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Large Language Models (LLMs) have demonstrated impressive capabilities across natural language processing tasks.
Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., and Szolovits, P · 2009
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Overview of the medical question answering task at TREC 2017 LiveQA
Abacha, A. B., Agichtein, E., Pinter, Y., and Demner-Fushman, D · 2017
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A question-entailment approach to question answering
Abacha, A. B. and Demner-Fushman, D · 2019
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Bridging the gap between consumers’ medication questions and trusted answers
Abacha, A. B., Mrabet, Y., Sharp, M., Goodwin, T., Shooshan, S. E., and Demner-Fushman, D · 2019
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Pubmedqa: A dataset for biomedical research question answering
Jin, Q., Dhingra, B., Liu, Z., Cohen, W., and Lu, X · 2019
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Head-qa: A healthcare dataset for complex reasoning
Vilares, D. and Gómez-Rodríguez, C · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., tau Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D · 2020
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Biogpt: generative pre-trained transformer for biomedical text generation and mining
Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., and Liu, T.-Y · 2022
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Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Large language models encode clinical knowledge, 2022
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Scharli, N., Chowdhery, A., Mansfield, P., y Arcas, B. A., Webster, D., Corrado, G. S., Matias, Y., Chou, K., Gottweis, J., Tomasev, N., Liu, Y., Rajkomar, A., Barral, J., Semturs, C., Karthikesalingam, A., and Natarajan, V · 2022
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Meditron-70b: Scaling medical pretraining for large language models, 2023
Chen, Z., Cano, A. H., Romanou, A., Bonnet, A., Matoba, K., Salvi, F., Pagliardini, M., Fan, S., Köpf, A., Mohtashami, A., Sallinen, A., Sakhaeirad, A., Swamy, V., Krawczuk, I., Bayazit, D., Marmet, A., Montariol, S., Hartley, M.-A., Jaggi, M., and Bosselut, A · 2023
Ecg-qa: A comprehensive question answering dataset combined with electrocardiogram
Oh, J., Lee, G., Bae, S., myoung Kwon, J., and Choi, E · 2023
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Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Babiker, A., Schärli, N., Chowdhery, A., Mansfield, P., Demner-Fushman, D., Agüera y Arcas, B., Webster, D., Corrado, G. S., Matias, Y., Chou, K., Gottweis, J., Tomasev, N., Liu, Y., Rajkomar, A., Barral, J., Semturs, C., Karthikesalingam, A., and Natarajan, V · 2023
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Olaph: Improving factuality in biomedical long-form question answering, 2024
Jeong, M., Hwang, H., Yoon, C., Lee, T., and Kang, J · 2024
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Azure AI Search, 2024a
Microsoft Azure · 2024
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Fine-tune models with Azure AI Foundry, 2024b
Microsoft Azure · 2024
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Towards accurate differential diagnosis with large language models, 2023
McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., Hou, L., Cheng, Y., Liu, Y., Mahdavi, S. S., Prakash, S., Pathak, A., Semturs, C., Patel, S., Webster, D. R., Dominowska, E., Gottweis, J., Barral, J., Chou, K., Corrado, G. S., Matias, Y., Sunshine, J., Karthikesalingam, A., and Natarajan, V · 2023
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Overview of BioASQ 2023: The Eleventh BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering , pp. 227–250
Nentidis, A., Katsimpras, G., Krithara, A., Lima López, S., Farré-Maduell, E., Gasco, L., Krallinger, M., and Paliouras, G · 2023
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Can generalist foundation models outcompete special-purpose tuning? case study in medicine, 2023
Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., Luo, R., McKinney, S. M., Ness, R. O., Poon, H., Qin, T., Usuyama, N., White, C., and Horvitz, E · 2023
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OpenAI
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OpenAI
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Capabilities of gemini models in medicine, 2024
Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., Chaves, J. Z., Hu, S.-Y., Schaekermann, M., Kamath, A., Cheng, Y., Barrett, D. G. T., Cheung, C., Mustafa, B., Palepu, A., McDuff, D., Hou, L., Golany, T., Liu, L., baptiste Alayrac, J., Houlsby, N., Tomasev, N., Freyberg, J., Lau, C., Kemp, J., Lai, J., Azizi, S., Kanada, K., Man, S., Kulkarni, K., Sun, R., Shakeri, S., He, L., Caine, B., Webson, A., Latysheva, N., Johnson, M., Mansfield, P., Lu, J., Rivlin, E., Anderson, J., Green, B., Wong, R., Krause, J., Shlens, J., Dominowska, E., Eslami, S. M. A., Chou, K., Cui, C., Vinyals, O., Kavukcuoglu, K., Manyika, J., Dean, J., Hassabis, D., Matias, Y., Webster, D., Barral, J., Corrado, G., Semturs, C., Mahdavi, S. S., Gottweis, J., Karthikesalingam, A., and Natarajan, V · 2024
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A generalist vision–language foundation model for diverse biomedical tasks
Zhang, K., Zhou, R., Adhikarla, E., Yan, Z., Liu, Y., Yu, J., Liu, Z., Chen, X., Davison, B. D., Ren, H., Huang, J., Chen, C., Zhou, Y., Fu, S., Liu, W., Liu, T., Li, X., Chen, Y., He, L., Zou, J., Li, Q., Liu, H., and Sun, L · 2024
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