[HTML][HTML] A Human–AI interaction paradigm and its application to rhinocytology
Abstract This article explores Human-Centered Artificial Intelligence (HCAI) in medical
cytology, with a focus on enhancing the interaction with AI. It presents a Human–AI …
cytology, with a focus on enhancing the interaction with AI. It presents a Human–AI …
Explainable AI evaluation: a top-down approach for selecting optimal explanations for black box models
Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its
extensive adoption, and the catastrophic consequence of misinterpreting sensitive data …
extensive adoption, and the catastrophic consequence of misinterpreting sensitive data …
[HTML][HTML] Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms
AR Ashraf, A Somogyi-Végh, S Merczel… - Artificial Intelligence in …, 2024 - Elsevier
Background Preventable patient harm, particularly medication errors, represent significant
challenges in healthcare settings. Dispensing the wrong medication is often associated with …
challenges in healthcare settings. Dispensing the wrong medication is often associated with …
Predicting abnormal C-reactive protein level for improving utilization by deep neural network model
D Mo, S Xiong, T Ji, Q Zhou, Q Zheng - International Journal of Medical …, 2025 - Elsevier
Background C-reactive protein (CRP) is an inflammatory biomarker frequently used in
clinical practice. However, insufficient evidence-based ordering inevitably results in its …
clinical practice. However, insufficient evidence-based ordering inevitably results in its …
[HTML][HTML] Balancing accuracy and Interpretability: An R package assessing complex relationships beyond the Cox model and applications to clinical prediction
D Shamsutdinova, D Stamate, D Stahl - International Journal of Medical …, 2025 - Elsevier
Background Accurate and interpretable models are essential for clinical decision-making,
where predictions can directly impact patient care. Machine learning (ML) survival methods …
where predictions can directly impact patient care. Machine learning (ML) survival methods …
The use of machine learning for the prediction of response to follow-up in spine registries
A Baroncini, A Campagner, F Cabitza… - International Journal of …, 2025 - Elsevier
Background One of the main challenges in the maintenance of registries is to keep a high
follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study …
follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study …
[HTML][HTML] External validation of the computer aided risk scoring system in predicting in-hospital mortality following emergency medical admissions
V Kingsley, L Fox, D Simm, GP Martin… - International Journal of …, 2024 - Elsevier
Background Clinical prediction models have the potential to improve the quality of care and
enhance patient safety outcomes. A Computer-aided Risk Scoring system (CARSS) was …
enhance patient safety outcomes. A Computer-aided Risk Scoring system (CARSS) was …
Investigating Calibrated Classification Scores Through the Lens of Interpretability
A Torabian, R Urner - World Conference on Explainable Artificial …, 2024 - Springer
Calibration is a frequently invoked concept when useful label probability estimates are
required on top of classification accuracy. A calibrated model is a function whose values …
required on top of classification accuracy. A calibrated model is a function whose values …
[PDF][PDF] Answering the Call to Go Beyond Accuracy: An Online Tool for the Multidimensional Assessment of Decision Support Systems.
The research about, and use of, AI-based Decision Support Systems (DSS) has been
steadily increasing in the recent years: however, tools and techniques to validate and …
steadily increasing in the recent years: however, tools and techniques to validate and …