BACKGROUND
Large language models are increasingly used to obtain medical information, but their performance in juvenile systemic lupus erythematosus remains insufficiently characterized.
OBJECTIVE
To compare expert-rated response quality and clinical relevance across 4 artificial intelligence (AI) models using standa...
Ufuk Furkan Ozdemir, Serpil Meric Toprak, Aybüke Günalp et al.· Journal of clinical rheumato...· 0 citations
Large language models (LLMs) increasingly mediate how people read the news, but how deeply their evaluations are shaped by textual cues remains unclear. This study manipulated news coverage of the Columbia Gaza campus protests at two levels: a shallow, lexical level (adjective framing: left, right, neutral, original) a...
Bettina Ziqianqian Wang· Applied and Computational En...· 0 citations
Abstract Background In 2019, Germany introduced a unique regulatory framework for digital therapeutics (DTx), termed digital health applications (DiGAs) in Germany, with the goal of integrating evidence-based DTx into statutory health care. DTx are eligible for reimbursement by statutory health insurance if manufacture...
Benjamin Kinast, Henrik Rohde, Björn Schreiweis et al.· JMIR Human Factors· 0 citations
The rapid development of artificial intelligence (AI), especially the fast iteration and widespread deployment of large language models (LLMs), has ushered in a new paradigm for the intelligent augmentation of electronic test instruments and automatic test systems (ATS). By combining the powerful reasoning and generati...
Jie Hu, Yuanjun Li, Chunhui Li et al.· International Journal of Adv...· 0 citations
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Large language model (LLM)-based multi-agent systems face three open challenges in high-stakes decision support: hallucination control in structured output generation, coupling of semantic reasoning with deterministic numerical simulation, and end-to-end auditability of multi-step agentic workflows. We present
Mac...
Yan Liu, Jun-Yi Shangguan, Qian-Yi Wang et al.· Scientific Reports· 0 citations
This paper systematically examines bias in Large Language Models (LLMs) through the lens of Data Feminism. The research demonstrates that bias in LLMs is not an issue of technical neutrality, but rather a systematic reproduction of societal power structures embedded in data collection, algorithmic design, and output ge...
Lihao Wang· Applied and Computational En...· 0 citations
Large language models (LLMs) are increasingly used for qualitative analysis in substance use research, yet their performance relative to human coders remains underexplored. This study compares ChatGPT-4.0 with human coders in performing qualitative coding tasks using NIH grant abstracts as a test case, focusing on the...
Sarah A. Alkhatib, Nadiya Jiwa, Dallin Judd et al.· PLoS ONE· 0 citations
ABSTRACT Despite significant advances since 2023 in large language model (LLM) capabilities and the introduction of web‐enabled search functionality, citation hallucination remains a persistent problem in AI‐generated academic content. We test GPT‐4o, DeepSeek V3, web‐enabled GPT‐4o, and web‐enabled GPT‐5.1 across prom...
Joy A. Buchanan, Stephen E. Hill, Olga Shapoval· Southern Economic Journal· 0 citations
Security operations center (SOC) analysts must interpret large volumes of alerts under severe time pressure, and this burden grows more demanding when advanced persistent threat (APT) activity is distributed across endpoint telemetry, network indicators, host context, and threat intelligence. This paper presents SecLM,...
Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Yashar Mousavi et al.· SPIE Applications of Machine...· 0 citations
BACKGROUND As the population of survivors of cancer grows and new information technologies become widespread in Hong Kong, survivors of cancer increasingly seek ongoing care and support information from large language models (LLMs). While these tools provide immediate conversational responses, they carry substantial ri...
Haoyu An, Wanshu Huang, Rachel Tsoi Man Cheung et al.· Journal of Medical Internet...· 0 citations
This research examines whether large language model agents can produce analytically useful evidence for advertising testing when treated as synthetic respondents rather than as substitutes for human consumers. We report an in-silico comparative post-test of two television advertisements in the French telecommunications...
Marta Trales, Guillaume Hochard, Christophe Benavent· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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