41 citations · 149 across the 15 of their papers we have counts for
16 papers
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting
Siyu Zhou, Jialin Ouyang, Fan Xia +8
Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monitoring methods typically contr…
The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
Eileanor LaRocco, Sarah Tan, Adarsh Subbaswamy +4
Autonomous AI systems are transitioning from advisory roles to autonomous ones for medication prescriptions. Recent U.S. bill H.R. 238 and Utah's prescription-renewal pilot program…
Knowledge-based anomaly detection for identifying network-induced shape artifacts
Rucha Deshpande, Tahsin Rahman, Miguel Lago +6
Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artif…
HistoART: Histopathology Artifact Detection and Reporting Tool
Seyed Kahaki, Alexander R. Webber, Ghada Zamzmi +3
In modern cancer diagnostics, Whole Slide Imaging (WSI) is widely used to digitize tissue specimens for detailed, high-resolution examination; however, other diagnostic approaches,…
Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework
Nathan Drenkow, Mitchell Pavlak, Keith Harrigian +5
Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparabl…
Scorecards for Synthetic Medical Data Evaluation and Reporting
Ghada Zamzmi, Adarsh Subbaswamy, Elena Sizikova +3
Although interest in synthetic medical data (SMD) for training and testing AI methods is growing, the absence of a standardized framework to evaluate its quality and applicability…