AI in Academic Writing: Ally or Foe? DOI Open Access
Arthur William Fodouop Kouam

International Journal of Research Publications, Journal Year: 2024, Volume and Issue: 148(1)

Published: April 16, 2024

This paper delves into the burgeoning field of AI in academic writing, exploring complex interplay between technology and scholarly communication. By examining advantages, limitations, ethical considerations employing tools this study sheds light on potential impact writing processes work quality. The findings offer a nuanced understanding benefits using such as enhanced efficiency, accuracy, accessibility, alongside challenges posed by lack subjectivity, bias algorithms, overreliance technology. Ethical surrounding use including plagiarism detection, data privacy, fairness, are also discussed. contributes to existing literature providing comprehensive analysis implications practices, highlighting need for collaboration, education, responsible promoting integrity innovation. Future research avenues suggested further explore critical thinking, develop guidelines academia, examine effectiveness diverse genres. underscores importance navigating complexities harness its while upholding standards fostering culture technological

Language: Английский

A review of top cardiology and cardiovascular medicine journal guidelines regarding the use of generative artificial intelligence tools in scientific writing DOI

Maha Inam,

Sana Sheikh, Abdul Mannan Khan Minhas

et al.

Current Problems in Cardiology, Journal Year: 2024, Volume and Issue: 49(3), P. 102387 - 102387

Published: Jan. 5, 2024

Language: Английский

Citations

22
Exploring the Impact of Generative AI on Peer Review: Insights from Journal Reviewers DOI Creative Commons
Saman Ebadi, Hassan Nejadghanbar,

Ahmed Rawdhan Salman

et al.

Journal of Academic Ethics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

Citations

2
Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review DOI Creative Commons
Jing Miao, Charat Thongprayoon, Supawadee Suppadungsuk

et al.

Clinics and Practice, Journal Year: 2023, Volume and Issue: 14(1), P. 89 - 105

Published: Dec. 30, 2023

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field nephrology academia. However, this advancement also given rise to ethical challenges, notably in scholarly writing. AI’s capacity automate labor-intensive tasks like literature reviews and data analysis created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives a range dilemmas that not only question authenticity contemporary endeavors but challenge credibility peer-review process integrity editorial oversight. Instances misconduct are highlighted, spanning from lesser-known journals reputable ones, even infiltrating graduate theses grant applications. subtle AI intrusion hints at systemic vulnerability within publishing domain, exacerbated by publish-or-perish mentality. solutions aimed mitigating employment academia include adoption sophisticated AI-driven plagiarism detection systems, robust augmentation an “AI scrutiny” phase, comprehensive training academics on usage, promotion culture transparency acknowledges role research. review underscores pressing need collaborative efforts among institutions foster environment application, thus preserving esteemed face rapid technological advancements. It makes plea rigorous research assess extent involvement literature, evaluate effectiveness AI-enhanced tools, understand long-term consequences utilization An example framework been proposed outline approach integrating Nephrology writing peer review. Using proactive initiatives evaluations, harmonious harnesses capabilities while upholding stringent standards can be envisioned.

Language: Английский

Citations

33
Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis DOI
Huizi Yu, Lizhou Fan, Lingyao Li

et al.

Journal of Healthcare Informatics Research, Journal Year: 2024, Volume and Issue: 8(4), P. 658 - 711

Published: Sept. 14, 2024

Language: Английский

Citations

6
Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest DOI
Gisele Sampaio Silva, Rohan Khera, Lee H. Schwamm

et al.

Stroke, Journal Year: 2024, Volume and Issue: 55(10), P. 2573 - 2578

Published: Sept. 3, 2024

Artificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality the nature authorship, we conducted a competitive essay contest in 2024 with both human AI participants. Human authors 4 distinct LLMs generated on controversial topics stroke care outcomes research. A panel

Language: Английский

Citations

5
Rethinking value in science: Moving beyond the stigma of artificial intelligence in academic communication DOI Creative Commons

Carlos González Morcillo

Anales de Pediatría (English Edition), Journal Year: 2025, Volume and Issue: unknown, P. 503696 - 503696

Published: Jan. 1, 2025

Language: Английский

Citations

0
Demographics and Utilization of an Addiction Helpline for Concerned Significant Others: Observational Study (Preprint) DOI Creative Commons
Rachel Chernick, A Sy, Sarah Dauber

