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Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure
Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions.
MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples
Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside.
Explanation Container in Case-Based Biomedical Question-Answering
The National Center for Advancing Translational Sciences(NCATS) Biomedical Data Translator (Translator) aims to attenuate problems faced by translational scientists.
Longitudinal Distance: Towards Accountable Instance Attribution
Previous research in interpretable machine learning (IML) and explainable artificial intelligence (XAI) can be broadly categorized as either focusing on seeking interpretability in the agent's model (i. e., IML) or focusing on the context of the user in addition to the model (i. e., XAI).
Data Representing Ground-Truth Explanations to Evaluate XAI Methods
The contributions of this paper include a methodology to create synthetic data representing ground-truth explanations, three data sets, an evaluation of LIME using these data sets, and a preliminary analysis of the challenges and potential benefits in using these data to evaluate existing XAI approaches.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science
We present a focused analysis of user studies in explainable artificial intelligence (XAI) entailing qualitative investigation.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
