Format of emergency department electronic health records in Europe. The European initiative and the eCREAM proposal by Guido Bertolini, Giulia Irene Ghilardi, Chiara Pandolfini, Alessandro Bacchiega, Felice Catania, Bernardo Magnini, Peter Mitro, George Notas, Gregor Prosen, Pankaj Sharma, Jacek Górka, Giovanni Nattino

“…Second, by developing an advanced natural language processing tool tailored to the specific needs of emergency medicine to automatically extract accurate, structured data from the free texts contained in EHRs. …”
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Menstrual Health Education Using a Specialized Large Language Model in India: Development and Evaluation Study of MenstLLaMA by Prottay Kumar Adhikary, Isha Motiyani, Gayatri Oke, Maithili Joshi, Kanupriya Pathak, Salam Michael Singh, Tanmoy Chakraborty

“…The evaluation followed a multilayered framework: (1) automatic evaluation using standard natural language processing metrics (BLEU [Bilingual Evaluation Understudy], METEOR [Metric for Evaluation of Translation with Explicit Ordering], ROUGE-L [Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence], and BERTScore [Bidirectional Encoder Representations from Transformers Score]); (2) evaluation by clinical experts (N=18), who rated 200 expert-curated queries for accuracy and appropriateness; (3) medical practitioner interaction through the ISHA (Intelligent System for Menstrual Health Assistance) interactive chatbot, assessing qualitative dimensions (eg, relevance, understandability, preciseness, correctness,context sensitivity ResultsMenstLLaMA achieved the highest scores in BLEU (0.059) and BERTScore (0.911), outperforming GPT-4o (BLEU: 0.052, BERTScore: 0.896) and Claude-3 (BERTScore: 0.888). …”
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Impact of the COVID-19 Pandemic and the 2021 National Institute for Health and Care Excellence Guidelines on Public Perspectives Toward Myalgic Encephalomyelitis/Chronic Fatigue Sy... by Iliya Khakban, Shagun Jain, Joseph Gallab, Blossom Dharmaraj, Fangwen Zhou, Cynthia Lokker, Wael Abdelkader, Dena Zeraatkar, Jason W Busse

“…A Robustly Optimized Bidirectional Embedding Representations from Transformers Pretraining Approach (RoBERTa) language processing model was used to categorize the sentiment of tweets as positive, negative, or neutral. …”
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BERT and BERTopic for screening clinical depression on open-ended text messages collected through a mobile application from older adults by Moo-Kwon Chung, Sang Yup Lee, Taeksoo Shin, Ji Young Park, Sangwon Hwang, Min-Hyuk Kim, Jinhee Lee, Kyoung-Joung Lee, Hyo-Sang Lim, Erdenebayar Urtnasan, YeonSu Jung, Dan-Kyung Kim, Eunji Shin, Jin-kyung Lee

“…Pre-trained Bidirectional Encoder Representations from Transformers (BERT)-based Natural Language Processing (NLP) models were utilized, using Python and the Hugging Face Transformers. …”
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Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules—a systematic review and meta-analysis by Karishma Jassal, Karishma Jassal, Melissa Edwards, Afsaneh Koohestani, Afsaneh Koohestani, Wendy Brown, Jonathan W. Serpell, Jonathan W. Serpell, James C. Lee, James C. Lee

“…A total of 20 models were presented in the seven studies included, five of which were radiological driven, one utilised natural language processing, and one focused on cytology. The pooled meta-analysis incorporated 16 area under the curve (AUC) results derived from 15 models across three studies yielding a combined estimate of 0.82 (95% CI: 0.81–0.84) indicating moderate-to-good classification performance across machine learning (ML) and deep learning (DL) architectures. …”
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Evaluation of an Artificial Intelligence Model for Identification of Intracranial Hemorrhage Subtypes on Computed Tomography of the Head by James M. Hillis, Bernardo C. Bizzo, Isabella Newbury‐Chaet, Sarah F. Mercaldo, John K. Chin, Ankita Ghatak, Madeleine A. Halle, Eric L'Italien, Ashley L. MacDonald, Alex S. Schultz, Karen Buch, John Conklin, Stuart Pomerantz, Sandra Rincon, Keith J. Dreyer, William A. Mehan