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e55621 - e55621

Published: March 6, 2025

Background Concerned significant others (CSOs) play a role in supporting individuals with substance use disorders. There is lack of tailored support services for these CSOs, despite their substantial contributions to the well-being loved ones (LOs). The emergence helplines as potential avenue CSO outlined, culminating focus on Partnership End Addiction’s helpline service, an innovative public health intervention aimed at aiding CSOs concerned about LO’s use. Objective article analyzes demographics and patterns highlighting critical such services, advocating expanded, models. Methods This observational study draws data from 8 platforms spanning April 2011 December 2021, encompassing 24,096 client records. Surveys were completed by specialists during synchronous telephone calls or self-reported before engagement. Collected information encompasses demographics, interaction language, concern, CSO-LO relationship, “use state,” that is, location continuum Results primarily comprised women (13,980/18,373, 76.1%) seeking children (1062/1542, 68.9%). LOs mostly male (1090/1738, 62.7%), aged 18-25 years (2380/7208, 33%), primary concerns being cannabis (5266/12,817, 40.9%), opioids (2445/12,817, 19%), stimulants (1563/12,817, 12.1%). sought aid struggling substances who not treatment (1102/1753, 62.9%). majority looking English (14,738/17,920, 82.2%), while rest (3182/17,920, 17.8%) preferred communicate Spanish. Spanish-speaking significantly more likely call (n=963, 53.7% vs n=4026, 38.6%) (n=304, 16.9% n=1185, 11.3%) than English-speaking (P<.001). On other hand, be (n=2215, 21.3% n=94, 5.2%; P<.001). Conclusions illuminates helpline’s pioneering grappling It highlights crucial resources revealing key demographic, substance-related, use-state trends. dominant presence among users aligns reflects traditional caregiving roles. While parents form percentage those reaching out, also siblings, friends, family members, emphasizing need assistance members social network. individuals’ outreach underscores necessity bilingual services. Substance revolve around cannabis, opioids, stimulants, influenced age language preferences. serves essential intermediary filling gap between acute crisis formalized care Overall, this tailored, accessible

Language: Английский

Citations

0
Formalistic data and code availability policy in high-profile medical journals and pervasive policy-practice gaps in published articles: A meta-research study DOI
Wei Li, Xuerong Liu, Qianyu Zhang

et al.

Accountability in Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 25

Published: March 25, 2025

Poor data and code (DAC) sharing undermines open science principles. This study evaluates the stringency of DAC availability policies in high-profile medical journals identifies policy-practice gaps (PPG) published articles. 931 Q1 (Clarivate JCR 2021) were evaluated, with PPGs quantified across 3,191 articles from The BMJ, JAMA, NEJM, Lancet. Only 9.1% (85/931) mandated statements, 70.6% these lacking mechanisms to verify authenticity, 61.2% allowing publication despite invalid sharing. Secondary analysis revealed a disproportionate distribution subspecialties, 18.6% (11/59) subspecialties having >20% policies. Journal impact factors exhibited positive correlations statement (ρ = 0.20, p < 0.001) but not 0.01, 0.737). Among articles, observed over 90% cases. Specifically, 33.7% lacked 23.3% refused (58.4% which without justification public statements), 13.5% declared sharing, 39.0% being unreachable. Finally, only 0.5% achieved full computational reproducibility. Formalistic prevalent undermine transparency, necessitating supportive ecosystem that empowers authors uphold scientific responsibility integrity.

Language: Английский

Citations

0
Ensuring peer review integrity in the era of large language models: A critical stocktaking of challenges, red flags, and recommendations DOI Creative Commons
Burak Koçak,

Mehmet Ruhi Onur,

Seong Ho Park

et al.

Published: April 1, 2025

Language: Английский

Citations

0
A survey analysis of the adoption of large language models among pathologists DOI
Thiyaphat Laohawetwanit, Daniel Pinto, Andrey Bychkov

et al.

American Journal of Clinical Pathology, Journal Year: 2024, Volume and Issue: unknown

Published: July 27, 2024

Abstract Objectives We sought to investigate the adoption and perception of large language model (LLM) applications among pathologists. Methods A cross-sectional survey was conducted, gathering data from pathologists on their usage views concerning LLM tools. The survey, distributed globally through various digital platforms, included quantitative qualitative questions. Patterns in respondents’ perspectives these artificial intelligence tools were analyzed. Results Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT (OpenAI), for professional purposes, predominantly information retrieval, proofreading, academic writing, drafting pathology reports, highlighting a significant time-saving benefit. Academic demonstrated better level understanding LLMs than peers. Although chatbots sometimes provided incorrect general domain information, they considered moderately proficient pathology-specific knowledge. technology mainly used educational materials programming tasks. most sought-after feature image analysis capabilities. Participants expressed concerns about accuracy, privacy, need regulatory approval. Conclusions Large are gaining notable acceptance pathologists, with nearly half respondents indicating less year after tools’ introduction market. They see benefits but also worried reliability, ethical implications, security.

Language: Английский

Citations

3