Review

Artificial intelligence generated content (AIGC) in medicine: A narrative review


  • Received: 27 September 2023 Revised: 05 December 2023 Accepted: 13 December 2023 Published: 02 January 2024
  • Recently, artificial intelligence generated content (AIGC) has been receiving increased attention and is growing exponentially. AIGC is generated based on the intentional information extracted from human-provided instructions by generative artificial intelligence (AI) models. AIGC quickly and automatically generates large amounts of high-quality content. Currently, there is a shortage of medical resources and complex medical procedures in medicine. Due to its characteristics, AIGC can help alleviate these problems. As a result, the application of AIGC in medicine has gained increased attention in recent years. Therefore, this paper provides a comprehensive review on the recent state of studies involving AIGC in medicine. First, we present an overview of AIGC. Furthermore, based on recent studies, the application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation. The basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized. Finally, we also discuss the limitations and challenges faced by AIGC and propose possible solutions with relevant studies. We hope this review can help readers understand the potential of AIGC in medicine and obtain some innovative ideas in this field.

    Citation: Liangjing Shao, Benshuang Chen, Ziqun Zhang, Zhen Zhang, Xinrong Chen. Artificial intelligence generated content (AIGC) in medicine: A narrative review[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1672-1711. doi: 10.3934/mbe.2024073

    Related Papers:

  • Recently, artificial intelligence generated content (AIGC) has been receiving increased attention and is growing exponentially. AIGC is generated based on the intentional information extracted from human-provided instructions by generative artificial intelligence (AI) models. AIGC quickly and automatically generates large amounts of high-quality content. Currently, there is a shortage of medical resources and complex medical procedures in medicine. Due to its characteristics, AIGC can help alleviate these problems. As a result, the application of AIGC in medicine has gained increased attention in recent years. Therefore, this paper provides a comprehensive review on the recent state of studies involving AIGC in medicine. First, we present an overview of AIGC. Furthermore, based on recent studies, the application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation. The basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized. Finally, we also discuss the limitations and challenges faced by AIGC and propose possible solutions with relevant studies. We hope this review can help readers understand the potential of AIGC in medicine and obtain some innovative ideas in this field.



    加载中


    [1] M. E. Sahin, Image processing and machine learning‐based bone fracture detection and classification using X‐ray images, Int. J. Imaging Syst. Technol., 33 (2023), 853–865. https://doi.org/10.1002/ima.22849 doi: 10.1002/ima.22849
    [2] Z. Zhao, Y. Tian, Z. Yuan, P. Zhao, F. Xia, S. Yu, A machine learning method for improving liver cancer staging, J. Biomed. Inf., 137 (2023), 104266. https://doi.org/10.1002/ima.22849 doi: 10.1002/ima.22849
    [3] S. Maurya, S. Tiwari, M. C. Mothukuri, C. M. Tangeda, R. N. S. Nandigam, D. C. Addagiri, A review on recent developments in cancer detection using Machine Learning and Deep Learning models, Biomed. Signal Process. Control, 80 (2023), 104398. https://doi.org/10.1016/j.bspc.2022.104398 doi: 10.1016/j.bspc.2022.104398
    [4] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, Improving language understanding by generative pre-training, OpenAI, 2018.
    [5] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, M. Chen, Hierarchical text-conditional image generation with CLIP latents, preprint, arXiv.2204.06125. https://doi.org/10.48550/arXiv.2204.06125
    [6] A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, D. S. W. Ting, Large language models in medicine, Nat. Med., 29 (2023), 1930–1940. https://doi.org/10.1038/s41591-023-02448-8 doi: 10.1038/s41591-023-02448-8
    [7] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, OpenAI blog, 1 (2019), 9.
    [8] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, et al., Language models are few-shot learners, Adv. Neural Inf. Process. Syst., 33 (2020), 1877–1901.
    [9] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, et al., Sparks of artificial general intelligence: Early experiments with gpt-4, preprint, arXiv: 2303.12712. https://doi.org/10.48550/arXiv.2303.12712
    [10] J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, et al., Scaling language models: Methods, analysis & insights from training gopher, preprint, arXiv: 2112.11446. https://doi.org/10.48550/arXiv.2112.11446
    [11] T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, et al., Bloom: A 176b-parameter open-access multilingual language model, preprint, arXiv: 2211.05100. https://doi.org/10.48550/arXiv.2211.05100
    [12] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, et al., Training language models to follow instructions with human feedback, Adv. Neural Inf. Process. Syst., 35 (2022), 27730–27744.
    [13] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, et al., Exploring the limits of transfer learning with a unified text-to-text transformer, J. Machine Learn. Res., 21 (2020), 5485–5551.
    [14] W. Fedus, B. Zoph, N. Shazeer, Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, J. Machine Learn. Res., 23 (2022), 5232–5270.
    [15] V. Aribandi, Y. Tay, T. Schuster, J. Rao, H. S. Zheng, S. V. Mehta, et al., Ext5: Towards extreme multi-task scaling for transfer learning, preprint, arXiv: 2111.10952. https://doi.org/10.48550/arXiv.2111.10952
    [16] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, et al., Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, preprint, arXiv: 1910.13461. https://doi.org/10.48550/arXiv.1910.13461
    [17] Z. Li, Z. Wang, M. Tan, R. Nallapati, P. Bhatia, A. Arnold, et al., Dq-bart: Efficient sequence-to-sequence model via joint distillation and quantization, preprint, arXiv: 2203.11239. https://doi.org/10.48550/arXiv.2203.11239
    [18] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial networks, Commun. ACM, 63 (2020), 139–144. https://doi.org/10.1145/3422622 doi: 10.1145/3422622
    [19] D. P. Kingma, M. Welling, Auto-encoding variational bayes, preprint, arXiv: 1312.6114. https://doi.org/10.48550/arXiv.1312.6114
    [20] L. Dinh, D. Krueger, Y. Bengio, Nice: Non-linear independent components estimation, preprint, arXiv: 1410.8516. https://doi.org/10.48550/arXiv.1410.8516
    [21] Y. Song, S. Ermon, Generative modeling by estimating gradients of the data distribution, Adv. Neural Inf. Process. Syst., 32 (2019).
    [22] E. L. Denton, S. Chintala, R. Fergus, Deep generative image models using a laplacian pyramid of adversarial networks, Adv. Neural Inf. Process. Syst., 28 (2015).
    [23] H. Zhang, I. Goodfellow, D. Metaxas, A. Odena, Self-attention generative adversarial networks, in International Conference on Machine Learning, (2019), 7354–7363.
