참고문헌
- Harris ZS. Distributional structure. Word 1954;10:146-162
- Le Q, Mikolov T. Distributed representations of sentences and documents [accessed on August 18, 2023]. Available at: https://proceedings.mlr.press/v32/le14.html?ref=https://githubhelp.com
- Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533-536
- Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735-1780
- Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv [Preprint]. 2014 [accessed on August 18, 2023]. Available at: https://doi.org/10.48550/arXiv.1406.1078
- Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks [accessed on August 18, 2023]. Available at: https://proceedings.neurips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html
- Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need [accessed on August 18, 2023]. Available at: https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
- Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv [Preprint]. 2018 [accessed on August 18, 2023]. Available at: https://doi.org/10.48550/arXiv.1810.04805
- Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al. Language models are few-shot learners [accessed on August 18, 2023]. Available at: https://proceedings.neurips.cc/paper_files/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html?utm_medium=email&utm_source=transaction
- Jung KH. Uncover this tech term: foundation model. Korean J Radiol 2023;24:1038-1041
- Hwang SI, Lim JS, Lee RW, Matsui Y, Iguchi T, Hiraki T, et al. Is ChatGPT a "fire of prometheus" for non-native English-speaking researchers in academic writing? Korean J Radiol 2023;24:952-959
- Koga S. The integration of large language models such as ChatGPT in scientific writing: harnessing potential and addressing pitfalls. Korean J Radiol 2023;24:924-925
- Park SH. Use of generative artificial intelligence, including large language models such as ChatGPT, in scientific publications: policies of KJR and prominent authorities. Korean J Radiol 2023;24:715-718
- Sarraju A, Bruemmer D, Van Iterson E, Cho L, Rodriguez F, Laffin L. Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model. JAMA 2023;329:842-844
- Haver HL, Ambinder EB, Bahl M, Oluyemi ET, Jeudy J, Yi PH. Appropriateness of breast cancer prevention and screening recommendations provided by ChatGPT. Radiology 2023;307:e230424
- Rahsepar AA, Tavakoli N, Kim GHJ, Hassani C, Abtin F, Bedayat A. How AI responds to common lung cancer questions: ChatGPT vs Google Bard. Radiology 2023;307:e230922
- Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepano C, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health 2023;2:e0000198
- Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations. Radiology 2023;307:e230582
- Bhayana R, Bleakney RR, Krishna S. GPT-4 in radiology: improvements in advanced reasoning. Radiology 2023;307:e230987
- Ueda D, Mitsuyama Y, Takita H, Horiuchi D, Walston SL, Tatekawa H, et al. ChatGPT's diagnostic performance from patient history and imaging findings on the diagnosis please quizzes. Radiology 2023;308:e231040
- Kottlors J, Bratke G, Rauen P, Kabbasch C, Persigehl T, Schlamann M, et al. Feasibility of differential diagnosis based on imaging patterns using a large language model. Radiology 2023;308:e231167
- Sun Z, Ong H, Kennedy P, Tang L, Chen S, Elias J, et al. Evaluating GPT4 on impressions generation in radiology reports. Radiology 2023;307:e231259
- Adams LC, Truhn D, Busch F, Kader A, Niehues SM, Makowski MR, et al. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 2023;307:e230725
- Fink MA, Bischoff A, Fink CA, Moll M, Kroschke J, Dulz L, et al. Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer. Radiology 2023;308:e231362
- Lyu Q, Tan J, Zapadka ME, Ponnatapura J, Niu C, Myers KJ, et al. Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: promising results, limitations, and potential. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2303.09038
- Doshi R, Amin K, Khosla P, Bajaj S, Chheang S, Forman HP. Utilizing large language models to simplify radiology reports: a comparative analysis of ChatGPT3.5, ChatGPT4.0, Google Bard, and Microsoft Bing. medRxiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.1101/2023.06.04.23290786
- Rau A, Rau S, Zoeller D, Fink A, Tran H, Wilpert C, et al. A context-based chatbot surpasses trained radiologists and generic ChatGPT in following the ACR appropriateness guidelines. Radiology 2023;308:e230970
- Gertz RJ, Bunck AC, Lennartz S, Dratsch T, Iuga AI, Maintz D, et al. GPT-4 for automated determination of radiological study and protocol based on radiology request forms: a feasibility study. Radiology 2023;307:e230877
- Rao A, Kim J, Kamineni M, Pang M, Lie W, Succi MD. Evaluating ChatGPT as an adjunct for radiologic decision-making. medRxiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.1101/2023.02.02.23285399
- Wu Z, Zhang L, Cao C, Yu X, Dai H, Ma C, et al. Exploring the trade-offs: unified large language models vs local fine-tuned models for highly-specific radiology NLI task. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2304.09138
- Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature 2023;620:172-180
- Singhal K, Tu T, Gottweis J, Sayres R, Wulczyn E, Hou L, et al. Towards expert-level medical question answering with large language models. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2305.09617
- Wang G, Yang G, Du Z, Fan L, Li X. ClinicalGPT: large language models finetuned with diverse medical data and comprehensive evaluation. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2306.09968
- Liu Z, Zhong A, Li Y, Yang L, Ju C, Wu Z, et al. Radiology-GPT: a large language model for radiology. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2306.08666
- Li H, Zhu J, Jiang X, Zhu X, Li H, Yuan C, et al. Uni-perceiver v2: a generalist model for large-scale vision and vision-language tasks [accessed on October 2, 2023]. Available at: https://openaccess.thecvf.com/content/CVPR2023/html/Li_Uni-Perceiver_v2_A_Generalist_Model_for_Large-Scale_Vision_and_Vision-Language_CVPR_2023_paper.html
- Zhang K, Yu J, Yan Z, Liu Y, Adhikarla E, Fu S, et al. BiomedGPT: a unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2305.17100
- Elkhatat AM. Evaluating the authenticity of ChatGPT responses: a study on text-matching capabilities. Int J Educ Integr 2023;19:15
- Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: can language models be too big? [accessed on October 2, 2023]. Available at: https://dl.acm.org/doi/abs/10.1145/3442188.3445922
- Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks [accessed on October 2, 2023]. Available at: https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
- Mukherjee P, Hou B, Lanfredi RB, Summers RM. Feasibility of using the privacy-preserving large language model Vicuna for labeling radiology reports. Radiology 2023;309:e231147
- Driess D, Xia F, Sajjadi MSM, Lynch C, Chowdhery A, Ichter B, et al. PaLM-E: an embodied multimodal language model. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2303.03378
- OpenAI. ChatGPT can now see, hear, and speak [accessed on October 2, 2023]. Available at: https://openai.com/blog/chatgpt-can-now-see-hear-and-speak
- Tu T, Azizi S, Driess D, Schaekermann M, Amin M, Chang PC, et al. Towards generalist biomedical AI. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2307.14334
- Wu C, Zhang X, Zhang Y, Wang Y, Xie W. Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data. arXiv [Preprint]. 2023 [accessed on October 2, 2023]. Available at: https://doi.org/10.48550/arXiv.2308.02463
- Delbrouck JB, Varma M, Chambon P, Langlotz C. Overview of the RadSum23 shared task on multi-modal and multi-anatomical radiology report summarization [accessed on October 2, 2023]. Available at: https://aclanthology.org/2023.bionlp-1.45/
- Fei N, Lu Z, Gao Y, Yang G, Huo Y, Wen J, et al. Towards artificial general intelligence via a multimodal foundation model. Nat Commun 2022;13:3094