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 biasesA Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-resolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imaging