    [24] A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, preprint, arXiv: 1511.06434. https://doi.org/10.48550/arXiv.1511.06434
    [25] M. Liu, O. Tuzel, Coupled generative adversarial networks, Adv. Neural Inf. Process. Syst., 29 (2016).
    [26] A. Brock, J. Donahue, K. Simonyan, Large scale GAN training for high fidelity natural image synthesis, preprint, arXiv: 1809.11096. https://doi.org/10.48550/arXiv.1809.11096
    [27] T. Nguyen, T. Le, H. Vu, D. Phung, Dual discriminator generative adversarial nets, Adv. Neural Inf. Process. Syst., 30 (2017).
    [28] I. Durugkar, I. Gemp, S. Mahadevan, Generative multi-adversarial networks, preprint, arXiv: 1611.01673. https://doi.org/10.48550/arXiv.1611.01673
    [29] Q. Hoang, T. D. Nguyen, T. Le, D. Phung, Multi-generator generative adversarial nets, preprint, arXiv: 1708.02556. https://doi.org/10.48550/arXiv.1708.02556
    [30] A. Ghosh, V. Kulharia, V. P. Namboodiri, P. H. Torr, P. K. Dokania, Multi-agent diverse generative adversarial networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), 8513–8521. https://doi.org/10.1109/CVPR.2018.00888
    [31] S. Nowozin, B. Cseke, R. Tomioka, f-gan: Training generative neural samplers using variational divergence minimization, Adv. Neural Inf. Process. Syst., 29 (2016).
    [32] T. Miyato, T. Kataoka, M. Koyama, Y. Yoshida, Spectral normalization for generative adversarial networks, preprint, arXiv: 1802.05957. https://doi.org/10.48550/arXiv.1802.05957
    [33] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. C. Courville, Improved training of wasserstein gans, Adv. Neural Inf. Process. Syst., 30 (2017).
    [34] G. Qi, Loss-sensitive generative adversarial networks on lipschitz densities, Int. J. Comput. Vis., 128 (2020), 1118–1140. https://doi.org/10.1007/s11263-019-01265-2 doi: 10.1007/s11263-019-01265-2
    [35] T. Che, Y. Li, A. P. Jacob, Y. Bengio, W. Li, Mode regularized generative adversarial networks, preprint, arXiv: 1612.02136. https://doi.org/10.48550/arXiv.1612.02136
    [36] L. Maaløe, M. Fraccaro, V. Liévin, O. Winther, Biva: A very deep hierarchy of latent variables for generative modeling, Adv. Neural Inf. Process. Syst., 32 (2019).
    [37] A. Vahdat, J. Kautz, NVAE: A deep hierarchical variational autoencoder, Adv. Neural Inf. Process. Syst., 33 (2020), 19667–19679.
    [38] B. Wu, S. Nair, R. Martin-Martin, L. Fei-Fei, C. Finn, Greedy hierarchical variational autoencoders for large-scale video prediction, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021), 2318–2328.
    [39] A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, et al., Zero-shot text-to-image generation, in International Conference on Machine Learning, (2021), 8821–8831.
    [40] P. Ghosh, M. S. Sajjadi, A. Vergari, M. Black, B. Schölkopf, From variational to deterministic autoencoders, preprint, arXiv: 1903.12436. https://doi.org/10.48550/arXiv.1903.12436
    [41] A. V. D. Oord, O. Vinyals, Neural discrete representation learning, Adv. Neural Inf. Process. Syst., 30 (2017).
    [42] A. Razavi, A. V. Oord, O. Vinyals, Generating diverse high-fidelity images with vq-vae-2, Adv. Neural Inf. Process. Syst., 32 (2019).
    [43] G. Zheng, Y. Yang, J. Carbonell, Convolutional normalizing flows, preprint, arXiv: 1711.02255. https://doi.org/10.48550/arXiv.1711.02255
    [44] E. Hoogeboom, R. Van Den Berg, M. Welling, Emerging convolutions for generative normalizing flows, in International Conference on Machine Learning, (2019), 2771–2780.
    [45] A. N. Gomez, M. Ren, R. Urtasun, R. B. Grosse, The reversible residual network: Backpropagation without storing activations, Adv. Neural Inf. Process. Syst., 30 (2017).
    [46] J. Jacobsen, A. Smeulders, E. Oyallon, i-revnet: Deep invertible networks, preprint, arXiv: 1802.07088. https://doi.org/10.48550/arXiv.1802.07088
    [47] T. Salimans, J. Ho, Progressive distillation for fast sampling of diffusion models, preprint, arXiv: 2202.00512. https://doi.org/10.48550/arXiv.2202.00512
    [48] E. Luhman, T. Luhman, Knowledge distillation in iterative generative models for improved sampling speed, preprint, arXiv: 2101.02388. https://doi.org/10.48550/arXiv.2101.02388
    [49] Z. Kong, W. Ping, On fast sampling of diffusion probabilistic models, preprint, arXiv: 2106.00132. https://doi.org/10.48550/arXiv.2106.00132
    [50] A. Q. Nichol, P. Dhariwal, Improved denoising diffusion probabilistic models, in International Conference on Machine Learning, (2021), 8162–8171.
    [51] D. Kingma, T. Salimans, B. Poole, J. Ho, Variational diffusion models, Adv. Neural Inf. Process. Syst., 34 (2021), 21696–21707.
    [52] R. San-Roman, E. Nachmani, L. Wolf, Noise estimation for generative diffusion models, preprint, arXiv: 2104.02600. https://doi.org/10.48550/arXiv.2104.02600
    [53] D. Watson, W. Chan, J. Ho, M. Norouzi, Learning fast samplers for diffusion models by differentiating through sample quality, in International Conference on Learning Representations, 2021.
    [54] D. Watson, J. Ho, M. Norouzi, W. Chan, Learning to efficiently sample from diffusion probabilistic models, preprint, arXiv: 2106.03802. https://doi.org/10.48550/arXiv.2106.03802
    [55] H. Zheng, P. He, W. Chen, M. Zhou, Truncated diffusion probabilistic models, preprint, arXiv: 2202.09671. https://doi.org/10.48550/arXiv.2202.09671
    [56] K. Pandey, A. Mukherjee, P. Rai, A. Kumar, Diffusevae: Efficient, controllable and high-fidelity generation from low-dimensional latents, preprint, arXiv: 2201.00308. https://doi.org/10.48550/arXiv.2201.00308
    [57] Q. Zhang, Y. Chen, Diffusion normalizing flow, Adv. Neural Inf. Process. Syst., 34 (2021), 16280–16291.
