Exploring the Impact of Large Language Models on Disease Diagnosis by Ibrahim Almubark

“…The review highlighted the widespread use of LLMs, such as GPT-4, ChatGPT, GPT-3.5, and LLaMA, with GPT-4 being the most frequently used in disease diagnostics due to its diverse applications, improved accuracy, and efficiency. …”
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A dataset for evaluating clinical research claims in large language models by Boya Zhang, Alban Bornet, Anthony Yazdani, Philipp Khlebnikov, Marija Milutinovic, Hossein Rouhizadeh, Poorya Amini, Douglas Teodoro

“…CliniFact contains 1,970 instances from 992 unique clinical trials related to 1,540 unique publications. When evaluating LLMs against CliniFact, discriminative models, such as BioBERT with an accuracy of 80.2%, outperformed generative counterparts, such as Llama3-70B, which reached 53.6% accuracy (p-value < 0.001). …”
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Towards evaluating and building versatile large language models for medicine by Chaoyi Wu, Pengcheng Qiu, Jinxin Liu, Hongfei Gu, Na Li, Ya Zhang, Yanfeng Wang, Weidi Xie

“…We evaluate nine leading LLMs, e.g., MEDITRON, Llama 3, Mistral, GPT-4, Claude-3.5, etc. and found that most models struggle with these complex tasks. …”
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Large Language Models for UAVs: Current State and Pathways to the Future by Shumaila Javaid, Hamza Fahim, Bin He, Nasir Saeed

“…Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. …”
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Zero-Shot Classification of Art With Large Language Models by Tatsuya Tojima, Mitsuo Yoshida

“…Our experiments demonstrated that the 4-bit quantized Llama-3 70B model, which can run on a local server, achieved the most accurate (over 0.9) automatic annotation of different art forms using LLMs, performing slightly better than the GPT-4o model from OpenAI. …”
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Traffic Flow Prediction Based on Large Language Models and Future Development Directions by Zhang Muhua, Zhao Wenzheng

“…By leveraging LLMs’ advanced understanding capabilities, R2T-LLM provides more transparent and interpretable predictions. …”
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Which curriculum components do medical students find most helpful for evaluating AI outputs? by William J. Waldock, George Lam, Ana Baptista, Risheka Walls, Amir H. Sam

“…Abstract Introduction The risk and opportunity of Large Language Models (LLMs) in medical education both rest in their imitation of human communication. …”
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The Potential Clinical Utility of the Customized Large Language Model in Gastroenterology: A Pilot Study by Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee, Jonghyung Park, Eunsil Kim, Subeen Kim, Minjae Kimm, Seoung-Ho Choi

“…Aim: This study explores the potential clinical utility of two LLMs in the field of gastroenterology: a customized GPT model and a conventional GPT-4o, an advanced LLM capable of retrieval-augmented generation (RAG). …”
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Application of large language models in disease diagnosis and treatment by Xintian Yang, Tongxin Li, Qin Su, Yaling Liu, Chenxi Kang, Yong Lyu, Lina Zhao, Yongzhan Nie, Yanglin Pan, Yuanyuan Ji

“…Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. …”
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Evaluating Diagnostic Accuracy and Treatment Efficacy in Mental Health: A Comparative Analysis of Large Language Model Tools and Mental Health Professionals by Inbar Levkovich

“…However, in more complex cases, such as early schizophrenia, LLM performance varied, with ChatGPT-4 achieving only 55% accuracy, while other LLMs and professionals performed better. …”
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Performance of Large Language Models on the Korean Dental Licensing Examination: A Comparative Study by Woojun Kim, Bong Chul Kim, Han-Gyeol Yeom

“…Purpose: This study investigated the potential application of large language models (LLMs) in dental education and practice, with a focus on ChatGPT and Claude3-Opus. …”
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Large language model usage guidelines in Korean medical journals: a survey using human-artificial intelligence collaboration by Sangzin Ahn

“…Background Large language models (LLMs), the most recent advancements in artificial intelligence (AI), have profoundly affected academic publishing and raised important ethical and practical concerns. …”
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Current applications and challenges in large language models for patient care: a systematic review by Felix Busch, Lena Hoffmann, Christopher Rueger, Elon HC van Dijk, Rawen Kader, Esteban Ortiz-Prado, Marcus R. Makowski, Luca Saba, Martin Hadamitzky, Jakob Nikolas Kather, Daniel Truhn, Renato Cuocolo, Lisa C. Adams, Keno K. Bressem

“…Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. …”
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Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare by Jessica D. Workum, Jessica D. Workum, Jessica D. Workum, Davy van de Sande, Davy van de Sande, Diederik Gommers, Diederik Gommers, Michel E. van Genderen, Michel E. van Genderen

“…In this paper, we propose a practical step-by-step approach to bridge this gap and support healthcare organizations and providers in warranting the responsible and safe implementation of LLMs into healthcare. The recommendations in this manuscript include protecting patient privacy, adapting models to healthcare-specific needs, adjusting hyperparameters appropriately, ensuring proper medical prompt engineering, distinguishing between clinical decision support (CDS) and non-CDS applications, systematically evaluating LLM outputs using a structured approach, and implementing a solid model governance structure. …”
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AI language model rivals expert ethicist in perceived moral expertise by Danica Dillion, Debanjan Mondal, Niket Tandon, Kurt Gray

“…Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment, reflecting moral judgments with relatively high accuracy. …”
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Large language models for causal hypothesis generation in science by Kai-Hendrik Cohrs, Emiliano Diaz, Vasileios Sitokonstantinou, Gherardo Varando, Gustau Camps-Valls

“…Debates persist around LLMs’ causal reasoning capacities. However, rather than engaging in philosophical debates, we propose integrating LLMs into a scientific framework for causal hypothesis generation alongside expert knowledge and data. …”
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Interacting Large Language Model Agents Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

“…First, we show using Bayesian revealed preferences from microeconomics that an individual LLMA satisfies the necessary and sufficient conditions for rationally inattentive (bounded rationality) Bayesian utility maximization and, given an observation, the LLMA chooses an action that maximizes a regularized utility. …”
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Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering by Yunqiao Fei, Jingchao Fan, Guomin Zhou

“…The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. …”
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Decoding substance use disorder severity from clinical notes using a large language model by Maria Mahbub, Gregory M. Dams, Sudarshan Srinivasan, Caitlin Rizy, Ioana Danciu, Jodie Trafton, Kathryn Knight

“…Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. …”
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Incremental accumulation of linguistic context in artificial and biological neural networks by Refael Tikochinski, Ariel Goldstein, Yoav Meiri, Uri Hasson, Roi Reichart

“…Abstract Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. …”
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