AI Chat Bots such as ChatGPT are revolutionizing our AI capabilities, especially in text generation, to help expedite many tasks, but they introduce new dilemmas. The detection of AI-generated text has become a subject of great debate considering the AI text detector's known and unexpected limitations. Thus far, much research in this area has focused on the detection of AI-generated text; however, the goal of this study was to evaluate the opposite scenario, an AI-text detection tool's ability to discriminate human-generated text. Thousands of abstracts from several of the most well-known scientific journals were used to test the predictive capabilities of these detection tools, assessing abstracts from 1980 to 2023. We found that the AI text detector erroneously identified up to 8% of the known real abstracts as AI-generated text. This further highlights the current limitations of such detection tools and argues for novel detectors or combined approaches that can address this shortcoming and minimize its unanticipated consequences as we navigate this new AI landscape.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10727991 | PMC |
| http://dx.doi.org/10.1016/j.jpi.2023.100342 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Improving readability in AI-generated medical information on fragility fractures: the role of prompt wording on ChatGPT's responses.
Osteoporos Int
January 2025
Division of Occupational Therapy, Faculty of Health Sciences, Kutahya Health Sciences University, Kutahya, Turkey.
Unlabelled: Understanding how the questions used when interacting with chatbots impact the readability of the generated text is essential for effective health communication. Using descriptive queries instead of just keywords during interaction with ChatGPT results in more readable and understandable answers about fragility fractures.
Purpose: Large language models like ChatGPT can enhance patients' understanding of medical information, making health decisions more accessible.
Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences.
J Allergy Clin Immunol Glob
February 2025
Big Data Department, Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil.
Background: The use of artificial intelligence (AI) in scientific writing is rapidly increasing, raising concerns about authorship identification, content quality, and writing efficiency.
Objectives: This study investigates the real-world impact of ChatGPT, a large language model, on those aspects in a simulated publication scenario.
Methods: Forty-eight individuals representing 3 medical expertise levels (medical students, residents, and experts in allergy or dermatology) evaluated 3 blinded versions of an atopic dermatitis case report: one each human written (HUM), AI generated (AI), and combined written (COM).
Artificial intelligence (AI), defined as algorithms built to reproduce human behavior, has various applications in health care such as risk prediction, medical image classification, text analysis, and complex disease diagnosis. Due to the increasing availability and volume of data, especially from electronic health records, AI technology is expanding into all fields of nursing and medicine. As the health care system moves toward automation and computationally driven clinical decision-making, nurses play a vital role in bridging the gap between the technological output, the patient, and the health care team.
View Article and Find Full Text PDFGender and racial diversity Assumed by text-to-image generators in microsurgery and plastic surgery-related subspecialities.
J Hand Microsurg
January 2025
Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
Background: Since the release of ChatGPT by OpenAI in November 2022, generative artificial intelligence (AI) models have attracted significant attention in various fields, including surgery. These advancements have been particularly notable for creating highly detailed and contextually accurate images from textual prompts. A notable area of clinical application is the representation of surgeon demographics in various specialties, particularly in the context of microsurgery and plastic surgery-related subspecialties.
View Article and Find Full Text PDFStrategies for integrating ChatGPT and generative AI into clinical studies.
Blood Res
December 2024
Department of Surgery, Division of HBP Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
Large language models, specifically ChatGPT, are revolutionizing clinical research by improving content creation and providing specific useful features. These technologies can transform clinical research, including data collection, analysis, interpretation, and results sharing. However, integrating these technologies into the academic writing workflow poses significant challenges.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!