    [58] L. H. Li, M. Yatskar, D. Yin, C. Hsieh, K. Chang, Visualbert: A simple and performant baseline for vision and language, preprint, arXiv: 1908.03557. https://doi.org/10.48550/arXiv.1908.03557
    [59] L. Zhou, H. Palangi, L. Zhang, H. Hu, J. Corso, J. Gao, Unified vision-language pre-training for image captioning and vqa, in Proceedings of the AAAI Conference on Artificial Intelligence, (2020), 13041–13049.
    [60] H. Tan, M. Bansal, Lxmert: Learning cross-modality encoder representations from transformers, preprint, arXiv: 1908.07490. https://doi.org/10.48550/arXiv.1908.07490
    [61] J. Lu, D. Batra, D. Parikh, S. Lee, Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks, Adv. Neural Inf. Process. Syst., 32 (2019).
    [62] M. Tsimpoukelli, J. L. Menick, S. Cabi, S. M. Eslami, O. Vinyals, F. Hill, Multimodal few-shot learning with frozen language models, Adv. Neural Inf. Process. Syst., 34 (2021), 200–212.
    [63] O. Patashnik, Z. Wu, E. Shechtman, D. Cohen-Or, D. Lischinski, Styleclip: Text-driven manipulation of stylegan imagery, in Proceedings of the IEEE/CVF International Conference on Computer Vision, (2021), 2085–2094. https://doi.org/10.1109/ICCV48922.2021.00209
    [64] A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, et al., Glide: Towards photorealistic image generation and editing with text-guided diffusion models, preprint, arXiv: 2112.10741. https://doi.org/10.48550/arXiv.2112.10741
    [65] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, et al., Photorealistic text-to-image diffusion models with deep language understanding, Adv. Neural Inf. Process. Syst., 35 (2022), 36479–36494. https://doi.org/10.1145/3528233.3530757 doi: 10.1145/3528233.3530757
    [66] M. Chen, X. Tan, B. Li, Y. Liu, T. Qin, S. Zhao, et al., Adaspeech: Adaptive text to speech for custom voice, preprint, arXiv: 2103.00993. https://doi.org/10.48550/arXiv.2103.00993
    [67] H. Liang, H. Wang, J. Wang, S. You, Z. Sun, J. Wei, et al., JTAV: Jointly learning social media content representation by fusing textual, acoustic, and visual features, preprint, arXiv: 1806.01483. https://doi.org/10.48550/arXiv.1806.01483
    [68] Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, et al., Codebert: A pre-trained model for programming and natural languages, preprint, arXiv: 2002.08155. https://doi.org/10.48550/arXiv.2002.08155
    [69] W. U. Ahmad, S. Chakraborty, B. Ray, K. Chang, Unified pre-training for program understanding and generation, preprint, arXiv: 2103.06333. https://doi.org/10.48550/arXiv.2103.06333
    [70] I. Melnyk, P. Dognin, P. Das, Knowledge graph generation from text, preprint, arXiv: 2211.10511. https://doi.org/10.48550/arXiv.2211.10511
    [71] B. Distiawan, J. Qi, R. Zhang, W. Wang, GTR-LSTM: A triple encoder for sentence generation from RDF data, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 1 (2018), 1627–1637.
    [72] M. Li, J. Wang, Y. Chen, Y. Tang, Z. Wu, Y. Qi, et al., Low-dose CT image synthesis for domain adaptation imaging using a generative adversarial network with noise encoding transfer learning, IEEE Trans. Med. Imaging, 2023.
    [73] Q. Gao, Z. Li, J. Zhang, Y. Zhang, H. Shan, CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization, preprint, arXiv: 2304.01814. https://doi.org/10.48550/arXiv.2304.01814
    [74] Z. Huang, J. Zhang, Y. Zhang, H. Shan, DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising, IEEE Trans. Instrum. Meas., 71 (2021), 1–12. https://doi.org/10.1109/TIM.2021.3128703 doi: 10.1109/TIM.2021.3128703
    [75] B. Chen, S. Leng, L. Yu, D. Holmes III, J. Fletcher, C. McCollough, An open library of CT patient projection data, in Medical Imaging 2016: Physics of Medical Imaging, 9783 (2016), 330–335. https://doi.org/10.1117/12.2216823
    [76] X. Zhao, T. Yang, B. Li, X. Zhang, SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction, Comput. Biol. Med., 153 (2023), 106513. https://doi.org/10.1016/j.compbiomed.2022.106513 doi: 10.1016/j.compbiomed.2022.106513
    [77] C. Zhang, R. Barbano, B. Jin, Conditional variational autoencoder for learned image reconstruction, Computation, 9 (2021), 114. https://doi.org/10.3390/computation9110114 doi: 10.3390/computation9110114
    [78] G. Luo, M. Heide, M. Uecker, MRI reconstruction via data driven markov chain with joint uncertainty estimation, preprint, arXiv: 2202.01479. https://doi.org/10.48550/arXiv.2202.01479
    [79] Y. Gu, Z. Zeng, H. Chen, J. Wei, Y. Zhang, B. Chen, et al., MedSRGAN: medical images super-resolution using generative adversarial networks, Multimed. Tools Appl., 79 (2020), 21815–21840. https://doi.org/10.1007/s11042-020-08980-w doi: 10.1007/s11042-020-08980-w
    [80] A. A. A. Setio, A. Traverso, T. D. Bel, M. S. Berens, C. V. D. Bogaard, P. Cerello, et al., Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge, Med. Image Anal., 42 (2017), 1–13. https://doi.org/10.1016/j.media.2017.06.015 doi: 10.1016/j.media.2017.06.015
    [81] B. Vasudeva, P. Deora, S. Bhattacharya, P. M. Pradhan, Co-VeGAN: Complex-valued generative adversarial network for compressive sensing MR image reconstruction, preprint, arXiv: 2002.10523. https://doi.org/10.48550/arXiv.2002.10523
    [82] B. Landman, S. Warfield, Diencephalon standard challenge, 2013. https://doi.org/10.7303/syn3270351
    [83] N. Bien, P. Rajpurkar, R. L. Ball, J. Irvin, A. Park, E. Jones, et al., Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet, PLoS Med., 15 (2018), e1002699. https://doi.org/10.1371/journal.pmed.1002699 doi: 10.1371/journal.pmed.1002699
    [84] J. Zbontar, F. Knoll, A. Sriram, T. Murrell, Z. Huang, M. J. Muckley, et al., fastMRI: An open dataset and benchmarks for accelerated MRI, preprint, arXiv: 1811.08839. https://doi.org/10.48550/arXiv.1811.08839
    [85] Z. Yuan, M. Jiang, Y. Wang, B. Wei, Y. Li, P. Wang, et al., SARA-GAN: Self-attention and relative average discriminator based generative adversarial networks for fast compressed sensing MRI reconstruction, Front. Neuroinf., 14 (2020), 611666. https://doi.org/10.3389/fninf.2020.611666 doi: 10.3389/fninf.2020.611666
    [86] M. Zehni, Z. Zhao, UVTOMO-GAN: An adversarial learning based approach for unknown view X-ray tomographic reconstruction, in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), (2021), 1812–1816. https://doi.org/10.1109/ISBI48211.2021.9433970
    [87] B. Gajera, S. R. Kapil, D. Ziaei, J. Mangalagiri, E. Siegel, D. Chapman, CT-scan denoising using a charbonnier loss generative adversarial network, IEEE Access, 9 (2021), 84093–84109. https://doi.org/10.1109/ACCESS.2021.3087424 doi: 10.1109/ACCESS.2021.3087424
    [88] M. A. Gavrielides, L. M. Kinnard, K. J. Myers, J. Peregoy, W. F. Pritchard, R. Zeng, et al., Data from phantom FDA. The cancer imaging archive, Nat. Cancer Inst., Bethesda, MD, USA, Tech. Rep, 2015.
