Assessing GPT's capabilities in consumer food survey analysis: A comparative approach for understanding food technophobia and novel protein perceptions by Peihua Ma, Si Chen, Wenfan Su, Jiping Sheng, Xiaoxue Jia, Cheng-I Wei, Yunbo Luo, Jiao Xu, Yan Song, Ling Yong, Tong Ou, Ying Yue

“…Traditional survey analysis methods face limitations in handling large-scale, open-ended responses, whereas GPT's natural language processing capabilities offer efficient, bias-reduced alternatives. …”
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A Model-Driven Approach Employing DSL and Generation Templates to Accelerate the Development of Conversational Agents for Smart Tourism by Charaf Ouaddi, Lamya Benaddi, Adnane Souha, Hamza Abdelmalek, Abdeslam Jakimi, Rachid Saadane

“…The development of conversational agents utilizing artificial intelligence (AI) techniques represents a significant advancement in Natural Language Processing (NLP). Numerous studies employ deep learning and NLP methodologies to construct sophisticated conversational agent systems. …”
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Human Behavior Analysis: A Comprehensive Survey on Techniques, Applications, Challenges, and Future Directions by Siham Essahraui, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui, Mouncef Filali Bouami, Ibrahim Ouahbi, Ahmed A. Abd El-Latif, May Almousa, Joel J. P. C. Rodrigues

“…The survey then explores traditional and modern techniques, from manual observation to AI-driven methods such as deep learning, natural language processing, and computer vision. A key contribution is our extensive coverage of HBA applications in healthcare, marketing, education, workplace productivity, activity recognition, and criminal justice. …”
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Feasibility Study of Ontological Development Using Semi-Automatic Method based on Lexical Frequency Analysis: A Case Study of “glaucoma” by Somayeh Tamjid, Fatemeh Nooshinfard, Moluk S. Hoseini Beheshti, nadjla Hariri, Fahimeh Babolhavaeji

“…Furthermore, ontologies make natural language processing approaches more feasible by reducing semantic ambiguity and making concepts comprehensible to computer-based deductions. …”
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Leveraging AI to Drive Timely Improvements in Patient Experience Feedback: Algorithm Validation by Mustafa Khanbhai, Catalina Carenzo, Sarindi Aryasinghe, David Manton, Erik Mayer

“…ObjectiveLeveraging machine learning and natural language processing, this study explores the utility of a supervised algorithm to systematically test and refine the algorithm’s cross-contextual performance in diverse health care settings, addressing variations in population characteristics, geographical locations, and care settings, ultimately driving improvements based on patient feedback. …”
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Training and Assessing Teamwork in Interprofessional Virtual Reality–Based Simulation Using the TeamSTEPPS Framework: Protocol for Randomized Pre-Post Intervention Study by Marie Lehmann, Jan Mikulasch, Horst Poimann, Joy Backhaus, Sarah König, Tobias Mühling

“…Future research could include a control group to measure the effects of team training more rigorously or use more enhanced technologies (eg, natural language processing) to capture the full range of teamwork behavior. …”
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Deep learning-based risk stratification of ductal carcinoma in situ using mammography and abbreviated breast magnetic resonance imaging by Tingfeng Zhang, Tingting Cui, Zhenjie Cao, Jintao Hu, Jintao Hu, Jie Ma

“…Deep learning and natural language processing techniques were used to extract relevant features, and model performance was assessed using bootstrap validation.ResultsAbbreviated Magnetic Resonance Imaging protocols demonstrated similar diagnostic accuracy to the full protocol (P > 0.05), offering a faster yet effective imaging option. …”
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Investigating the Predominance of Large Language Models in Low-Resource Bangla Language over Transformer Models for Hate Speech Detection: A Comparative Analysis by Fatema Tuj Johora Faria, Laith H. Baniata, Sangwoo Kang

“…Our research contributes to the growing body of literature on LLM applications in natural language processing, particularly in the context of low-resource languages.…”
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NLP-based removal of personally identifiable information from Hungarian electronic health records by András Berzi, Ervin Berényi, Zita Képes, Barnabás Antal, Ábrahám Gergely Varga, Miklós Emri

“…To safeguard patient confidentiality, under the General Data Protection Regulation (GDPR), strict measures are required to ensure personal data is anonymized or pseudonymized to protect individual privacy. Natural language processing has consistently proven effective in automating the de-identification of sensitive information.MethodsWe present spaCy models to recognize personally identifiable information (PII) from a wide range of free-text medical records written in Hungarian, a low-resource language. …”
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Need for Executive Leadership Coaching in Public Health, Healthcare Administration, Public Safety Crisis Management, and Healthcare Humanitarian Relief by Dr. Amalisha Sabie Aridi

