Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017).

Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) 4171–4186 (2019).

Oh, K. et al. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images. Scientific reports 11, 1897 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mohsin, S. N. et al. The role of artificial intelligence in prediction, risk stratification, and personalized treatment planning for congenital heart diseases. Cureus 15 (2023).

Nivedhaa, N. A comprehensive study of artificial intelligence’s contribution to streamlining healthcare workflows and enhancing decision-making practices. International Journal of Information Technology and Electrical Engineering (IJITEE) 13, 1–7 (2024).

Google Scholar 

Qureshi, R. et al. Ai in drug discovery and its clinical relevance. Heliyon 9 (2023).

Chen, X. et al. Recent advances and clinical applications of deep learning in medical image analysis. Medical image analysis 79, 102444 (2022).

Sandino, C. M. et al. Upstream machine learning in radiology. Radiologic Clinics 59, 967–985 (2021).

PubMed  PubMed Central  Google Scholar 

Boland, G. W., Duszak Jr, R. & Kalra, M. Protocol design and optimization. Journal of the American College of Radiology 11, 440–441 (2014).

Article  PubMed  Google Scholar 

Blackmore, C. C., Mecklenburg, R. S. & Kaplan, G. S. Effectiveness of clinical decision support in controlling inappropriate imaging. Journal of the American College of Radiology 8, 19–25 (2011).

Article  PubMed  Google Scholar 

Schemmel, A. et al. Radiology workflow disruptors: a detailed analysis. Journal of the American College of Radiology 13, 1210–1214 (2016).

Article  PubMed  Google Scholar 

Flaherty, S., Zepeda, E. D., Mortele, K. & Young, G. J. Magnitude and financial implications of inappropriate diagnostic imaging for three common clinical conditions. International Journal for Quality in Health Care 31, 691–697 (2019).

PubMed  Google Scholar 

Brown, A. D. & Marotta, T. R. Using machine learning for sequence-level automated mri protocol selection in neuroradiology. Journal of the American Medical Informatics Association 25, 568–571 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Trivedi, H., Mesterhazy, J., Laguna, B., Vu, T. & Sohn, J. H. Automatic determination of the need for intravenous contrast in musculoskeletal mri examinations using ibm watson’s natural language processing algorithm. Journal of digital imaging 31, 245–251 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Ferrucci, D. A. Introduction to “this is watson”. IBM Journal of Research and Development 56, 1–1 (2012).

Ferrucci, D., Levas, A., Bagchi, S., Gondek, D. & Mueller, E. T. Watson: beyond jeopardy! Artificial Intelligence 199, 93–105 (2013).

Article  Google Scholar 

Lee, Y. H. Efficiency improvement in a busy radiology practice: determination of musculoskeletal magnetic resonance imaging protocol using deep-learning convolutional neural networks. Journal of digital imaging 31, 604–610 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Kalra, A., Chakraborty, A., Fine, B. & Reicher, J. Machine learning for automation of radiology protocols for quality and efficiency improvement. Journal of the American College of Radiology 17, 1149–1158 (2020).

Article  PubMed  Google Scholar 

Xavier, B. A. & Chen, P.-H. Natural language processing for imaging protocol assignment: machine learning for multiclass classification of abdominal ct protocols using indication text data. Journal of Digital Imaging 35, 1120–1130 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Howard, J. & Ruder, S. Universal language model fine-tuning for text classification. arXiv:1801.06146 (2018).

Lau, W., Aaltonen, L., Gunn, M. & Yetisgen, M. Automatic assignment of radiology examination protocols using pre-trained language models with knowledge distillation. AMIA Annual Symposium Proceedings 2021, 668 (2022).

PubMed  PubMed Central  Google Scholar 

Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al. Improving language understanding by generative pre-training (2018).

Achiam, J. et al. Gpt-4 technical report. arXiv:2303.08774 (2023).

Gertz, R. J. et al. Gpt-4 for automated determination of radiologic study and protocol based on radiology request forms: a feasibility study. Radiology 307, e230877 (2023).

Grattafiori, A. et al. The llama 3 herd of models. arXiv:2407.21783 (2024).

Lewis, P. et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems 33, 9459–9474 (2020).

Google Scholar 

von Werra, L. et al. Trl: Transformer reinforcement learning. https://github.com/huggingface/trl (2020).

Wolf, T. et al. Transformers: State-of-the-art natural language processing. Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations 38–45 (2020).

Johnson, J., Douze, M. & Jégou, H. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data 7, 535–547 (2019).

Article  Google Scholar 

Song, K., Tan, X., Qin, T., Lu, J. & Liu, T.-Y. Mpnet: Masked and permuted pre-training for language understanding. Advances in neural information processing systems 33, 16857–16867 (2020).

Google Scholar