    [89] A. Aghabiglou, E. M. Eksioglu, MR image reconstruction based on densely connected residual generative adversarial network–DCR-GAN, in Advances in Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Kallithea, Rhodes, Greece, September 29–October 1, 2021, Proceedings 13, (2021), 679–689. https://doi.org/10.1007/978-3-030-88113-9_55
    [90] J. Lv, C. Wang, G. Yang, PIC-GAN: a parallel imaging coupled generative adversarial network for accelerated multi-channel MRI reconstruction, Diagnostics, 11 (2021), 61. https://doi.org/10.3390/diagnostics11010061 doi: 10.3390/diagnostics11010061
    [91] M. Jiang, M. Zhi, L. Wei, X. Yang, J. Zhang, Y. Li, et al., FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution, Comput. Med. Imaging. Graph., 92 (2021), 101969. https://doi.org/10.1016/j.compmedimag.2021.101969 doi: 10.1016/j.compmedimag.2021.101969
    [92] S. Kyung, J. Won, S. Pak, G. Hong, N. Kim, MTD-GAN: Multi-task discriminator based generative adversarial networks for low-dose CT denoising, in International Workshop on Machine Learning for Medical Image Reconstruction, (2022), 133–144. https://doi.org/10.1007/978-3-031-17247-2_14
    [93] H. Zhou, X. Liu, H. Wang, Q. Chen, R. Wang, Z. Pang, et al., The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network, Quant. Imaging Med. Surg., 12 (2022), 28. https://doi.org/10.21037/qims-21-182 doi: 10.21037/qims-21-182
    [94] M. Yaqub, F. Jinchao, S. Ahmed, K. Arshid, M. A. Bilal, M. P. Akhter, et al., Gan-tl: Generative adversarial networks with transfer learning for mri reconstruction, Appl. Sci., 12 (2022), 8841. https://doi.org/10.3390/app12178841 doi: 10.3390/app12178841
    [95] X. Liu, H. Du, J. Xu, B. Qiu, DBGAN: A dual-branch generative adversarial network for undersampled MRI reconstruction, Magn. Reson. Imaging, 89 (2022), 77–91. https://doi.org/10.1016/j.mri.2022.03.003 doi: 10.1016/j.mri.2022.03.003
    [96] K. Zhang, H. Hu, K. Philbrick, G. M. Conte, J. D. Sobek, P. Rouzrokh, et al., SOUP-GAN: Super-resolution MRI using generative adversarial networks, Tomography, 8 (2022), 905–919. https://doi.org/10.3390/tomography8020073 doi: 10.3390/tomography8020073
    [97] H. Chung, J. C. Ye, Score-based diffusion models for accelerated MRI, Med. Image Anal., 80 (2022), 102479. https://doi.org/10.1016/j.media.2022.102479 doi: 10.1016/j.media.2022.102479
    [98] A. Güngör, S. U. Dar, Ş. Öztürk, Y. Korkmaz, H. A. Bedel, G. Elmas, et al., Adaptive diffusion priors for accelerated MRI reconstruction, Med. Image Anal., (2023), 102872. https://doi.org/10.1016/j.media.2023.102872 doi: 10.1016/j.media.2023.102872
    [99] C. Peng, P. Guo, S. K. Zhou, V. M. Patel, R. Chellappa, Towards performant and reliable undersampled MR reconstruction via diffusion model sampling, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2022), 623–633. https://doi.org/10.1007/978-3-031-16446-0_59
    [100] A. D. Desai, A. M. Schmidt, E. B. Rubin, C. M. Sandino, M. S. Black, V. Mazzoli, et al., Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation, preprint, arXiv: 2203.06823. https://doi.org/10.48550/arXiv.2203.06823
    [101] Y. Xie, Q. Li, Measurement-conditioned denoising diffusion probabilistic model for under-sampled medical image reconstruction, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2022), 655–664. https://doi.org/10.1007/978-3-031-16446-0_62
    [102] X. Liu, Y. Xie, S. Diao, S. Tan, X. Liang, A diffusion probabilistic prior for low-dose CT image denoising, preprint, arXiv: 2305.15887. https://doi.org/10.48550/arXiv.2305.15887
    [103] Q. Gao, H. Shan, CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising, in Developments in X-Ray Tomography XIV, 2022.
