Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records Data collected in the multicentric PRAIS ...

In past roles, I’ve spent countless hours trying to understand why state-of-the-art models produced subpar outputs. The underlying issue here is that machine learning models don’t “think” like humans ...

Large language models (LLMs) such as GPT and Llama are driving exceptional innovations in AI, but research aimed at improving ...

Adnan and colleagues evaluated machine learning models’ ability to screen for Parkinson’s disease using self-recorded smile videos. 2. The models achieved high sensitivity and specificity among ...

Explainable AI plays a central role in validating model behavior. Using established explainability techniques, the study examines which financial variables drive fraud predictions. The results show a ...

The strong role of socioeconomic factors underscores the limits of purely spatial or technical solutions. While predictive models can identify where risk concentrates, addressing why it does so ...

The progression of glaucoma was accurately predicted by machine learning models based on structural, functional and vascular ...