Results 251 to 260 of about 36,775 (295)
An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance. [PDF]
Alu FF, Oluwadare S.
europepmc +1 more source
Human‐in‐the‐Loop Object Segmentation for 3D Gaussian Splatting via Finger‐based VR Interface
This study introduces a human‐in‐the‐loop segmentation framework for 3D Gaussian Splatting that integrates real‐time optimization with intuitive VR‐based finger prompting. Compared with existing automatic, learning‐based methods, it achieves significantly higher accuracy and reduced segmentation time.
Yongseok Lee +5 more
wiley +1 more source
Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency. [PDF]
Rewthamrongsris P +5 more
europepmc +1 more source
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users. [PDF]
Hu M +6 more
europepmc +1 more source
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti +4 more
wiley +1 more source
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna +2 more
wiley +1 more source
Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction
This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin +3 more
wiley +1 more source
Analysis Model for Infant Incubator Adverse Events Using Retrieval-Augmented Generation Combined With Dual-Adapter Fine-Tuning: Development and Evaluation Study. [PDF]
Xia W +5 more
europepmc +1 more source