    [104] Z. Cui, C. Cao, S. Liu, Q. Zhu, J. Cheng, H. Wang, et al., Self-score: Self-supervised learning on score-based models for mri reconstruction, preprint, arXiv: 2209.00835. https://doi.org/10.48550/arXiv.2209.00835
    [105] W. Xia, Q. Lyu, G. Wang, Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup, preprint, arXiv: 2209.15136. https://doi.org/10.48550/arXiv.2209.15136
    [106] B. Huang, L. Zhang, S. Lu, B. Lin, W. Wu, Q. Liu, One sample diffusion model in projection domain for low-dose CT imaging, preprint, arXiv: 2212.03630. https://doi.org/10.48550/arXiv.2212.03630
    [107] B. Zhao, T. Cheng, X. Zhang, J. Wang, H. Zhu, R. Zhao, et al., CT synthesis from MR in the pelvic area using residual transformer conditional GAN, Comput. Med. Imaging. Graph., 103 (2023), 102150. https://doi.org/10.1016/j.compmedimag.2022.102150 doi: 10.1016/j.compmedimag.2022.102150
    [108] X. Li, K. Shang, G. Wang, M. D. Butala, DDMM-Synth: A denoising diffusion model for cross-modal medical image synthesis with sparse-view measurement embedding, preprint, arXiv: 2303.15770. https://doi.org/10.48550/arXiv.2303.15770
    [109] W. Wei, E. Poirion, B. Bodini, M. Tonietto, S. Durrleman, O. Colliot, et al., Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis, Neuroimage, 223 (2020), 117308. https://doi.org/10.1016/j.neuroimage.2020.117308 doi: 10.1016/j.neuroimage.2020.117308
    [110] Q. Hu, H. Li, J. Zhang, Domain-adaptive 3D medical image synthesis: An efficient unsupervised approach, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2022), 495–504. https://doi.org/10.1007/978-3-031-16446-0_47
    [111] X. Meng, Y. Gu, Y. Pan, N. Wang, P. Xue, M. Lu, et al., A novel unified conditional score-based generative framework for multi-modal medical image completion, preprint, arXiv: 2207.03430. https://doi.org/10.48550/arXiv.2207.03430
    [112] V. Bharti, B. Biswas, K. K. Shukla, Qemcgan: quantized evolutionary gradient aware multiobjective cyclic gan for medical image translation, IEEE J. Biomed. Health Inf., 2023. https://doi.org/10.1109/JBHI.2023.3263434 doi: 10.1109/JBHI.2023.3263434
    [113] O. S. Al-Kadi, I. Almallahi, A. Abu-Srhan, A. M. Abushariah, W. Mahafza, Unpaired MR-CT brain dataset for unsupervised image translation, Data Brief, 42 (2022), 108109. https://doi.org/10.1016/j.dib.2022.108109 doi: 10.1016/j.dib.2022.108109
    [114] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imaging, 34 (2014), 1993–2024. https://doi.org/10.1109/TMI.2014.2377694 doi: 10.1109/TMI.2014.2377694
    [115] T. Nyholm, S. Svensson, S. Andersson, J. Jonsson, M. Sohlin, C. Gustafsson, et al., MR and CT data with multiobserver delineations of organs in the pelvic area—Part of the Gold Atlas project, Med. Phys., 45 (2018), 1295–1300. https://doi.org/10.1002/mp.12748 doi: 10.1002/mp.12748
    [116] L. Jiang, Y. Mao, X. Chen, X. Wang, C. Li, CoLa-Diff: Conditional latent diffusion model for multi-modal MRI synthesis, preprint, arXiv: 2303.14081. https://doi.org/10.48550/arXiv.2303.14081
    [117] M. Özbey, O. Dalmaz, S. U. Dar, H. A. Bedel, Ş. Özturk, A. Güngör, et al., Unsupervised medical image translation with adversarial diffusion models, IEEE Trans. Med. Imaging, 2023. https://doi.org/10.1109/TMI.2023.3290149 doi: 10.1109/TMI.2023.3290149
    [118] J. Peng, R. L. Qiu, J. F. Wynne, C. Chang, S. Pan, T. Wang, et al., CBCT-based synthetic CT image generation using conditional denoising diffusion probabilistic model, preprint, arXiv: 2303.02649. https://doi.org/10.48550/arXiv.2303.02649
    [119] Q. Lyu, G. Wang, Conversion between CT and MRI images using diffusion and score-matching models, preprint, arXiv: 2209.12104. https://doi.org/10.48550/arXiv.2209.12104
    [120] S. Pan, E. Abouei, J. Wynne, T. Wang, R. L. Qiu, Y. Li, et al., Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model, preprint, arXiv: 2305.19467. https://doi.org/10.48550/arXiv.2305.19467
    [121] F. Bazangani, F. J. Richard, B. Ghattas, E. Guedj, FDG-PET to T1 weighted MRI translation with 3D elicit generative adversarial network (E-GAN), Sensors, 22 (2022), 4640. https://doi.org/10.3390/s22124640 doi: 10.3390/s22124640
    [122] H. Emami, M. Dong, C. Glide-Hurst, CL-GAN: Contrastive learning-based generative adversarial network for modality transfer with limited paired data, in European Conference on Computer Vision, (2022), 527–542. https://doi.org/10.1007/978-3-031-25066-8_30
    [123] I. S. A. Abdelhalim, M. F. Mohamed, Y. B. Mahdy, Data augmentation for skin lesion using self-attention based progressive generative adversarial network, Expert Syst. Appl., 165 (2021), 113922. https://doi.org/10.1016/j.eswa.2020.113922 doi: 10.1016/j.eswa.2020.113922
    [124] A. A. E. Ambita, E. N. V. Boquio, P. C. Naval Jr, Covit-gan: vision transformer forcovid-19 detection in CT scan imageswith self-attention GAN for data augmentation, in International Conference on Artificial Neural Networks, (2021), 587–598. https://doi.org/10.1007/978-3-030-86340-1_47
    [125] M. Hajij, G. Zamzmi, R. Paul, L. Thukar, Normalizing flow for synthetic medical images generation, in 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), (2022), 46–49. https://doi.org/10.1109/HI-POCT54491.2022.9744072
    [126] R. Summers, Nih chest x-ray dataset of 14 common thorax disease categories, NIH Clinical Center: Bethesda, MD, USA, 2019.
    [127] P. A. Moghadam, S. V. Dalen, K. C. Martin, J. Lennerz, S. Yip, H. Farahani, et al., A morphology focused diffusion probabilistic model for synthesis of histopathology images, in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, (2023), 2000–2009. https://doi.org/10.1109/WACV56688.2023.00204
    [128] S. Shahriar, S. Allana, M. H. Fard, R. Dara. A survey of privacy risks and mitigation strategies in the artificial intelligence life cycle, IEEE Access, 2023. https://doi.org/10.1109/ACCESS.2023.3287195 doi: 10.1109/ACCESS.2023.3287195
    [129] R. L. Grossman, A. P. Heath, V. Ferretti, H. E. Varmus, D. R. Lowy, W. A. Kibbe, et al., Toward a shared vision for cancer genomic data, N. Engl. J. Med., 375 (2016), 1109–1112. https://doi.org/10.1056/NEJMp1607591 doi: 10.1056/NEJMp1607591
    [130] S. Pan, T. Wang, R. L. Qiu, M. Axente, C. Chang, J. Peng, et al., 2D medical image synthesis using transformer-based denoising diffusion probabilistic model, Phys. Med. Biol., 68 (2023), 105004. https://doi.org/10.1088/1361-6560/acca5c doi: 10.1088/1361-6560/acca5c
    [131] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers, Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2017), 2097–2106. https://doi.org/10.1109/CVPR.2017.369
    [132] O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P. Heng, et al., Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?, IEEE Trans. Med. Imaging, 37 (2018), 2514–2525. https://doi.org/10.1109/TMI.2018.2837502 doi: 10.1109/TMI.2018.2837502
    [133] B. Landman, Z. Xu, J. E. Igelsias, M. Styner, T. R. Langerak, A. Klein, 2015 miccai multi-atlas labeling beyond the cranial vault workshop and challenge, in Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, 2015.
