Bias in medical AI: Implications for clinical decision-making
Cross J, Choma M, Onofrey J. Bias in medical AI: Implications for clinical decision-making. PLOS Digital Health 2024, 3: e0000651. PMID: 39509461, PMCID: PMC11542778, DOI: 10.1371/journal.pdig.0000651.Peer-Reviewed Original ResearchMedical AIArtificial intelligenceClinical decision-makingSupervised learning modelsMedical artificial intelligenceDiverse data setsSocial determinants of healthDeployment solutionDeterminants of healthAI algorithmsData featuresDebiasing methodsPerformance metricsLearning modelsAI lifecycleAI developmentModel interpretationData setsSuboptimal performanceModel's clinical utilityHealthcare disparitiesSocial determinantsCare practicesDecision-makingImplicit cognitive biasesMonte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning
Zeevi T, Venkataraman R, Staib L, Onofrey J. Monte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635511.Peer-Reviewed Original ResearchArtificial neural networkState-of-the-artMedical image dataPredictive uncertainty estimationBiomedical image dataImage dataOptimal artificial neural networkMC dropoutDropout approachSource-codeDrop-connectDeep learningNeural networkSignal spaceMonte-CarloPrediction uncertaintyUncertainty estimationDiverse setComprehensive comparisonPrediction scenariosDeepPosterior predictive distributionRepositoryDecision-makingNetwork