“…The positive cases were selected on the basis of the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. …”
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Generalized domain prompt learning for accessible scientific vision-language models by Qinglong Cao, Yuntian Chen, Lu Lu, Hao Sun, Zhengzhong Zeng, Xiaokang Yang, Dongxiao Zhang

“…The framework integrates insights from machine learning, natural language processing, and domain-specific expertise, highlighting the interdisciplinary nature of the work.Long-term, GDPL aspires to democratize AI resources, bridging gaps between academia and industry. …”
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<tt>TrialSieve</tt>: A Comprehensive Biomedical Information Extraction Framework for PICO, Meta-Analysis, and Drug Repurposing by David Kartchner, Haydn Turner, Christophe Ye, Irfan Al-Hussaini, Batuhan Nursal, Albert J. B. Lee, Jennifer Deng, Courtney Curtis, Hannah Cho, Eva L. Duvaris, Coral Jackson, Catherine E. Shanks, Sarah Y. Tan, Selvi Ramalingam, Cassie S. Mitchell

“…The performance (accuracy, precision, recall, F1-score) of four natural-language processing (NLP) models (BioLinkBERT, BioBERT, KRISSBERT, PubMedBERT) and the large language model (LLM), GPT-4o, was evaluated using the human-annotated TrialSieve dataset. …”
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Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception by Michael Nissen, Shih-Yuan Huang, Katharina M. Jäger, Madeleine Flaucher, Adriana Titzmann, Hannah Bleher, Constanza A. Pontones, Hanna Huebner, Nina Danzberger, Peter A. Fasching, Bjoern M. Eskofier, Heike Leutheuser

“…Automated text analysis (natural language processing) was used on app reviews to assess users’ perception of evidence-based content/expert involvement and commercialization techniques. …”
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Enhancing Immersion in Virtual Reality–Based Advanced Life Support Training: Randomized Controlled Trial by Dilek Kitapcioglu, Mehmet Emin Aksoy, Arun Ekin Ozkan, Tuba Usseli, Dilan Cabuk Colak, Tugrul Torun

“…With advancements in artificial intelligence (AI) and natural language processing, voice command interfaces offer an intuitive alternative to traditional virtual reality (VR) controllers in VR applications. …”
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Analisis Perbandingan Model Bert Dan Xlnet Untuk Klasifikasi Tweet Bully Pada Twitter by Teuku Radillah, Okta Veza, Sarjon Defit

“…This study aims to compare the performance of two recent natural language processing models, namely BERT (Bidirectional Encoder Representations from Transformers) and XLNet, in the classification of tweets containing bullying. …”
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Artificial Intelligence in Nursing Decision-Making: A Bibliometric Analysis of Trends and Impacts by Mengdie Hu, Yan Wang, Yunsong Liu, Bingqing Cai, Fanjing Kong, Qian Zheng, Dan Zhao, Guanghui Gao, Zhouguang Hui

“…Keyword analysis highlights the significant potential of natural language processing technologies, particularly those based on large language models (e.g., ChatGPT), in nursing decision-making. …”
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Exploring Scientific Outputs about Globalization: A Conceptual Framework Study by Saleh Rahimi, Faramarz Soheili

“…Employing natural language processing techniques within this software, key terms (nouns or noun phrases) were extracted. …”
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Navigating the Ethical Terrain of AI-Generated Text Tools: A Review by Yasir Abdelgadir Mohamed, Abdul Hakim H. M. Mohamed, Akbar Khanan, Mohamed Bashir, Mousab A. E. Adiel, Muawia A. Elsadig

