2025
Artificial Intelligence–Guided Lung Ultrasound by Nonexperts
Baloescu C, Bailitz J, Cheema B, Agarwala R, Jankowski M, Eke O, Liu R, Nomura J, Stolz L, Gargani L, Alkan E, Wellman T, Parajuli N, Marra A, Thomas Y, Patel D, Schraft E, O’Brien J, Moore C, Gottlieb M. Artificial Intelligence–Guided Lung Ultrasound by Nonexperts. JAMA Cardiology 2025, 10: 245-253. PMID: 39813064, PMCID: PMC11904735, DOI: 10.1001/jamacardio.2024.4991.Peer-Reviewed Original ResearchThis study shows AI helps non-experts create expert-quality lung ultrasound images, which may improve healthcare diagnostics access in underserved areas.
2024
Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes
Bogaerts J, Steenbeek M, Bokhorst J, van Bommel M, Abete L, Addante F, Brinkhuis M, Chrzan A, Cordier F, Devouassoux‐Shisheboran M, Fernández‐Pérez J, Fischer A, Gilks C, Guerriero A, Jaconi M, Kleijn T, Kooreman L, Martin S, Milla J, Narducci N, Ntala C, Parkash V, de Pauw C, Rabban J, Rijstenberg L, Rottscholl R, Staebler A, Van de Vijver K, Zannoni G, van Zanten M, Bart J, Bentz J, Bosse T, Bulten J, Desouki M, Lastra R, Numan T, Schoolmeester J, Schwartz L, Shih I, Soong T, Turashvili G, Vang R, Volchek M, Aliredjo R, Kusters‐Vandevelde H, de Hullu J, Simons M, van der Laak J. Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes. The Journal Of Pathology Clinical Research 2024, 10: e70006. PMID: 39439213, PMCID: PMC11496567, DOI: 10.1002/2056-4538.70006.Peer-Reviewed Original ResearchConceptsDeep learning modelsSerous tubal intraepithelial carcinomaArtificial intelligenceAI assistanceDiagnosis of serous tubal intraepithelial carcinomaTubal intraepithelial carcinomaReview timeFallopian tubeIntraepithelial carcinomaAI supportHigh-grade serous ovarian carcinomaSerous ovarian carcinomaStandalone performanceAverage sensitivityGroup of pathologistsAccuracyOvarian carcinomaHistopathological diagnosisPathologist performanceMixed-model analysisDiagnostic certaintyCarcinomaDiagnostic setting
2018
Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability
Tajmir S, Lee H, Shailam R, Gale H, Nguyen J, Westra S, Lim R, Yune S, Gee M, Do S. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiology 2018, 48: 275-283. PMID: 30069585, DOI: 10.1007/s00256-018-3033-2.Peer-Reviewed Original ResearchConceptsBone age assessmentAutomated artificial intelligenceAI assistanceBone age radiographsConvolutional neural networkDeep learning algorithmsRoot mean square errorMean square errorPediatric radiologistsUtilization of AILearning algorithmsNeural networkArtificial intelligenceIntraclass correlation coefficientImproved performancePooled cohortRadiologist interpretationImaging studiesInter-rater variationAccuracyMetabolic disordersIncreased accuracyRadiologistsAge accuracyMeasures of accuracy
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