“…This model bridges theory and practice by proposing how AI tools, including machine learning, natural language processing, and behavioral analytics, can deliver personalized, scalable coaching to cultivate leaders’ emotional intelligence, adaptive skills, and ethical decision-making during high-stakes public health emergencies. …”
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A Hybrid Attention-Based Transformer Model for Arabic News Classification Using Text Embedding and Deep Learning by Md. Mithun Hossain, Md. Shakil Hossain, Mejdl Safran, Sultan Alfarhood, Meshal Alfarhood, M. F. Mridha

“…This study contributes to the field of Arabic natural language processing by offering a novel method that combines deep learning with traditional techniques, thereby advancing the state of Arabic news classification. …”
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Effects of movement behaviors on preschoolers’ cognition: a systematic review of randomized controlled trials by Catalina Pacheco, Victoria Culkin, Amelia Putkaradze, Nan Zeng

“…The cognitive outcomes evaluated included executive function, attention, memory, and other key domains critical to early childhood development, such as language, processing speed, and social cognition. Results Twenty-two RCTs (14 individual, 8 cluster) met the inclusion criteria. …”
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The application of risk models based on machine learning to predict endometriosis‐associated ovarian cancer in patients with endometriosis by Xiaopei Chao, Shu Wang, Jinghe Lang, Jinhua Leng, Qingbo Fan

“…We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method – gradient‐boosting decision tree – to construct a predictive model for EAOC and to evaluate the accuracy of the model. …”
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Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study by Mohammed A Almanna, Lior M Elkaim, Mohammed A Alvi, Jordan J Levett, Ben Li, Muhammad Mamdani, Mohammed Al‑Omran, Naif M Alotaibi

“…ObjectiveThis study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods. MethodsA mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. …”
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Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review by Maryam Azam, Shah Khalid, Sulaiman Almutairi, Hasan Ali Khattak, Abdallah Namoun, Amjad Ali, Hafiz Syed Muhammad Bilal

“…This review article is a valuable resource for advancing text summarization techniques in natural language processing and machine learning by identifying future research directions and open challenges. …”
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Pitik: A Cebuano-Binisaya Intent-Based Chatbot for Cardiovascular Disease Patient Profiling and Risk Factor Recommendations by Joseph Cedeño, Andrew Manteza, Nicole Nacar, Merhamdin Umbukan, Cherrie Muaña, Ma. Juliet Vasay-Cruz, Ceasar Ian Benablo, Kristine Mae Adlaon

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Examining the Popularity, Content, and Intersections With the Substance Abuse and Mental Health Services Administration’s Definition of Recovery in a Nonclinical Online Cannabis Ce... by Elyse J Thulin, Maureen A Walton, Erin E Bonar, Anne Fernandez

“…A linear model and a nonlinear model were compared to evaluate change in the number of posts by year. Mixed natural language processing and qualitative analyses were applied to identify top terms, phrases, and themes present in posts over time. …”
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High clinical burden of classical homocystinuria in the United States: a retrospective analysis by Mahim Jain, Mehul Shah, Kamlesh M. Thakker, Andrew Rava, Agnes Pelts Block, Colette Ndiba-Markey, Lionel Pinto

“…Patients who had 1 or more International Classification of Diseases, Tenth Revision code for homocystinuria (E72.11) or the signs, disease, and symptoms term homocystinuria in the natural language processing dataset were included. To obtain a study population most likely to have HCU, stratifications by tHcy levels, clinical characteristics, and phenotypic expressions were applied to refine the cohort. …”
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Using a Diverse Test Suite to Assess Large Language Models on Fast Health Care Interoperability Resources Knowledge: Comparative Analysis by Ahmad Idrissi-Yaghir, Kamyar Arzideh, Henning Schäfer, Bahadir Eryilmaz, Mikel Bahn, Yutong Wen, Katarzyna Borys, Eva Hartmann, Cynthia Schmidt, Obioma Pelka, Johannes Haubold, Christoph M Friedrich, Felix Nensa, René Hosch

“… Abstract BackgroundRecent natural language processing breakthroughs, particularly with the emergence of large language models (LLMs), have demonstrated remarkable capabilities on general knowledge benchmarks. …”
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Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation by Jin Han, Liang Yang

“…In natural language processing (NLP) tasks, computing semantic textual similarity (STS) is crucial for capturing nuanced semantic differences in text. …”
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