“…The study analyzes prominent language models, including GPT-3, GPT-4, LaMDA, PaLM, Claude, Jasper, and Llama 2, evaluating their capabilities in natural language processing and generation. The analysis reveals significant advancements, with GPT-3 demonstrating a 92% accuracy rate on standard natural language understanding benchmarks, outperforming LaMDA (88%) and PaLM (85%). …”
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The DRAGON benchmark for clinical NLP by Joeran S. Bosma, Koen Dercksen, Luc Builtjes, Romain André, Christian Roest, Stefan J. Fransen, Constant R. Noordman, Mar Navarro-Padilla, Judith Lefkes, Natália Alves, Max J. J. de Grauw, Leander van Eekelen, Joey M. A. Spronck, Megan Schuurmans, Bram de Wilde, Ward Hendrix, Witali Aswolinskiy, Anindo Saha, Jasper J. Twilt, Daan Geijs, Jeroen Veltman, Derya Yakar, Maarten de Rooij, Francesco Ciompi, Alessa Hering, Jeroen Geerdink, Henkjan Huisman, On behalf of the DRAGON consortium

“…Abstract Artificial Intelligence can mitigate the global shortage of medical diagnostic personnel but requires large-scale annotated datasets to train clinical algorithms. Natural Language Processing (NLP), including Large Language Models (LLMs), shows great potential for annotating clinical data to facilitate algorithm development but remains underexplored due to a lack of public benchmarks. …”
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Musical Expertise Reshapes Cross-Domain Semantic Integration: ERP Evidence from Language and Music Processing by Xing Wang, Tao Zeng

“…Moreover, music training not only enhances domain-specific musical processing but also facilitates cross-domain language processing. However, existing research has predominantly focused on Indo-European languages, with limited evidence from paratactic languages such as Mandarin Chinese. …”
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Context-Aware Embedding Techniques for Addressing Meaning Conflation Deficiency in Morphologically Rich Languages Word Embedding: A Systematic Review and Meta Analysis by Mosima Anna Masethe, Hlaudi Daniel Masethe, Sunday O. Ojo

“…The creation of language-specific models for low-resource languages is one way to increase performance and consistency across Natural Language Processing (NLP) applications in a practical sense. …”
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An Empirically Validated Framework for Automated and Personalized Residential Energy-Management Integrating Large Language Models and the Internet of Energy by Vinícius Pereira Gonçalves, Andre Luiz Marques Serrano, Gabriel Arquelau Pimenta Rodrigues, Matheus Noschang de Oliveira, Rodolfo Ipolito Meneguette, Guilherme Dantas Bispo, Maria Gabriela Mendonça Peixoto, Geraldo Pereira Rocha Filho

“…The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. …”
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Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study by Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal, JaMor Hairston, Jeanne Powell, Drew Walker, Snigdha Peddireddy, Sahithi Lakamana, Selen Bozkurt, Matthew Reyna, Reza Sameni, Yunyu Xiao, Sangmi Kim, Rasheeta Chandler, Natalie Hernandez, Danielle Mowery, Rachel Wightman, Jennifer Love, Anthony Spadaro, Jeanmarie Perrone, Abeed Sarker

“…However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. …”
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Hair salons as a promising space to provide HIV and sexual and reproductive health services for young women in Lesotho: a citizen scientist mixed-methods study by Malena Chiaborelli, Mamaswatsi Kopeka, Pontšo Sekhesa, Madeleine Sehrt, Tsepang Mohloanyane, Tala Ballouz, Dominik Menges, Jennifer A. Brown, Jennifer M. Belus, Felix Gerber, Fabian Raeber, Andréa Williams, David Jackson-Perry, Meri Hyöky, Donaldson F. Conserve, Karen Hampanda, Alain Amstutz, and the Hair SALON Citizen Scientist Working Group

“…Subsequently, we conducted individual in-depth interviews among 14 stylists and clients, following the rapid thematic analysis framework, supported by natural language processing. Clients and stylists were involved at the design, implementation, and results interpretation stage. …”
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