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  1. 101

    An Empirical Evaluation of Large Language Models on Consumer Health Questions by Moaiz Abrar, Yusuf Sermet, Ibrahim Demir

    Published 2025-02-01
    “…<b>Conclusions:</b> Current small or medium sized LLMs struggle to provide accurate answers to consumer health questions and must be significantly improved.…”
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    Article
  2. 102

    Assessing the performance of zero-shot visual question answering in multimodal large language models for 12-lead ECG image interpretation by Tomohisa Seki, Yoshimasa Kawazoe, Yoshimasa Kawazoe, Hiromasa Ito, Yu Akagi, Toru Takiguchi, Kazuhiko Ohe, Kazuhiko Ohe

    Published 2025-02-01
    “…These findings suggest a need for improved control over image hallucination and indicate that performance evaluation using the percentage of correct answers to multiple-choice questions may not be sufficient for performance assessment in VQA tasks.…”
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  3. 103
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  5. 105

    Research on a traditional Chinese medicine case-based question-answering system integrating large language models and knowledge graphs by Yuchen Duan, Qingqing Zhou, Yu Li, Chi Qin, Ziyang Wang, Hongxing Kan, Hongxing Kan, Jili Hu, Jili Hu

    Published 2025-01-01
    “…This approach could play a crucial role in modernizing TCM research and improving access to clinical insights. Future research may explore expanding the dataset and refining the query system for broader applications.…”
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    Article
  6. 106

    Enhancing vaccine communication in social Q&A: identifying readily applicable factors for answer acceptance on medical sciences stack exchange by Hengyi Fu

    Published 2025-03-01
    “…This study investigates factors influencing the acceptance of answers to vaccine-related questions on social Q&A platforms, aiming to improve online vaccine communication. …”
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    Article
  7. 107
  8. 108

    Analyzing Diagnostic Reasoning of Vision–Language Models via Zero-Shot Chain-of-Thought Prompting in Medical Visual Question Answering by Fatema Tuj Johora Faria, Laith H. Baniata, Ahyoung Choi, Sangwoo Kang

    Published 2025-07-01
    “…Medical Visual Question Answering (MedVQA) lies at the intersection of computer vision, natural language processing, and clinical decision-making, aiming to generate accurate responses from medical images paired with complex inquiries. …”
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    Article
  9. 109

    From Questions to Answers: Teaching Evidence-Based Medicine Question Formulation and Literature Searching Skills to First-Year Medical Students by Juliana Magro, Caitlin Plovnick, Gregory Laynor, Joey Nicholson

    Published 2025-02-01
    “…After the workshop, students completed a posttest. Students showed improvement in differentiating background and foreground questions (p < .001), formulating answerable clinical questions (p < .001), and developing appropriate database searches (p < .001 and p = .002). …”
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  10. 110

    The Use of the Cloze Test in Reading Comprehension Assessment in Brazil: Post-Pandemic Challenges by Flávia Oliveira Freitas, Gislane Evangelista dos Santos, Raquel Meister Ko Freitag

    Published 2025-05-01
    “…The criteria for analyzing these answers are based on Taylor’s (1953) exact answers initial proposal (Brown 1980; 2013), added to other assessment instruments used in the Psychology field. …”
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  11. 111

    Accuracy, appropriateness, and readability of ChatGPT-4 and ChatGPT-3.5 in answering pediatric emergency medicine post-discharge questions by Mitul Gupta, Aiza Kahlun, Ria Sur, Pramiti Gupta, Andrew Kienstra, Winnie Whitaker, Graham Aufricht

    Published 2025-04-01
    “…This study compared 2 versions of ChatGPT in answering post-discharge follow-up questions in the area of pediatric emergency medicine (PEM). …”
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  12. 112

    How reliable are ChatGPT and Google’s answers to frequently asked questions about unicondylar knee arthroplasty from a scientific perspective? by Ali Aydilek, Ömer Levent Karadamar

    Published 2025-06-01
    “…Results A total of 83.3% of ChatGPT’s responses were found to be consistent with academic sources, whereas this rate was 58.3% for Google. ChatGPT’s answers of 142.8 words, compared to Google’s 85.6-word average. …”
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  13. 113
  14. 114

    Mother: a maternal online technology for health care dataset by Odongo Steven Eyobu, Brian Angoda Nyanga, Lukman Bukenya, Daniel Ongom, Tonny J. Oyana

    Published 2025-04-01
    “…The answers to the questions were provided and validated by professional medical personnel. …”
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  15. 115

    Arch-Eval benchmark for assessing chinese architectural domain knowledge in large language models by Jie Wu, Mincheng Jiang, Juntian Fan, Shimin Li, Hongtao Xu, Ye Zhao

    Published 2025-04-01
    “…The results reveal significant differences in the performance of these models in the domain of architectural knowledge question-answering. Our findings show that the average accuracy difference between Chain-of-Thought (COT) evaluation and Answer-Only (AO) evaluation is less than 3%, but the response time for COT is significantly longer, extending to 26 times that of AO (62.23 seconds per question vs. 2.38 seconds per question). …”
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  16. 116

    Enhancing Chatbot Responses through Improved T5 Model Incorporating Aggregated Multi-Head Attention Mechanism and Bidirectional Long Short-Term Memory by Muthukumaran N., Vignesh A.

    Published 2025-07-01
    “…This research proposes an advanced transformer model, the Improved T5 (IT5), designed to address these issues. …”
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  17. 117

    Enhancing responses from large language models with role-playing prompts: a comparative study on answering frequently asked questions about total knee arthroplasty by Yi-Chen Chen, Sheng-Hsun Lee, Huan Sheu, Sheng-Hsuan Lin, Chih-Chien Hu, Shih-Chen Fu, Cheng-Pang Yang, Yu-Chih Lin

    Published 2025-05-01
    “…This study aims to evaluate and compare the performance of these LLMs in answering frequently asked questions (FAQs) about Total Knee Arthroplasty (TKA), with a specific focus on the impact of role-playing prompts. …”
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  18. 118

    Comparative performance of ChatGPT, Gemini, and final-year emergency medicine clerkship students in answering multiple-choice questions: implications for the use of AI in medical e... by Shaikha Nasser Al-Thani, Shahzad Anjum, Zain Ali Bhutta, Sarah Bashir, Muhammad Azhar Majeed, Anfal Sher Khan, Khalid Bashir

    Published 2025-08-01
    “…While these tools show promise for answering multiple-choice questions (MCQs), their efficacy in specialized domains, such as Emergency Medicine (EM) clerkship, remains underexplored. …”
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