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Articles de revues sur le sujet « OpenAI o3 »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Alsaudi, Esraa M., Sireen A. Shilbayeh, and Rana K. Abu-Farha. "Benchmarking ChatGPT-3.5 and OpenAI o3 Against Clinical Pharmacists: Preliminary Insights into Clinical Accuracy, Sensitivity, and Specificity in Pharmacy MCQs." Healthcare 13, no. 14 (2025): 1751. https://doi.org/10.3390/healthcare13141751.

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Objective: This proof-of-concept study aimed to evaluate and compare the clinical performance of two AI language models (ChatGPT-3.5 and OpenAI o3) in answering clinical pharmacy multiple-choice questions (MCQs), benchmarked against responses from specialist clinical pharmacists in Jordan, including academic preceptors and hospital-based clinicians. Methods: A total of 60 clinical pharmacy MCQs were developed based on current guidelines across four therapeutic areas: cardiovascular, endocrine, infectious, and respiratory diseases. Each item was reviewed by academic and clinical experts and the

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Wan, Boqiao. "Logical Reasoning Capabilities of Large Language Models: A Comparative Evaluation on GPQA Dataset." Applied and Computational Engineering 177, no. 1 (2025): 91–102. https://doi.org/10.54254/2755-2721/2025.bj25665.

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Recent advancements in Large Language Models (LLMs) have significantly enhanced their logical reasoning capabilities, presenting new opportunities for applications in scientific reasoning tasks. This study systematically evaluates and compares the logical reasoning performance of five prominent LLMsGPT-4o, OpenAI-o3, OpenAI-o3 pro, DeepSeek V3, and DeepSeek R1using the GPQA dataset, a standardized collection of science-related multiple-choice questions spanning biology, chemistry, and physics. Three dimensions were analyzed: overall accuracy, response time, and performance across difficulty le

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Christakis, Nicholas, and Dimitris Drikakis. "Evaluating Large Language Models in Code Generation: INFINITE Methodology for Defining the Inference Index." Applied Sciences 15, no. 7 (2025): 3784. https://doi.org/10.3390/app15073784.

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This study introduces a new methodology for an Inference Index (InI) called the Inference Index In Testing Model Effectiveness methodology (INFINITE), aiming to evaluate the performance of Large Language Models (LLMs) in code generation tasks. The InI index provides a comprehensive assessment focusing on three key components: efficiency, consistency, and accuracy. This approach encapsulates time-based efficiency, response quality, and the stability of model outputs, offering a thorough understanding of LLM performance beyond traditional accuracy metrics. We apply this methodology to compare Op

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Schlesinger, Maik, Marcus Weber, Steffen Schulze, Michael Hietschold та Michael Mehring. "Metastable β-Bi2 O3 Nanoparticles with Potential for Photocatalytic Water Purification Using Visible Light Irradiation". ChemistryOpen 2, № 4 (2013): 146–55. http://dx.doi.org/10.1002/open.201300013.

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Schlesinger, Maik, Marcus Weber, Steffen Schulze, Michael Hietschold та Michael Mehring. "Metastable β-Bi2 O3 Nanoparticles with Potential for Photocatalytic Water Purification Using Visible Light Irradiation". ChemistryOpen 2, № 4 (2013): 126. http://dx.doi.org/10.1002/open.201300032.

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Huzlík, Jiří, Jitka Hegrová, Karel Effenberger, Roman Ličbinský, and Martin Brtnický. "Air Quality in Brno City Parks." Atmosphere 11, no. 5 (2020): 510. http://dx.doi.org/10.3390/atmos11050510.

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Parks embody an important element of urban infrastructure and a basic type of public space that shapes the overall character of a city. They form a counterweight to built-up areas and public spaces with paved surfaces. In this context, parks compensate for the lack of natural, open landscapes in cities and thus have a fundamental impact on the quality of life of their inhabitants. For this reason, it is important to consider the quality of the environment in urban parks, air quality in particular. Concentrations of gaseous pollutants, namely, nitric oxide (NO), nitrogen dioxide (NO2), and ozon

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Yan, Zijun, Ke-qin Fan, Qi Zhang, et al. "Comparative analysis of the performance of the large language models DeepSeek-V3, DeepSeek-R1, open AI-O3 mini and open AI-O3 mini high in urology." World Journal of Urology 43, no. 1 (2025). https://doi.org/10.1007/s00345-025-05757-4.

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Abstract Objectives We sought to compare how DeepSeek‑V3, DeepSeek‑R1, OpenAI o3‑mini, and OpenAI o3‑mini high handle urological questions, especially in areas such as benign prostatic enlargement, urinary stones, infections, and guideline updates. The intent was to identify how these text‑creation platforms might aid clinical practice without overlooking potential gaps in accuracy. Methods A set of 34 routinely asked questions plus 25 queries based on newly revised guidelines was assembled. Six board‑certified urologists independently scored each system’s replies using a five‑point scale. Que

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Lin, Zhiwu, Yuanyuan Li, Min Wu, et al. "Performance analysis of large language models Chatgpt-4o, OpenAI O1, and OpenAI O3 mini in clinical treatment of pneumonia: a comparative study." Clinical and Experimental Medicine 25, no. 1 (2025). https://doi.org/10.1007/s10238-025-01743-7.

