Abacha BA, Hasan SA, Datla VV, Demner-Fushman D, Müller H (2019) Vqa-med: overview of the medical visual question answering task at imageclef 2019. Proceedings of Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-2380/paper_272.pdf
Alayrac JB, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, Ring R, Rutherford E, Cabi S, Han T, Gong Z, Samangooei S, Moteiro M, Menick J, Borgeaud S, Brock A, Nematzadeh A, Sharifzadeh S, Binkowski M, Barreira R, Vinyals O, Zisserman A (2022) Flamingo: a visual language model for few-shot learning. Adv Neural Inf Process Syst 35: 23716−23736
Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S, Schuh P, Shi K, Tsvyashchenko S, Maynez J, Rao A, Barnes P, Tay Y, Shazeer N, Prabhakaran V, Reif E, Du N, Hutchinson B, Pope R, Bradbury J, Austin J, Isard M, Gur-Ari G, Yin P, Duke T, Levskaya A, Ghemawat S, Dev S, Michalewski H, Garcia X, Misra V, Robinson K, Fedus L, Zhou D, Ippolito D, Luan D, Lim H, Zoph B, Spiridonov A, Sepassi R, Dohan D, Agrawal S, Omernick M, Dai AM, Pillai TS, Pellat M, Lewkowycz A, Moreira E, Child R, Polozov O, Lee K, Zhou Z, Wang X, Saeta B, Diaz M, Firat O, Catasta M, Wei J, Meier-Hellstern K, Eck D, Dean J, Petrov S, Fiedel N (2023) Palm: Scaling language modeling with pathways. J Mach Learn Res 24(240): 1−113
Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33: 1877−1901
Cai X, Liu S, Han J, Yang L, Liu Z, Liu T (2021) ChestXRayBERT: a pretrained language model for chest radiology report summarization. IEEE Trans Multimed 25: 845−855 doi: 10.1109/TMM.2021.3132724
Chen J, Zhu D, Shen X, Li X, Liu Z, Zhang P, Krishnamoorthi R, Chandra V, Xiong Y, Elhoseiny M (2023a) Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv: 2310.09478. https://doi.org/10.48550/arXiv.2310.09478
Chen YC, Li L, Yu L, El Kholy A, Ahmed F, Gan Z, Liu J (2020) Uniter: universal image-text representation learning. European conference on computer vision. pp. 104−120
Chen Z, Cano AH, 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 MA, Jaggi M, Bosselut A (2023b) MEDITRON-70B: scaling medical pretraining for large language model. arXiv: 2311.16079. https://doi.org/10.48550/arXiv.2311.16079
Cheng J, Ye J, Deng Z, Chen J, Li T, Wang H, Su Y, Huang Z, Chen J, Jiang L, Sun H, He J, Zhang S, Zhu M, Qiao Y (2023) SAM-Med2D. arXiv: 2308.16184. https://doi.org/10.48550/arXiv.2308.16184
Cui Y, Che W, Liu T, Qin B, Wang S, Hu G (2020) Revisiting pre-trained models for Chinese natural language processing. arXiv: 2004.13922. https://doi.org/10.48550/arXiv.2004.13922
Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv: 1810.04805. https://doi.org/10.48550/arXiv.1810.04805
Dong L, Yang N, Wang W, Wei F, Liu X, Wang Y, Gao J, Zhou M, Hon HW (2019) Unified language model pre-training for natural language understanding and generation. Proceedings of the 33rd International Conference on Neural Information Processing Systems. pp. 13063–13075
Driess D, Xia F, Sajjadi MS, Lynch C, Chowdhery A, Ichter B, Wahid A, Tompson J, Vuong Q, Yu T, Huang W, Chebotar Y, Sermanet P, Duckworth D, Levine S, Vanhoucke V, Hausman K, Toussaint M, Greff K, Zeng A, Mordatch I, Florence P (2023) PaLM-E: an embodied multimodal language model. arXiv: 2303.03378. https://doi.org/10.48550/arXiv.2303.03378
Du N, Huang Y, Dai AM, Tong S, Lepikhin D, Xu Y, Krikun M, Zhou Y, Yu AW, Firat O, Zoph B, Fedus L, Bosma M, Zhou Z, Wang T, Wang YE, Webster K, Pellat M, Robinson K, Meier-Hellstern K, Duke T, Dixon L, Zhang K, Le QV, Wu Y, Chen Z, Cui C (2022) Glam: efficient scaling of language models with mixture-of-experts. Proceedings of the 39th International Conference on Machine Learning. pp. 5547−5569
Eslami S, de Melo G, Meinel C (2021) Does CLIP benefit visual question answering in the medical domain as much as it does in the general domain? arXiv: 2112.13906. https://doi.org/10.48550/arXiv.2112.13906