    [134] R. Zhang, W. Lu, J. Gao, Y. Tian, X. Wei, C. Wang, et al., RFI-GAN: A reference-guided fuzzy integral network for ultrasound image augmentation, Inf. Sci., 623 (2023), 709–728. https://doi.org/10.1016/j.ins.2022.12.026 doi: 10.1016/j.ins.2022.12.026
    [135] R. Zhang, W. Lu, X. Wei, J. Zhu, H. Jiang, Z. Liu, et al., A progressive generative adversarial method for structurally inadequate medical image data augmentation, IEEE J. Biomed. Health Inf., 26 (2021), 7–16. https://doi.org/10.1109/JBHI.2021.3101551 doi: 10.1109/JBHI.2021.3101551
    [136] K. Guo, J. Chen, T. Qiu, S. Guo, T. Luo, T. Chen, et al., MedGAN: An adaptive GAN approach for medical image generation, Comput. Biol. Med., (2023), 107119. https://doi.org/10.1016/j.compbiomed.2023.107119 doi: 10.1016/j.compbiomed.2023.107119
    [137] B. Kim, J. C. Ye, Diffusion deformable model for 4D temporal medical image generation, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2022), 539–548. https://doi.org/10.1007/978-3-031-16431-6_51
    [138] W. H. Pinaya, P. Tudosiu, J. Dafflon, P. F. D. Costa, V. Fernandez, P. Nachev, et al., Brain imaging generation with latent diffusion models, in MICCAI Workshop on Deep Generative Models, (2022), 117–126. https://doi.org/10.1007/978-3-031-18576-2_12
    [139] P. Tschandl, C. Rosendahl, H. Kittler, The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Sci. Data, 5 (2018), 1–9. https://doi.org/10.1038/sdata.2018.161 doi: 10.1038/sdata.2018.161
    [140] J. Nada, S. Bougleux, J. Lapuyade-Lahorgue, S. Ruan, F. Ghazouani, MR image synthesis using Riemannian geometry constrained in VAE, in 2022 16th IEEE International Conference on Signal Processing (ICSP), (2022), 485–488. https://doi.org/10.1109/ICSP56322.2022.9965357
    [141] H. Dai, Z. Liu, W. Liao, X. Huang, Y. Cao, Z. Wu, et al., AugGPT: Leveraging ChatGPT for text data augmentation, preprint, arXiv: 2302.13007. https://doi.org/10.48550/arXiv.2302.13007
    [142] H. Li, Y. Wu, V. Schlegel, R. Batista-Navarro, T. Nguyen, A. R. Kashyap, et al., PULSAR: Pre-training with extracted healthcare terms for summarising patients' problems and data augmentation with black-box large language models, preprint, arXiv: 2306.02754. https://doi.org/10.48550/arXiv.2306.02754
    [143] D. Jin, E. Pan, N. Oufattole, W. Weng, H. Fang, P. Szolovits, What disease does this patient have? a large-scale open domain question answering dataset from medical exams, Appl. Sci., 11 (2021), 6421. https://doi.org/10.3390/app11146421 doi: 10.3390/app11146421
    [144] A. Pal, L. K. Umapathi, M. Sankarasubbu, Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering, in Conference on Health, Inference, and Learning, (2022), 248–260.
    [145] D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, et al. Measuring massive multitask language understanding, preprint, arXiv: 2009.03300. https://doi.org/10.48550/arXiv.2009.03300
    [146] Q. Jin, B. Dhingra, Z. Liu, W. W. Cohen, X. Lu, Pubmedqa: A dataset for biomedical research question answering, preprint, arXiv: 1909.06146. https://doi.org/10.48550/arXiv.1909.06146
    [147] A. B. Abacha, E. Agichtein, Y. Pinter, D. Demner-Fushman, Overview of the medical question answering task at TREC 2017 LiveQA, in TREC, (2017), 1–12.
    [148] A. B. Abacha, Y. Mrabet, M. Sharp, T. R. Goodwin, S. E. Shooshan, D. Demner-Fushman, Bridging the gap between consumers' medication questions and trusted answers., in MedInfo, (2019), 25–29.
    [149] K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, et al., Large language models encode clinical knowledge, preprint, arXiv: 2212.13138. https://doi.org/10.48550/arXiv.2212.13138
    [150] A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, et al., Palm: Scaling language modeling with pathways, preprint, arXiv: 2204.02311. https://doi.org/10.48550/arXiv.2204.02311
    [151] C. Wu, X. Zhang, Y. Zhang, Y. Wang, W. Xie, Pmc-llama: Further finetuning llama on medical papers, preprint, arXiv: 2304.14454. https://doi.org/10.48550/arXiv.2304.14454
    [152] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. Lachaux, T. Lacroix, et al., Llama: Open and efficient foundation language models, preprint, arXiv: 2302.13971. https://doi.org/10.48550/arXiv.2302.13971
    [153] K. Lo, L. L. Wang, M. Neumann, R. Kinney, D. S. Weld, S2ORC: The semantic scholar open research corpus, preprint, arXiv: 1911.02782. https://doi.org/10.48550/arXiv.1911.02782
    [154] O. Thawkar, A. Shaker, S. S. Mullappilly, H. Cholakkal, R. M. Anwer, S. Khan, et al., Xraygpt: Chest radiographs summarization using medical vision-language models, preprint, arXiv: 2306.07971. https://doi.org/10.48550/arXiv.2306.07971
    [155] W. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, et al., Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, Available from: https://vicuna.lmsys.org.
    [156] A. E. Johnson, T. J. Pollard, S. J. Berkowitz, N. R. Greenbaum, M. P. Lungren, C. Deng, et al., MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports, Sci. Data, 6 (2019), 317. https://doi.org/10.1038/s41597-019-0322-0 doi: 10.1038/s41597-019-0322-0
    [157] D. Demner-Fushman, M. D. Kohli, M. B. Rosenman, S. E. Shooshan, L. Rodriguez, S. Antani, et al., Preparing a collection of radiology examinations for distribution and retrieval, J. Am. Med. Inf. Assoc., 23 (2016), 304–310. https://doi.org/10.1093/jamia/ocv080 doi: 10.1093/jamia/ocv080
    [158] J. Zhou, X. He, L. Sun, J. Xu, X. Chen, Y. Chu, et al., SkinGPT-4: An interactive dermatology diagnostic system with visual large language model, medRxiv, (2023), 2023–2026.
