingsAutismReciprocal conversionAbnormal rangePotential implicationsClinical toolInter-modality source coupling: a fully automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in large-scale (N~8K) analysis
Kotoski A, Morris R, Calhoun V. Inter-modality source coupling: a fully automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in large-scale (N~8K) analysis. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039662, DOI: 10.1109/embc53108.2024.10781720.Peer-Reviewed Original ResearchExtracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner J, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clinical Cancer Informatics 2024, 8: e2300166. PMID: 38885475, PMCID: PMC12032536, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchConceptsNatural language processingDomain-specific language modelsNatural language processing systemsInformation extraction systemRule-based moduleNarrative clinical textsNLP tasksEntity recognitionText normalizationAssertion classificationLanguage modelInformation extractionClinical textElectronic health recordsLearning-basedClinical notesLanguage processingTest setSystem performanceHealth recordsResponse extractionTime-consumingAnticancer therapyInformationAssessment informationRelation Extraction
Devarakonda M, Raja K, Xu H. Relation Extraction. Cognitive Informatics In Biomedicine And Healthcare 2024, 101-135. DOI: 10.1007/978-3-031-55865-8_5.Peer-Reviewed Original Research
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