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The advancement of Large Vision-Language Models (LVLMs) has propelled their application in the medical field.
Crafting papers on machine learning
Langley, P · 2000
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A · 2005
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Preparing a collection of radiology examinations for distribution and retrieval
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A dataset of clinically generated visual questions and answers about radiology images
Lau, J. J., Gayen, S., Ben Abacha, A., and Demner-Fushman, D · 2018
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Artificial intelligence in radiotherapy treatment planning: present and future
Wang, C., Zhu, X., Hong, J. C., and Zheng, D · 2019
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Mimic-iv
Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., and Mark, R · 2020
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering
Liu, B., Zhan, L.-M., Xu, L., Ma, L., Yang, Y., and Wu, X.-M · 2021
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Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Tăuţan, A.-M., Ionescu, B., and Santarnecchi, E · 2021
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A unified drug–target interaction prediction framework based on knowledge graph and recommendation system
Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., and Hou, T · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., and Henighan, T · 2022
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Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond
Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J · 2023
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Holistic analysis of hallucination in gpt-4v (ision): Bias and interference challenges
Cui, C., Zhou, Y., Yang, X., Wu, S., Zhang, L., Zou, J., and Yao, H · 2023
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Nurvid: A large expert-level video database for nursing procedure activity understanding
Hu, M., Wang, L., Yan, S., Ma, D., Ren, Q., Xia, P., Feng, W., Duan, P., Ju, L., and Ge, Z · 2023
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Med-flamingo: a multimodal medical few-shot learner
Moor, M., Huang, Q., Wu, S., Yasunaga, M., Dalmia, Y., Leskovec, J., Zakka, C., Reis, E. P., and Rajpurkar, P · 2023
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OpenAI · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2023
Cited alongside, same era.
Xraygpt: Chest radiographs summarization using medical vision-language models
Thawkar, O., Shaker, A., Mullappilly, S. S., Cholakkal, H., Anwer, R. M., Khan, S., Laaksonen, J., and Khan, F. S · 2023
Cited alongside, same era.
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W · 2023
Cited alongside, same era.
Medthink: Inducing medical large-scale visual language models to hallucinate less by thinking more
Jiang, Y., Chen, J., Yang, D., Li, M., Wang, S., Wu, T., Li, K., and Zhang, L · 2024
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Biomistral: A collection of open-source pretrained large language models for medical domains
Labrak, Y., Bazoge, A., Morin, E., Gourraud, P.-A., Rouvier, M., and Dufour, R · 2024
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Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback
Lee, H., Phatale, S., Mansoor, H., Mesnard, T., Ferret, J., Lu, K. R., Bishop, C., Hall, E., Carbune, V., Rastogi, A., et al · 2024
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Tp-drseg: Improving diabetic retinopathy lesion segmentation with explicit text-prompts assisted sam
Li, W., Xiong, X., Xia, P., Ju, L., and Ge, Z · 2024
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Multimedeval: A benchmark and a toolkit for evaluating medical vision-language models
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Zhang, X., Wu, C., Zhao, Z., Lin, W., Zhang, Y., Wang, Y., and Xie, W · 2023
Cited alongside, same era.
Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization
Zhao, Z., Wang, B., Ouyang, L., Dong, X., Wang, J., and He, C · 2023
Cited alongside, same era.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2023
Cited alongside, same era.
Banerjee, O., Zhou, H.-Y., Adithan, S., Kwak, S., Wu, K., and Rajpurkar, P · 2024
Cited alongside, same era.
Detecting and evaluating medical hallucinations in large vision language models
Chen, J., Yang, D., Wu, T., Jiang, Y., Hou, X., Li, M., Wang, S., Xiao, D., Li, K., and Zhang, L · 2024
Cited alongside, same era.
Med42-v2: A suite of clinical llms
Christophe, C., Kanithi, P. K., Raha, T., Khan, S., and Pimentel, M. A · 2024
Cited alongside, same era.
Fine-grained verifiers: Preference modeling as next-token prediction in vision-language alignment
Cui, C., Zhang, A., Zhou, Y., Chen, Z., Deng, G., Yao, H., and Chua, T.-S · 2024
Cited alongside, same era.
Royer, C., Menze, B., and Sekuboyina, A · 2024
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Stllava-med: Self-training large language and vision assistant for medical
Sun, G., Qin, C., Fu, H., Wang, L., and Tao, Z · 2024
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Opportunities and challenges for chatgpt and large language models in biomedicine and health
Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Y., Chen, Q., Kim, W., Comeau, D. C., et al · 2024
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Towards generalist biomedical ai
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al · 2024
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Rule: Reliable multimodal rag for factuality in medical vision language models
Xia, P., Zhu, K., Li, H., Zhu, H., Li, Y., Li, G., Zhang, L., and Yao, H · 2024
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Medtrinity-25m: A large-scale multimodal dataset with multigranular annotations for medicine
Xie, Y., Zhou, C., Gao, L., Wu, J., Li, X., Zhou, H.-Y., Liu, S., Xing, L., Zou, J., Xie, C., et al · 2024
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., and Weston, J. E · 2024
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Mdocagent: A multi-modal multi-agent framework for document understanding
Han, S., Xia, P., Zhang, R., Sun, T., Li, Y., Zhu, H., and Yao, H · 2025
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Mj-video: Fine-grained benchmarking and rewarding video preferences in video generation
Tong, H., Wang, Z., Chen, Z., Ji, H., Qiu, S., Han, S., Geng, K., Xue, Z., Zhou, Y., Xia, P., et al · 2025
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Screening cognitive impairment in patients with atrial fibrillation: a deep learning model based on retinal fundus photographs
Wang, Z., Li, M., Xia, P., Jiang, C., Shen, T., Ma, J., Bai, Y., Zhang, S., Lai, Y., Li, S., et al · 2025
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Hgclip: Exploring vision-language models with graph representations for hierarchical understanding
Xia, P., Yu, X., Hu, M., Ju, L., Wang, Z., Duan, P., and Ge, Z · 2025
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Anyprefer: An agentic framework for preference data synthesis
Zhou, Y., Wang, Z., Wang, T., Xing, S., Xia, P., Li, B., Zheng, K., Zhang, Z., Chen, Z., Zheng, W., et al · 2025
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