    [159] R. Daneshjou, M. Yuksekgonul, Z. R. Cai, R. Novoa, J. Y. Zou, Skincon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis, Adv. Neural Inf. Process. Syst., 35 (2022), 18157–18167.
    [160] D. Zhu, J. Chen, X. Shen, X. Li, M. Elhoseiny, Minigpt-4: Enhancing vision-language understanding with advanced large language models, preprint, arXiv: 2304.10592. https://doi.org/10.48550/arXiv.2304.10592
    [161] G. Zeng, W. Yang, Z. Ju, Y. Yang, S. Wang, R. Zhang, et al., MedDialog: Large-scale medical dialogue datasets, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), (2020), 9241–9250. https://doi.org/10.18653/v1/2020.emnlp-main.743
    [162] A. B. Abacha, Y. Mrabet, M. Sharp, T. R. Goodwin, S. E. Shooshan, D. Demner-Fushman, Bridging the Gap Between Consumers' Medication Questions and Trusted Answers, in MedInfo, (2019), 25–29.
    [163] M. Savery, A. B. Abacha, S. Gayen, D. Demner-Fushman, Question-driven summarization of answers to consumer health questions, Sci. Data, 7 (2020), 322. https://doi.org/10.1038/s41597-020-00667-z doi: 10.1038/s41597-020-00667-z
    [164] H. Yuan, Z. Yuan, R. Gan, J. Zhang, Y. Xie, S. Yu, BioBART: Pretraining and evaluation of a biomedical generative language model, preprint, arXiv: 2204.03905. https://doi.org/10.48550/arXiv.2204.03905
    [165] R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, et al., BioGPT: generative pre-trained transformer for biomedical text generation and mining, Brief. BioInf., 23 (2022), bbac409. https://doi.org/10.1093/bib/bbac409 doi: 10.1093/bib/bbac409
    [166] J. Li, Y. Sun, R. J. Johnson, D. Sciaky, C. Wei, R. Leaman, et al., BioCreative V CDR task corpus: a resource for chemical disease relation extraction, Database, 2016 (2016). https://doi.org/10.1093/database/baw068 doi: 10.1093/database/baw068
    [167] Y. Hou, Y. Xia, L. Wu, S. Xie, Y. Fan, J. Zhu, et al., Discovering drug-target interaction knowledge from biomedical literature, Bioinformatics, 38 (2022), 5100–5107. https://doi.org/10.1093/bioinformatics/btac648 doi: 10.1093/bioinformatics/btac648
    [168] M. Herrero-Zazo, I. Segura-Bedmar, P. Martínez, T. Declerck, The DDI corpus: An annotated corpus with pharmacological substances and drug–drug interactions, J. Biomed. Inf., 46 (2013), 914–920. https://doi.org/10.1016/j.jbi.2013.07.011 doi: 10.1016/j.jbi.2013.07.011
    [169] S. Baker, I. Silins, Y. Guo, I. Ali, J. Högberg, U. Stenius, et al., Automatic semantic classification of scientific literature according to the hallmarks of cancer, Bioinformatics, 32 (2016), 432–440. https://doi.org/10.1093/bioinformatics/btv585 doi: 10.1093/bioinformatics/btv585
    [170] A. Venigalla, J. Frankle, M. Carbin, Biomedlm: a domain-specific large language model for biomedical text, MosaicML. Accessed: Dec, 23 (2022), 2.
    [171] G. Balikas, A. Krithara, I. Partalas, G. Paliouras, Bioasq: A challenge on large-scale biomedical semantic indexing and question answering, in Multimodal Retrieval in the Medical Domain: First International Workshop, MRMD 2015, Vienna, Austria, March 29, 2015, Revised Selected Papers, (2015), 26–39. https://doi.org/10.1007/978-3-319-24471-6_3
    [172] A. B. Abacha, Y. M Rabet, Y. Zhang, C. Shivade, C. Langlotz, D. Demner-Fushman, Overview of the MEDIQA 2021 shared task on summarization in the medical domain, in Proceedings of the 20th Workshop on Biomedical Language Processing, (2021), 74–85. https://doi.org/10.18653/v1/2021.bionlp-1.8
    [173] S. Mohan, D. Li, Medmentions: A large biomedical corpus annotated with umls concepts, preprint, arXiv: 1902.09476. https://doi.org/10.48550/arXiv.1902.09476
    [174] R. I. Doğan, R. Leaman, Z. Lu, NCBI disease corpus: a resource for disease name recognition and concept normalization, J. Biomed. Inf., 47 (2014), 1–10. https://doi.org/10.1016/j.jbi.2013.12.006 doi: 10.1016/j.jbi.2013.12.006
    [175] M. Basaldella, F. Liu, E. Shareghi, N. Collier, COMETA: A corpus for medical entity linking in the social media, preprint, arXiv: 2010.03295. https://doi.org/10.48550/arXiv.2010.03295
    [176] N. Limsopatham, N. Collier, Normalising medical concepts in social media texts by learning semantic representation, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long papers), (2016), 1014–1023. https://doi.org/10.18653/v1/P16-1096
    [177] S. Pradhan, N. Elhadad, B. R. South, D. Martinez, L. M. Christensen, A. Vogel, et al., Task 1: ShARe/CLEF eHealth Evaluation Lab 2013., CLEF (working notes), 1179 (2013).
    [178] D. L. Mowery, S. Velupillai, B. R. South, L. Christensen, D. Martinez, L. Kelly, et al., Task 2: ShARe/CLEF eHealth evaluation lab 2014, in Proceedings of CLEF 2014, (2014).
