Dependency on ChatGPT is characterized by excessive reliance on AI-driven conversational agents, such as ChatGPT, in the healthcare sector. This article explores the consequences of overreliance on AI chatbots like ChatGPT in healthcare settings. It discusses the increasing use of AI chatbots for patient consultations, information dissemination, and decision support, highlighting their potential benefits in improving healthcare delivery efficiency and patient outcomes. The editorial explores the factors contributing to ChatGPT Dependency Disorder among healthcare professionals, such as convenience, lack of training, and time constraints, and examines the challenges and benefits associated with integrating AI chatbots in clinical workflows. It emphasizes the importance of maintaining a human-centered approach alongside AI technologies to optimize patient care outcomes and emphasizes the need for responsible integration of AI chatbots in healthcare settings to ensure ethical standards and patient safety. This article concludes by calling for further research and strategies to address ChatGPT Dependency Disorder and promote a balanced approach to leveraging AI technology in healthcare practice.
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http://dx.doi.org/10.7759/cureus.66155 | DOI Listing |
J Intell
December 2024
Laboratoire de Psychologie et d'Ergonomie Appliquée (LaPEA), Université Paris Cité and Univ Gustave Eiffel, F-92100 Boulogne-Billancourt, France.
This study examined generative artificial intelligences (GenAIs), as popularized by ChatGPT, in standardized creativity tests. Benchmarking GenAI against human performance, the results showed that ChatGPT demonstrated remarkable fluency in content generation, though the creative output was average. The random nature of AI creativity and the dependency on the richness of the training database require a reassessment of traditional creativity metrics, especially for AI.
View Article and Find Full Text PDFJMIR Form Res
December 2024
School of Journalism and Communication, Beijing Normal University, Beijing, China.
Background: The proliferation of generative artificial intelligence (AI), such as ChatGPT, has added complexity and richness to the virtual environment by increasing the presence of AI-generated content (AIGC). Although social media platforms such as TikTok have begun labeling AIGC to facilitate the ability for users to distinguish it from human-generated content, little research has been performed to examine the effect of these AIGC labels.
Objective: This study investigated the impact of AIGC labels on perceived accuracy, message credibility, and sharing intention for misinformation through a web-based experimental design, aiming to refine the strategic application of AIGC labels.
JMIR Form Res
December 2024
Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany.
Background: The rapid development of large language models (LLMs) such as OpenAI's ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical licensing examination assistance. Recently, LLMs have been enhanced with image recognition capabilities.
View Article and Find Full Text PDFIndian J Radiol Imaging
January 2025
Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India.
The integration of large language models (LLMs) into medical education has received increasing attention as a potential tool to enhance learning experiences. However, there remains a need to explore radiology postgraduate students' engagement with LLMs and their perceptions of their utility in medical education. Hence, we conducted this study to investigate radiology postgraduate students' knowledge, attitudes, and practices regarding LLMs in medical education.
View Article and Find Full Text PDFbioRxiv
November 2024
Department of Genetics and Genomic Sciences, Department of Artificial Intelligence and Human Health, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
Protein abundance correlates only moderately with mRNA levels, and are modulated post-transcriptionally by a network of regulators including ribosomes, RNA-binding proteins (RBPs), and the proteasome. Here, we identified ster rotein abundance egulators (MaPRs) across ten cancer types by devising a new computational pipeline that jointly analyzed transcriptomes and proteomes from 1,305 tumor samples. We identified 232 to 1,394 MaPRs per cancer type, mediating up to 79% of post-transcriptional regulatory networks.
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