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Xu, Pusheng, Yue Wu, Kai Jin, Xiaolan Chen, Mingguang He, and Danli Shi. "DeepSeek-R1 Outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in Bilingual Complex Ophthalmology Reasoning." Advances in Ophthalmology Practice and Research, May 2025. https://doi.org/10.1016/j.aopr.2025.05.001.

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Carlson, Kristen W. "Review: Large language Model-Powered AI Systems Achieve Self-Replication with No Human Intervention." SuperIntelligence - Robotics - Safety & Alignment 2, no. 2 (2025). https://doi.org/10.70777/si.v2i2.14607.

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Self-replication with no human intervention is broadly recognized as one of the principal red lines associated with frontier AI systems. While leading corporations such as OpenAI and Google DeepMind have assessed GPT-o3-mini and Gemini on replication-related tasks and concluded that these systems pose a minimal risk regarding self-replication, our research presents novel findings. Following the same evaluation protocol, we demonstrate that 11 out of 32 existing AI systems under evaluation already possess the capability of self-replication. In hundreds of experimental trials, we observe a non-t

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Carlson, Kristen. "Review: Large Language Model-Powered AI Systems Achieve Self-Replication with No Human Intervention." SuperIntelligence - Robotics - Safety & Alignment 2, no. 2 (2025). https://doi.org/10.70777/si.v2i2.14689.

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Self-replication with no human intervention is broadly recognized as one of the principal red lines associated with frontier AI systems. While leading corporations such as OpenAI and Google DeepMind have assessed GPT-o3-mini and Gemini on replication-related tasks and concluded that these systems pose a minimal risk regarding self-replication, our research presents novel findings. Following the same evaluation protocol, we demonstrate that 11 out of 32 existing AI systems under evaluation already possess the capability of self-replication. In hundreds of experimental trials, we observe a non-t

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Sarkisian MD, Saro, Rozee Junhan Liu, Eddie Liu, and Anna Forsythe. "A living systematic literature review (L-SLR) for non–small-cell lung (NSCLC), prostate (PC), and breast cancer (BC), built with an agentic text annotation system powered by large language models (LLM) to assist treatment decision making." Journal of Clinical Oncology 43, no. 16_suppl (2025). https://doi.org/10.1200/jco.2025.43.16_suppl.e13657.

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e13657 Background: With demanding schedules, oncologists often struggle to stay updated on the latest clinical trial data from publications, especially without access to pre-curated databases. Traditionally, building annotated clinical trial databases required extensive time and manual effort. To address these challenges, we explored assembling an L-SLR library of clinical trial data using extractions generated by an agentic LLM system and evaluated the system’s accuracy and associated time savings. Methods: Agentic LLM systems are autonomous systems where multiple LLMs maintain control over h

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Rowland, Olawale Emmanuel. "Comparative analysis of meteorological parameters and their relationship with NO2, PM10, PM2.5 and O3 concentrations at selected urban air quality monitoring stations in Krakow, Paris, and Milan." Discover Environment 2, no. 1 (2024). http://dx.doi.org/10.1007/s44274-024-00060-2.

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AbstractMeteorological parameters play a major role in air pollutant concentrations as they create conditions that either hinder or facilitate the reaction and dispersion of pollutants in our environments. This is particularly evident in Europe, where frequent alternation of meteorological parameters has the potential to significantly impact pollutant concentrations. This study applied the R openair package to comparatively analyse the relationship between key meteorological parameters and NO2, O3, PM2.5, and PM10 concentrations measured at selected air quality monitoring stations in Krakow, M

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Fieldhouse, Rachel. "OpenAI’s o3 tops new AI league table for answering scientific questions." Nature, July 10, 2025. https://doi.org/10.1038/d41586-025-02177-7.

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Jones, Nicola. "How should we test AI for human-level intelligence? OpenAI’s o3 electrifies quest." Nature, January 14, 2025. https://doi.org/10.1038/d41586-025-00110-6.

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Phoon, K. "Trustworthy data-centric geotechnics." Trustworthy data-centric geotechnics 1 (February 28, 2025). https://doi.org/10.1016/j.geoai.2025.100008.

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There are two major technology related changes that are widely viewed as seismic. One is the emergence of artificial intelligence (AI) that is close to passing the Turing test. The second is the introduction of the first AI Act in the European Union (EU). The former portends business opportunities that are too large to ignore in all industries. The latter mandates businesses to manage risks appropriately depending on the impact of AI systems on individuals and society. Machine learning (ML) and AI are at most deployed as proofs of concept in geotechnics. This perspectives article advocates a g

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Yorkor, B., T. G. Leton, and J. N. Ugbebor. "Analysis of Temporal Variations of Air Pollutant Concentrations in Ogoni Area, Niger Delta Region, Nigeria." Asian Journal of Environment & Ecology, November 3, 2021, 63–73. http://dx.doi.org/10.9734/ajee/2021/v16i430260.

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This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at

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