    [179] S. Karimi, A. Metke-Jimenez, M. Kemp, C. Wang, Cadec: A corpus of adverse drug event annotations, J. Biomed. Inf., 55 (2015), 73–81. https://doi.org/10.1016/j.jbi.2015.03.010 doi: 10.1016/j.jbi.2015.03.010
    [180] J. Kim, T. Ohta, Y. Tateisi, J. I. Tsujii, GENIA corpus—a semantically annotated corpus for bio-textmining, Bioinformatics, 19 (2003), i180–i182. https://doi.org/10.1093/bioinformatics/btg1023 doi: 10.1093/bioinformatics/btg1023
    [181] Y. Li, Z. Li, K. Zhang, R. Dan, Y. Zhang, Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge, preprint, arXiv: 2303.14070. https://doi.org/10.48550/arXiv.2303.14070
    [182] A. Toma, P. R. Lawler, J. Ba, R. G. Krishnan, B. B. Rubin, B. Wang, Clinical camel: An open-source expert-level medical language model with dialogue-based knowledge encoding, preprint, arXiv: 2305.12031. https://doi.org/10.48550/arXiv.2305.12031
    [183] G. Wang, G. Yang, Z. Du, L. Fan, X. Li, ClinicalGPT: Large language models finetuned with diverse medical data and comprehensive evaluation, preprint, arXiv: 2306.09968. https://doi.org/10.48550/arXiv.2306.09968
    [184] S. Zhang, X. Zhang, H. Wang, L. Guo, S. Liu, Multi-scale attentive interaction networks for chinese medical question answer selection, IEEE Access, 6 (2018), 74061–74071. https://doi.org/10.1109/ACCESS.2018.2883637 doi: 10.1109/ACCESS.2018.2883637
    [185] T. M. Lai, C. Zhai, H. Ji, KEBLM: Knowledge-enhanced biomedical language models, J. Biomed. Inf., 143 (2023), 104392. https://doi.org/10.1016/j.jbi.2023.104392 doi: 10.1016/j.jbi.2023.104392
    [186] J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, et al., BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36 (2020), 1234–1240. https://doi.org/10.1093/bioinformatics/btz682 doi: 10.1093/bioinformatics/btz682
    [187] I. Beltagy, K. Lo, A. Cohan, SciBERT: A pretrained language model for scientific text, preprint, arXiv: 1903.10676. https://doi.org/10.48550/arXiv.1903.10676
    [188] A. Romanov, C. Shivade, Lessons from natural language inference in the clinical domain, preprint, arXiv: 1808.06752. https://doi.org/10.48550/arXiv.1808.06752
    [189] H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, et al., Scaling instruction-finetuned language models, preprint, arXiv: 2210.11416. https://doi.org/10.48550/arXiv.2210.11416
    [190] Y. Gao, T. Miller, M. Afshar, D. Dligach, BioNLP Workshop 2023 Shared Task 1A: Problem List Summarization, in Proceedings of the 22nd Workshop on Biomedical Language Processing, 2023.
    [191] J. Hu, Z. Li, Z. Chen, Z. Li, X. Wan, T. Chang, Graph enhanced contrastive learning for radiology findings summarization, preprint, arXiv: 2204.00203. https://doi.org/10.48550/arXiv.2204.00203
    [192] C. Ma, Z. Wu, J. Wang, S. Xu, Y. Wei, Z. Liu, et al., ImpressionGPT: an iterative optimizing framework for radiology report summarization with chatGPT, preprint, arXiv: 2304.08448. https://doi.org/10.48550/arXiv.2304.08448
    [193] B. Pang, E. Nijkamp, W. Kryściński, S. Savarese, Y. Zhou, C. Xiong, Long document summarization with top-down and bottom-up inference, preprint, arXiv: 2203.07586. https://doi.org/10.48550/arXiv.2203.07586
    [194] G. Frisoni, P. Italiani, S. Salvatori, G. Moro, Cogito ergo summ: abstractive summarization of biomedical papers via semantic parsing graphs and consistency rewards, in Proceedings of the AAAI Conference on Artificial Intelligence, (2023), 12781–12789. https://doi.org/10.1609/aaai.v37i11.26503
    [195] Y. Guo, W. Qiu, Y. Wang, T. Cohen, Automated lay language summarization of biomedical scientific reviews, in Proceedings of the AAAI Conference on Artificial Intelligence, (2021), 160–168. https://doi.org/10.1609/aaai.v35i1.16089
    [196] S. Casper, X. Davies, C. Shi, T. K. Gilbert, J. Scheurer, J. Rando, et al., Open problems and fundamental limitations of reinforcement learning from human feedback, preprint, arXiv: 2307.15217. https://doi.org/10.48550/arXiv.2307.15217
    [197] O. Ostapenko, T. Lesort, P. Rodriguez, M. R. Arefin, A. Douillard, I. Rish, et al., Continual learning with foundation models: An empirical study of latent replay, in Conference on Lifelong Learning Agents, (2022), 60–91.
    [198] I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, I. Androutsopoulos, LEGAL-BERT: The muppets straight out of law school, preprint, arXiv: 2010.02559. https://doi.org/10.48550/arXiv.2010.02559
    [199] J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, et al., Training compute-optimal large language models, preprint, arXiv: 2203.15556. https://doi.org/10.48550/arXiv.2203.15556
    [200] A. Aghajanyan, L. Yu, A. Conneau, W. Hsu, K. Hambardzumyan, S. Zhang, et al., Scaling laws for generative mixed-modal language models, preprint, arXiv: 2301.03728. https://doi.org/10.48550/arXiv.2301.03728
    [201] D. Shah, H. A. Schwartz, D. Hovy, Predictive biases in natural language processing models: A conceptual framework and overview, preprint, arXiv: 2301.03728. https://doi.org/10.48550/arXiv.2301.03728
    [202] Y. Dong, N. Liu, B. Jalaian, J. Li, Edits: Modeling and mitigating data bias for graph neural networks, in Proceedings of the ACM Web Conference 2022, (2022), 1259–1269. https://doi.org/10.1145/3485447.3512173
    [203] H. Zhao, W. Zhou, D. Chen, T. Wei, W. Zhang, N. Yu, Multi-attentional deepfake detection, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021), 2185–2194. https://doi.org/10.1109/CVPR46437.2021.00222
    [204] A. Brauneck, L. Schmalhorst, M. M. K. Majdabadi, M. Bakhtiari, U. Völker, J. Baumbach, et al., Federated machine learning, privacy-enhancing technologies, and data protection laws in medical research: Scoping review, J. Med. Internet Res., 25 (2023), e41588. https://doi.org/10.2196/41588 doi: 10.2196/41588
    [205] Q. Yang, Y. Liu, T. Chen, Y. Tong, Federated machine learning: Concept and applications, ACM Trans. Intell. Syst. Technol., 10 (2019), 1–19. https://doi.org/10.1145/3298981 doi: 10.1145/3298981
    [206] P. Zhang, M. N. K. Boulos, Generative AI in medicine and healthcare: promises, opportunities and challenges, Future Internet, 15 (2023), 286. https://doi.org/10.3390/fi15090286 doi: 10.3390/fi15090286
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5051) PDF downloads(1004) Cited by(7)

Article outline

Figures and Tables

Figures(9)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog