Results 171 to 180 of about 97,157 (255)

Essential work, invisible workers: The role of digital curation in COVID‐19 Open Science

Journal of the Association for Information Science and Technology, Volume 76, Issue 4, Page 703-717, April 2025.
Abstract In this paper, we examine the role digital curation practices and practitioners played in facilitating open science (OS) initiatives amid the COVID‐19 pandemic. In Summer 2023, we conducted a content analysis of available information regarding 50 OS initiatives that emerged—or substantially shifted their focus—between 2020 and 2022 to address ...
Irene V. Pasquetto, Amina A. Abdu, Natascha Chtena   +2 more
wiley   +1 more source

Can social media provide early warning of retraction? Evidence from critical tweets identified by human annotation and large language models

Journal of the Association for Information Science and Technology, EarlyView.
Abstract Timely detection of problematic research is essential for safeguarding scientific integrity. To explore whether social media commentary can serve as an early indicator of potentially problematic articles, this study analyzed 3815 tweets referencing 604 retracted articles and 3373 tweets referencing 668 comparable non‐retracted articles. Tweets
Er‐Te Zheng, Hui‐Zhen Fu, Mike Thelwall, Zhichao Fang   +3 more
wiley   +1 more source

A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI. [PDF]

Quant Imaging Med Surg
Teng Z, Li L, Xin Z, Xiang D, Huang J, Zhou H, Shi F, Zhu W, Cai J, Peng T, Chen X.   +10 more
europepmc   +1 more source

Potential contribution of biomass gasification‐based technology in energy transition: a technical review coupled with bibliometric studies

Biofuels, Bioproducts and Biorefining, EarlyView.
Abstract Biomass gasification technology has been extensively researched around the world; however, there is a need to evaluate the current research landscape and evolutionary direction of research in the broader context of energy transition. A systematic bibliometric analysis of the Web of Science database was performed for articles that fall within ...
Olasunkanmi Opeoluwa Adeoye, Electo Eduardo Silva Lora, Rubenildo Vieira Andrade, Leonardo Peña Pupo, René Lesme Jaén, Osvaldo José Venturini   +5 more
wiley   +1 more source

Evaluating and leveraging large language models in clinical pharmacology and therapeutics assessment: From exam takers to exam shapers

British Journal of Clinical Pharmacology, EarlyView.
Aims In medical education, the ability of large language models (LLMs) to match human performance raises questions about their potential as educational tools. This study evaluates LLMs' performance on Clinical Pharmacology and Therapeutics (CPT) exams, comparing their results to medical students and exploring their ability to identify poorly formulated
Alexandre O. Gérard, Diane Merino, Marc Labriffe, Fanny Rocher, Delphine Viard, Laurence Zemori, Thibaud Lavrut, Erik M. Donker, Joost D. Piët, Jean‐Paul Fournier, Milou‐Daniel Drici, Alexandre Destere   +11 more
wiley   +1 more source

Implementing Trustworthy AI in VA High Reliability Health Care Organizations. [PDF]

Fed Pract
Isaacks DB, Borkowski AA.
europepmc   +1 more source

Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI. [PDF]

AI Soc
Sheir S, Manzini A, Smith H, Ives J.
europepmc   +1 more source

Student perspectives on AI‐supported formative assessment in pharmacology

British Journal of Clinical Pharmacology, EarlyView.
Abstract Aims High‐quality feedback is crucial for helping medical students understand and apply core concepts of pharmacology, yet personalized feedback is resource‐intensive to produce. Artificial intelligence (AI) offers a potential solution, but little is known about students' perspectives on AI‐generated feedback.
Jon Andsnes Berg, Øyvind Repstad, Trond Trætteberg Serkland, Tiril Egset Mork, Christian Bru, Olav Tenstad, Monika Kvernenes   +6 more
wiley   +1 more source

Stakeholder Perspectives on Trustworthy AI for Parkinson Disease Management Using a Cocreation Approach: Qualitative Exploratory Study.

J Med Internet Res
Alves B, Alhussein G, Riggare S, Duncan TS, Saad A, Lyreskog DM, Chatzichristos C, Gerasimou I, Hadjidimitriou S, Hadjileontiadis LJ, B Dias S, AI-PROGNOSIS Consortium.   +11 more
europepmc   +1 more source

TAI-PRM: trustworthy AI-project risk management framework towards Industry 5.0. [PDF]

AI Ethics
Vyhmeister E, Castane GG.
europepmc   +1 more source