2024
Release of complex imaging reports to patients, do radiologists trust AI to help?
Amin K, Davis M, Naderi A, Forman H. Release of complex imaging reports to patients, do radiologists trust AI to help? Current Problems In Diagnostic Radiology 2024, 54: 147-150. PMID: 39676024, DOI: 10.1067/j.cpradiol.2024.12.008.Peer-Reviewed Original ResearchRadiology reportsArtificial intelligenceImprove patient comprehensionAcademic medical centerEight-question surveyManual checkingPatient portalsCentury Cures ActArtificial intelligence systemsArtificial intelligence technologyPatient comprehensionOpinions of radiologistsClinical fellowsIntelligent systemsCures ActMedical CenterImaging ReportingIntelligence technologyRadiology attendingsContact informationPatientsRadiologistsRadiologyShould I Help?: A Skill-Based Framework for Deciding Socially Appropriate Assistance in Human-Robot Interactions
Ramnauth R, Brščić D, Scassellati B. Should I Help?: A Skill-Based Framework for Deciding Socially Appropriate Assistance in Human-Robot Interactions. 2024, 00: 2051-2058. DOI: 10.1109/ro-man60168.2024.10731350.Peer-Reviewed Original ResearchDesigning medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility
Oikonomou E, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic Journal Of Cardiology 2024, 81: 9-17. PMID: 39025234, DOI: 10.1016/j.hjc.2024.07.003.Peer-Reviewed Original ResearchArtificial intelligenceMedical artificial intelligence systemsDesigning AI systemsMachine learning systemsArtificial intelligence systemsBenefits of AIIntelligent systemsAI systemsLearning systemEnd-usersData typesAI developmentInteroperabilityTemporal settingAccessScalabilityTreatment of cardiovascular diseasesIntelligenceSystemMachineQuality assuranceInternational cohortCardiovascular diseaseObstacles407 IMPACT OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR UPPER GASTROINTESTINAL BLEEDING ON CLINICIAN TRUST AND LEARNING USING LARGE LANGUAGE MODELS: A RANDOMIZED PILOT SIMULATION STUDY
Chung S, Rajashekar N, Pu Y, Shin Y, Giuffrè M, Chan C, You K, Saarinen T, Hsiao A, Sekhon J, Wong A, Evans L, McCall T, Kizilcec R, Laine L, Shung D. 407 IMPACT OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR UPPER GASTROINTESTINAL BLEEDING ON CLINICIAN TRUST AND LEARNING USING LARGE LANGUAGE MODELS: A RANDOMIZED PILOT SIMULATION STUDY. Gastroenterology 2024, 166: s-95-s-96. DOI: 10.1016/s0016-5085(24)00715-7.Peer-Reviewed Original Research
2020
Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies
Raciti P, Sue J, Ceballos R, Godrich R, Kunz J, Kapur S, Reuter V, Grady L, Kanan C, Klimstra D, Fuchs T. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Modern Pathology 2020, 33: 2058-2066. PMID: 32393768, PMCID: PMC9235852, DOI: 10.1038/s41379-020-0551-y.Peer-Reviewed Original ResearchConceptsWhole slide imagesProstate needle core biopsiesArtificial intelligence systemsNeedle core biopsyIntelligent systemsCore biopsyProstate cancerState-of-the-artDetection of prostate cancerMachine learning algorithmsCore needle biopsyHigh test accuracyLow grade tumorsWell-differentiated cancersStained with hematoxylin and eosinAverage sensitivityLearning algorithmsHematoxylin and eosinGenitourinary pathologistsGrade tumorsNeedle biopsyDetection systemPathological diagnosisStatistically significant changesAncillary studiesStigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence
Walsh CG, Chaudhry B, Dua P, Goodman KW, Kaplan B, Kavuluru R, Solomonides A, Subbian V. Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence. JAMIA Open 2020, 3: 9-15. PMID: 32607482, PMCID: PMC7309258, DOI: 10.1093/jamiaopen/ooz054.Peer-Reviewed Original Research
2019
Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence
Ye W, Gu W, Guo X, Yi P, Meng Y, Han F, Yu L, Chen Y, Zhang G, Wang X. Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence. BioMedical Engineering OnLine 2019, 18: 6. PMID: 30670024, PMCID: PMC6343356, DOI: 10.1186/s12938-019-0627-4.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsDatabases, FactualDeep LearningDiagnosis, Computer-AssistedFalse Positive ReactionsHumansImage Processing, Computer-AssistedLungLung NeoplasmsRadiographic Image Interpretation, Computer-AssistedRadiologyReproducibility of ResultsSensitivity and SpecificitySolitary Pulmonary NoduleTomography, X-Ray ComputedConceptsDeep learningF-scoreLung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) databaseMethod of deep learningArtificial intelligence systemsInput imageNetwork trainingDetect pulmonary nodulesIdentification of lung nodulesIntelligent systemsNodule classificationGround-glass opacity imagesPreprocessing methodsDetect ground-glass opacitiesEvaluation resultsLearningAccuracyAlexNetResNet50PreprocessingImagesThree-dimensional featuresGround-glass opacitiesNetworkFeatures
1998
Alternative Essences of Intelligence
Brooks R, Breazeal C, Irie R, Kemp C, Marjanovic M, Scassellati B, Williamson M. Alternative Essences of Intelligence. 1998 DOI: 10.21236/ada457180.Peer-Reviewed Original Research
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