Gardères F, Ziaeefard M, Abeloos B, Lecue F (2020) Conceptbert: concept-aware representation for visual question answering. Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 489−498
Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H (2021) Domain-specific language model pretraining for biomedical natural language processing. ACM Trans ComputHealthc 3(1): 1−23
Hu X, Gu L, Kobayashi K, An Q, Chen Q, Lu Z, Su C, Harada T, Zhu Y (2023) Interpretable medical image visual question answering via multi-modal relationship graph learning. arXiv: 2302.09636. https://doi.org/10.48550/arXiv.2302.09636
Kanakarajan KR, Kundumani B, Sankarasubbu M (2021) BioELECTRA: pretrained biomedical text encoder using discriminators. Proceedings of the 20th Workshop on Biomedical Language Processing. pp. 143−154
Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo WY, Dolla´r P, and Girshick R (2023) Segment anything. arXiv: 2304.02643. https://doi.org/10.48550/arXiv.2304.02643
Kim S, Joo SJ, Kim D, Jang J, Ye S, Shin J, Seo M (2023) The COT COLLECTION: improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning. arXiv: 2305.14045. https://doi.org/10.48550/arXiv.2305.14045
Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: a lite bert for self-supervised learning of language representations. arXiv: 1909.11942. https://doi.org/10.48550/arXiv.1909.11942
Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2019) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. arXiv: 1901.08746. https://doi.org/10.48550/arXiv.1901.08746
Li C, Wong C, Zhang S, Usuyama N, Liu H, Yang J, Naumann T, Poon H, Gao J (2023a) LLaVA-Med: large language-and-vision assistant for biomedicine. arXiv: 2304.04342. https://doi.org/10.48550/arXiv.2304.04342
Liévin V, Hother CE, Motzfeldt AG, Winther O (2022) Can large language models reason about medical questions? arXiv: 2207.08143. https://doi.org/10.48550/arXiv.2207.08143
Li P, Liu G, Tan L, Liao J, Zhong S (2023b) Self-supervised vision-language pretraining for medial visual question answering. arXiv: 2211.13594. https://doi.org/10.48550/arXiv.2211.13594
Liu Y, Wang Z, Xu D, Zhou L (2023) Q2ATransformer: Improving medical vqa via an answer querying decoder. arXiv: 2304.01611. https://doi.org/10.48550/arXiv.2304.01611
Lu J, Batra D, Parikh D, Lee S (2019) Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv: 1908.02265. https://doi.org/10.48550/arXiv.1908.02265
Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, and Liu TY (2022) BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform 23(6): bbac409. https://doi.org/10.1093/bib/bbac409 doi: 10.1093/bib/bbac409
Luo Y, Zhang J, Fan S, Yang K, Wu Y, Qiao M, Nie Z (2023) BioMedGPT: open multimodal generative pre-trained transformer for biomedicine. arXiv: 2308.09442. https:// doi.org/10.48550/arXiv.2308.09442
Ma L, Han J, Wang Z, Zhang D (2023) CephGPT-4: an interactive multimodal cephalometric measurement and diagnostic system with visual large language model. arXiv: 2307.07518. https://doi.org/10.48550/arXiv.2307.07518
Manmadhan S, Kovoor BC (2023) Parallel multi-head attention and term-weighted question embedding for medical visual question answering. Mult Tools Appl 82: 34937−34958 doi: 10.1007/s11042-023-14981-2
Moor M, Huang Q, Wu S, Yasunaga M, Zakka C, Dalmia Y, Reis EP, Rajpurkar P, Leskovec J (2023) Med-Flamingo: a multimodal medical few-shot learner. arXiv: 2307.15189. https://doi.org/10.48550/arXiv.2307.15189
Nori H, King N, McKinney SM, Carignan D, Horvitz E (2023) Capabilities of GPT-4 on medical challenge problems. arXiv: 2303.13375. https://doi.org/10.48550/arXiv.2303.13375
OpenAI (2022) Introducing ChatGPT. https://openai.com/blog/chatgpt
Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, 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) Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 35: 27730−27744
Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, and Sutskever I (2021) Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning. pp. 8748−8763
Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8): 9. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(1): 5485−5551
Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Sutskever I (2021) Zero-shot text-to-image generation. Proceedings of the 38th International Conference on Machine Learning. pp. 8821−8831
Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684−10695
Scao TL, Fan A, Akiki C, Pavlick E, Ili ́c S, Hesslow D, Castagné R, Luccioni AS, Yvon F, Gallé M, Tow J, Rush AM, Biderman S, Webson A, Ammanamanchi PS, Wang T, Sagot B, Muennighoff N, Moral AV, Ruwase O, Bawden R, Bekman S, Major AM, Wolf T, Beltagy I, Nguyen H, Saulnier L, Tan S, Suarez PO, Sanh V, Lauren ̧con H, Jernite Y, Launay J, Mitchell M, Raffel C (2022) BLOOM: a 176b-parameter open-access multilingual language model. arXiv: 2211.05100. https://doi.org/10.48550/arXiv.2211.05100
Sharma D, Purushotham S, Reddy CK (2021) MedFuseNet: an attention-based multimodal deep learning model for visual question answering in the medical domain. Sci Rep 11(1):19826. https://doi.org/10.1038/s41598-021-98390-1
Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S, Payne P, Seneviratne M, Gamble P, Kelly C, Schärli N, Chowdhery A, Mansfield P, Agüera y Arcas B, Webster D, Corrado GS, Matias Y, Chou K, Gottweis J, Tomasev N, Liu Y, Rajkomar A, Barral J, Semturs C, Karthikesalingam A, Natarajan V (2022) Large language models encode clinical knowledge. arXiv: 2212.13138. https://doi.org/10.48550/arXiv.2212.13138
Singhal K, Tu T, Gottweis J, Sayres R, Wulczyn E, Hou L, Clark K, Pfohl S, Cole-Lewis H, Neal D, Schaekermann M, Wang A, Amin M, Lachgar S, Mansfield P, Prakash S, Green B, Dominowska E, Aguera y Arcas B, Tomasev N, Liu Y, Wong R, Semturs C, Mahdavi SS, Barral J, Webster D, Corrado GS, Matias Y, Azizi S, Karthikesalingam A, Natarajan V (2023) Towards expert-level medical question answering with large language models. arXiv: 2305.09617. https://doi.org/10.48550/arXiv.2305.09617
Tan H, Bansal M (2019) Lxmert: learning cross-modality encoder representations from transformers. arXiv: 1908.07490. https://doi.org/10.48550/arXiv.1908.07490
Taylor R, Kardas M, Cucurull G, Scialom T, Hartshorn A, Saravia E, Poulton A, Kerkez V, Stojnic R (2022) Galactica: a large language model for science. arXiv: 2211.09085. https://doi.org/10.48550/arXiv.2211.09085
Thawkar O, Shaker A, Mullappilly SS, Cholakkal H, Anwer RM, Khan S, Laaksonen J, Khan FS (2023) XrayGPT: chest radiographs summarization using large medical vision-language models. arXiv: 2306.07971. https://doi.org/10.48550/arXiv.2306.07971
Thoppilan R, De Freitas D, Hall J, Shazeer N, Kulshreshtha A, Cheng HT, Jin A, Bos T, Baker L, Du Y, Li Y, Lee H, Zheng HS, Ghafouri A, Menegali M, Huang Y, Krikun M, Lepikhin D, Qin J, Chen D, Xu Y, Chen Z, Roberts A, Bosma M, Zhao V, Zhou Y, Chang CC, Krivokon I, Rusch W, Pickett M, Srinivasan P, Man L, Meier-Hellstern K, Morris MR, Doshi T, Delos Santos R, Duke T, Soraker J, Zevenbergen B, Prabhakaran V, Diaz M, Hutchinson B, Olson K, Molina A, Hoffman-John E, Lee J, Aroyo L, Rajakumar R, Butryna A, Lamm M, Kuzmina V, Fenton J, Cohen A, Bernstein R, Kurzweil R, Aguera-Arcas B, Cui C, Croak M, Chi E, Le Q (2022) Lamda: language models for dialog applications. arXiv: 2201.08239. https://doi.org/10.48550/arXiv.2201.08239
Tian Y, Gan R, Song Y, Zhang J, Zhang Y (2023) CHIMED-GPT: a chinese medical large language model with full training regime and better alignment to human preferences. arXiv: 2311.06025. https://doi.org/10.48550/arXiv.2311.06025
Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, and Lample G (2023) Llama: open and efficient foundation language models. arXiv: 2302.13971. https://doi.org/10.48550/arXiv.2302.13971
Tu T, Azizi S, Driess D, Schaekermann M, Amin M, Chang PC, Carroll A, Lau C, Tanno R, Ktena I, Mustafa B, Chowdhery A, Liu Y, Kornblith S, Fleet D, Mansfield P, Prakash S, Wong R, Virmani S, Semturs C, Mahdavi SS, Green B, Dominowska E, Aguera y Arcas B, Barral J, Webster D, Corrado GS, Matias Y, Singhal K, Florence P, Karthikesalingam A, Natarajan V (2023) Towards generalist biomedical AI. arXiv: 2307.14334. https://doi.org/10.48550/arXiv.2307.14334
Wang G, Yang G, Du Z, Fan L, Li X (2023a) ClinicalGPT: large language models finetuned with diverse medical data and comprehensive evaluation. arXiv: 2306.09968. https://doi.org/10.48550/arXiv.2306.09968
Wang Z, Wu Z, Agarwal D, Sun J (2023b) MedCLIP: contrastive learning from unpaired medical images and text. arXiv: 2210.10163. https://doi.org/10.48550/arXiv.2210.10163
Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le Q, and Zhou D (2022) Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 35: 24824−24837
Wu C, Lin W, Zhang X, Zhang Y, Wang Y, Xie W (2023a) PMC-LLaMA: an open-source language model for medical applications. arXiv: 2304.14454. https://doi.org/10.48550/arXiv.2304.14454
Wu S, Fei H, Qu L, Ji W, Chua TS (2023b) NExT-GPT: any-to-any multimodal LLM. arXiv: 2309.05519. https://doi.org/10.48550/arXiv.2309.05519
Wu Y, Wang S, Yang H, Zheng T, Zhang H, Zhao Y, Qin B (2023c) An early evaluation of gpt-4v(ision). arXiv: 2310.16534. https://doi.org/10.48550/arXiv.2310.16534
Xu H, Ghosh G, Huang PY, Arora P, Aminzadeh M, Feichtenhofer C, Metze F, Zettlemoyer L (2021). Vlm: task-agnostic video-language model pre-training for video understanding. arXiv: 2105.09996. https://doi.org/10.48550/arXiv.2105.09996
Xu M (2023) MedicalGPT: training medical GPT models. https://github.com/shibing624/MedicalGPT
Yasunaga M, Bosselut A, Ren H, Zhang X, Manning CD, Liang PS, Leskovec J (2022a) Deep bidirectional language-knowledge graph pretraining. Adv Neural Inf Process Syst 35: 37309−37323
Yasunaga M, Leskovec J, Liang P (2022b) LinkBERT: pretraining language models with document links. arXiv: 2203.15827. https://doi.org/10.48550/arXiv.2203.15827
Ye F, Liu G, Wu X, Wu L (2023) AltDiffusion: a multilingual text-to-image diffusion model. arXiv: 2308.09991. https://doi.org/10.48550/arXiv.2308.09991
Yu Z, Yu J, Cui Y, Tao D, Tian Q (2019) Deep modular co-attention networks for visual question answering. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6281−6290
Zhan LM, Liu B, Fan L, Chen J, Wu XM (2020) Medical visual question answering via conditional reasoning. In Proceedings of the 28th ACM International Conference on Multimedia. pp. 2345−2354
Zhang S, Roller S, Goyal N, Artetxe M, Chen M, Chen S, Dewan C, Diab M, Li X, Lin XV, Mihaylov T, Ott M, Shleifer S, Simig D, Koura PS, Sridhar A, Wang T, Zettlemoyer L (2022) OPT: open pre-trained transformer language models. arXiv: 2205.01068. https://doi.org/10.48550/arXiv.2205.01068
Zhang S, Xu Y, Usuyama N, Bagga J, Tinn R, Preston S, Rao R, Wei M, Valluri N, Wong C, Lungren MP, Naumann T, Poon H (2023) Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv: 2303.00915. https://doi.org/10.48550/arXiv.2303.00915
Zhao H, Cai Z, Si S, Ma X, An K, Chen L, Liu Z, Wang S, Han W, Chang B (2023) MMICL: empowering vision-language model with multi-modal in-context learning. arXiv: 2309.07915. https://doi.org/10.48550/arXiv.2309.07915
Zhu D, Chen J, Shen X, Li X, Elhoseiny M (2023) MiniGPT-4: enhancing vision-language understanding with advanced large language models. arXiv: 2304.10592. https://doi.org/10.48550/arXiv.2304.10592