Opportunities and challenges in the application of large artificial intelligence models in radiology
Not on the map. Why?
Abstract (arXiv)
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 86 of its 150 references that are in the map. First posted on arXiv 24 Mar 2024.
Builds on
86 of 150 references.
References · 86
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2023Segment Anythingcited by 8,158
- 2020Scaling Laws for Neural Language Modelscited by 6,411
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2022Scalable Diffusion Models with Transformerscited by 5,931
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
38 more
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
- 2023A Survey of Large Language Modelscited by 2,537
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
- 2022Video Diffusion Modelscited by 2,280
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
- 2022Large Language Models Encode Clinical Knowledgecited by 1,807
- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
- 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
- 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,287
- 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
- 2022Diffusion Models in Vision: A Surveycited by 1,008
- 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
- 2023Large Language Modelscited by 774
- 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
- 2020Beyond English-Centric Multilingual Machine Translationcited by 559
- 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 557
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
- 2024Lumiere: A Space-Time Diffusion Model for Video Generationcited by 450
- 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
- 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
- 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
- 2019CamemBERT: a Tasty French Language Modelcited by 368
- 2019Multi-Task Deep Neural Networks for Natural Language Understandingcited by 349
- 2019MASS: Masked Sequence to Sequence Pre-training for Language Generationcited by 345
- 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 277
- 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 258
- 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 254
- 2020UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learningcited by 239
- 2019ERNIE 2.0: A Continual Pre-training Framework for Language Understandingcited by 230
- 2019Understanding and Improving Layer Normalizationcited by 213
- 2018Marian: Fast Neural Machine Translation in C++cited by 202
- 2015The Power of Depth for Feedforward Neural Networkscited by 181
- 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
- 2022Multiview Transformers for Video Recognitioncited by 148
- 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
- 2021Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentationcited by 133
50 most cited of 86 All 86 (JSON)
Built on by
Citing papers per year: 2024: 1.
1 paper.