Copertina

The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Schwabe, Daniel, Becker, Katinka, Seyferth, Martin, Klaß, Andreas, Schäffter, Tobias
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Metric Hub: A metric library and practical selection workflow for use-case-driven data quality assessment in medical AI
di: Becker, Katinka, et al.
Pubblicazione: (2026)

A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
di: Andrys-Olek, Justyna, et al.
Pubblicazione: (2026)

Self-organizing maps for water quality assessment in reservoirs and lakes: A systematic literature review
di: Almegdadi, Oraib, et al.
Pubblicazione: (2025)

Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare
di: Vallevik, Vibeke Binz, et al.
Pubblicazione: (2024)

EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification
di: Showrav, Tushar Talukder, et al.
Pubblicazione: (2025)

GENEOnet: Statistical analysis supporting explainability and trustworthiness
di: Bocchi, Giovanni, et al.
Pubblicazione: (2025)

Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
di: Schwabe, Tim, et al.
Pubblicazione: (2023)

AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
di: Spaanderman, Douwe J., et al.
Pubblicazione: (2024)

Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications
di: Schouten, Daan, et al.
Pubblicazione: (2024)

From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
di: Antonesi, Gabriel, et al.
Pubblicazione: (2025)

Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques
di: Bolchini, Cristiana, et al.
Pubblicazione: (2023)

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
di: Fidon, Lucas, et al.
Pubblicazione: (2022)

Fairboard: a quantitative framework for equity assessment of healthcare models
di: Ruffle, James K., et al.
Pubblicazione: (2026)

An evaluation framework for synthetic data generation models
di: Livieris, Ioannis E., et al.
Pubblicazione: (2024)

Safety challenges of AI in medicine in the era of large language models
di: Wang, Xiaoye, et al.
Pubblicazione: (2024)

Transfer learning for financial data predictions: a systematic review
di: Lanzetta, V.
Pubblicazione: (2024)

A theoretical framework for self-supervised contrastive learning for continuous dependent data
di: Marusov, Alexander, et al.
Pubblicazione: (2025)

I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers
di: Vashistha, Ritwik, et al.
Pubblicazione: (2025)

Detecting Malicious AI Agents Through Simulated Interactions
di: Pi, Yulu, et al.
Pubblicazione: (2025)

FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy
di: Chen, Jingyun, et al.
Pubblicazione: (2024)

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
di: Paterakis, George, et al.
Pubblicazione: (2025)

FastODT: A tree-based framework for efficient continual learning
di: Bretsko, Daniel, et al.
Pubblicazione: (2026)

Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction
di: Văduva, Alin-Gabriel, et al.
Pubblicazione: (2026)

From paintbrush to pixel: A review of deep neural networks in AI-generated art
di: Maerten, Anne-Sofie, et al.
Pubblicazione: (2023)

Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages
di: Khraisha, Qusai, et al.
Pubblicazione: (2023)

Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
di: Schlegel, Udo, et al.
Pubblicazione: (2024)

Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Moulding
di: Muaz, Muhammad, et al.
Pubblicazione: (2025)

Mechanistic understanding and validation of large AI models with SemanticLens
di: Dreyer, Maximilian, et al.
Pubblicazione: (2025)

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
di: Jennings, Charlotte, et al.
Pubblicazione: (2025)

AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field
di: Rudroff, Thorsten, et al.
Pubblicazione: (2024)

NeuroAI for AI Safety
di: Mineault, Patrick, et al.
Pubblicazione: (2024)

Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment
di: Kuhn, Martin, et al.
Pubblicazione: (2025)

Scientific Computing with Large Language Models
di: Culver, Christopher, et al.
Pubblicazione: (2024)

Graph Counterfactual Explainable AI via Latent Space Traversal
di: Hansen, Andreas Abildtrup, et al.
Pubblicazione: (2025)

Framing AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset
di: Fursin, Grigori, et al.
Pubblicazione: (2025)

An AI-driven framework for rapid and localized optimizations of urban open spaces
di: Eshraghi, Pegah, et al.
Pubblicazione: (2025)

Rethinking industrial artificial intelligence: a unified foundation framework
di: Lee, Jay, et al.
Pubblicazione: (2025)

Habits and goals in synergy: a variational Bayesian framework for behavior
di: Han, Dongqi, et al.
Pubblicazione: (2023)

A framework for measuring the training efficiency of a neural architecture
di: Cueto-Mendoza, Eduardo, et al.
Pubblicazione: (2024)

Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring
di: Luis, Samuel Yanes, et al.
Pubblicazione: (2024)