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Latest AI and machine learning research in surveys for healthcare professionals.

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InTec: integrated things-edge computing: a framework for distributing machine learning pipelines in edge AI systems

With the rapid expansion of the Internet of Things (IoT), sensors, smartphones, and wearables have...

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Biases in Edge Language Models: Detection, Analysis, and Mitigation

The integration of large language models (LLMs) on low-power edge devices such as Raspberry Pi, kn...

A Survey of LLM-based Agents in Medicine: How far are we from Baymax?

Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agen...

(1)
A Critical Review of Predominant Bias in Neural Networks

Bias issues of neural networks garner significant attention along with its promising advancement. ...

USER-VLM 360: Personalized Vision Language Models with User-aware Tuning for Social Human-Robot Interactions

The integration of vision-language models into robotic systems constitutes a significant advanceme...

Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias

Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English...

Immersive virtual games: winners for deep cognitive assessment

Studies of human cognition often rely on brief, controlled tasks emphasizing group-level effects b...

Exploring the Camera Bias of Person Re-identification

We empirically investigate the camera bias of person re-identification (ReID) models. Previously, ...

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Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing

Deep learning model effectiveness in classification tasks is often challenged by the quality and q...

A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective

Tabular data is one of the most widely used data formats across various domains such as bioinforma...

(1)
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation

Large Language Models (LLMs) struggle with hallucinations and outdated knowledge due to their reli...

SB-Bench: Stereotype Bias Benchmark for Large Multimodal Models

Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, underm...

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models

Background: Data collected in controlled settings typically results in high-quality datasets. Howe...

A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook

Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, signi...

HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses

Recent advances in large language models (LLMs) have shown promising improvements, often surpassin...

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Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders

While recent work has found that vision-language models trained under the Contrastive Language Ima...

Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenv...

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Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy

Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve ann...

(1)
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos

With the rapid development of AI-generated content (AIGC), the creation of high-quality AI-generat...

(2)
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