Source Characteristics Influence AI-Enabled Orthopaedic Text Simplification by Saman Andalib, BS, Sean S. Solomon, BS, Bryce G. Picton, BS, Aidin C. Spina, BS, John A. Scolaro, MD, Ariana M. Nelson, MD

“…This study assesses the effectiveness of large language models (LLMs) in simplifying complex language within orthopaedic patient education materials (PEMs) and identifies predictive factors for successful text transformation. …”
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Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia by Benedikt Schmidl, Tobias Hütten, Steffi Pigorsch, Fabian Stögbauer, Cosima C. Hoch, Timon Hussain, Barbara Wollenberg, Markus Wirth

“…Advancements in artificial intelligence led to Image recognition being introduced recently into large language models (LLMs) such as ChatGPT 4.0. This exploratory study, for the first time, evaluated the application of image recognition by ChatGPT to diagnose squamous cell carcinoma and leukoplakia based on clinical images, with images without any lesion as a control group. …”
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Assessing the Capabilities of Generative Pretrained Transformer-4 in Addressing Open-Ended Inquiries of Oral Cancer by Kaiyuan Ji, Jing Han, Guangtao Zhai, Jiannan Liu

“…Introduction and aims: In the face of escalating oral cancer rates, the application of large language models like Generative Pretrained Transformer (GPT)-4 presents a novel pathway for enhancing public awareness about prevention and early detection. …”
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Correlation-guided decoding strategy for low-resource Uyghur scene text recognition by Miaomiao Xu, Jiang Zhang, Lianghui Xu, Wushour Silamu, Yanbing Li

“…Abstract Currently, most state-of-the-art scene text recognition methods are based on the Transformer architecture and rely on pre-trained large language models. However, these pre-trained models are primarily designed for resource-rich languages and exhibit limitations when applied to low-resource languages. …”
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Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition by Syed Ali Haider, Olivia A. Ho, Sahar Borna, Cesar A. Gomez-Cabello, Sophia M. Pressman, Dave Cole, Ajai Sehgal, Bradley C. Leibovich, Antonio Jorge Forte

“…This study evaluates the accuracy of publicly available Large Language Models (LLMs)—ChatGPT-4, ChatGPT-4o, and Gemini—and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.0), in identifying surgical instruments from images. …”
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Unleashing the potential of chatbots in mental health: bibliometric analysis by Qing Han, Chenyang Zhao

“…High-frequency terms such as “ChatGPT”, “machine learning”, and “large language models” underscore the current state of research, highlighting the cutting-edge advancements and frontiers in this field.ConclusionsThis study provides an in-depth analysis of the most prominent countries, institutions, publications, collaboration status, and research topics associated with utilization of chatbots in mental health over the last decade. …”
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The externalization of internal experiences in psychotherapy through generative artificial intelligence: a theoretical, clinical, and ethical analysis by Yuval Haber, Dorit Hadar Shoval, Inbar Levkovich, Dror Yinon, Karny Gigi, Oori Pen, Tal Angert, Zohar Elyoseph, Zohar Elyoseph

“…Recent advances in generative artificial intelligence (GenAI), specifically large language models (LLMs), present new possibilities for therapeutic interventions; however, their integration into core psychotherapy practices remains largely unexplored. …”
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Understanding Human Papillomavirus Vaccination Hesitancy in Japan Using Social Media: Content Analysis by Junyu Liu, Qian Niu, Momoko Nagai-Tanima, Tomoki Aoyama

“…Natural language processing techniques and large language models (LLMs) were used for stance analysis of the collected data. …”
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AI versus human-generated multiple-choice questions for medical education: a cohort study in a high-stakes examination by Alex KK Law, Jerome So, Chun Tat Lui, Yu Fai Choi, Koon Ho Cheung, Kevin Kei-ching Hung, Colin Alexander Graham

“…Abstract Background The creation of high-quality multiple-choice questions (MCQs) is essential for medical education assessments but is resource-intensive and time-consuming when done by human experts. Large language models (LLMs) like ChatGPT-4o offer a promising alternative, but their efficacy remains unclear, particularly in high-stakes exams. …”
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What’s Going On With Me and How Can I Better Manage My Health? The Potential of GPT-4 to Transform Discharge Letters Into Patient-Centered Letters to Enhance Patient Safety: Prospe... by Felix Eisinger, Friederike Holderried, Moritz Mahling, Christian Stegemann–Philipps, Anne Herrmann–Werner, Eric Nazarenus, Alessandra Sonanini, Martina Guthoff, Carsten Eickhoff, Martin Holderried

“…However, these letters are often written in professional jargon, making them difficult for patients with limited medical knowledge to understand. Large language models, such as GPT, have the potential to transform these discharge summaries into patient-friendly letters, improving accessibility and understanding. …”
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Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study by Zhichao Yang, Zonghai Yao, Mahbuba Tasmin, Parth Vashisht, Won Seok Jang, Feiyun Ouyang, Beining Wang, David McManus, Dan Berlowitz, Hong Yu

“…GPT-4V’s accuracy was compared with 2 state-of-the-art large language models, GPT-3.5 Turbo and GPT-4. The quality of the explanations was evaluated by choosing human preference between an explanation by GPT-4V (without hint), an explanation by an expert, or a tie, using 3 qualitative metrics: comprehensive explanation, question information, and image interpretation. …”
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