The field of healthcare is witnessing a significant shift towards the development of interpretable AI models, with a focus on transparency and explainability. Recent research has shown that incorporating feature interactions, graph-based explainable AI, and prototype-based reasoning can improve the accuracy and reliability of predictive models. Furthermore, the integration of numerical features with temporal logic rules has enhanced the interpretability of temporal point processes. The use of active learning and parsimonious dataset construction methods has also reduced the need for extensive labeling, making deep learning applications more feasible in the medical context. Notable papers in this area include MedRep, which provides a novel approach to medical concept representation, and ProtoECGNet, which offers a transparent and faithful explanation mechanism for ECG classification. Additionally, the development of tabular foundation models, such as TabPFN v2 and TabICL, has shown promising results in detecting empathy from visual cues. Overall, these advancements have the potential to improve patient outcomes and enhance clinical decision-making.
Advances in Interpretable AI for Healthcare
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Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI
MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification
Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines
Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Tabular foundation model to detect empathy from visual cues
DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction
Interpretable Hybrid-Rule Temporal Point Processes
Interpretable AI-driven Guidelines for Type 2 Diabetes Treatment from Observational Data
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation