Site icon Medical Qest

Learning neuroimaging models from health system-scale data

Learning neuroimaging models from health system-scale data
  • Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature 619, 357–362 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.), Vol. 139 of Proceedings of Machine Learning Research 8748–8763 (PMLR, 2021).

  • Ramesh, A. et al. Zero-shot text-to-image generation. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.), Vol. 139 of Proceedings of Machine Learning Research 8821–8831 (PMLR, 2021).

  • Alayrac, J.-B. et al. Flamingo: a visual language model for few-shot learning. In Advances in Neural Information Processing Systems, Vol. 35 (eds Koyejo, S. et al.) 23716–23736 (Curran Associates, 2022).

  • Dreisbach, J. N. & Lukin, R. Where have all the neuroradiologists gone? AJNR Am. J. Neuroradiol. 22, 1636–1638 (2001).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rula, E. Y. Radiology workforce shortage and growing demand: something has to give. (2024).

  • Christensen, E. W. et al. Association of state share of nonphysician practitioners with diagnostic imaging ordering among emergency department visits for medicare beneficiaries. JAMA Netw. Open 5, e2241297 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fawzy, N. A. et al. Incidence and factors associated with burnout in radiologists: a systematic review. Eur. J. Radiol. Open 11, 100530 (2023).

    Article 

    Google Scholar 

  • Krupinski, E. A., Berbaum, K. S., Caldwell, R. T., Schartz, K. M. & Kim, J. Long radiology workdays reduce detection and accommodation accuracy. J. Am. Coll. Radiol. 7, 698–704 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ivanovic, V. et al. Neuroradiology diagnostic errors at a tertiary academic centre: effect of participation in tumour boards and physician experience. Clin. Radiol. 77, 607–612 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ivanovic, V. et al. Factors associated with neuroradiology diagnostic errors at a large tertiary-care academic medical center: a case-control study. Am. J. Roentgenol. 221, 355–362 (2023).

    Article 

    Google Scholar 

  • O’Neill, T. J. et al. Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage. Radiol. Artif. Intell. 3, e200024 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Shin, H. J., Han, K., Ryu, L. & Kim, E.-K. The impact of artificial intelligence on the reading times of radiologists for chest radiographs. npj Digit. Med. 6, 82 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Alexander, R. et al. Mandating limits on workload, duty, and speed in radiology. Radiology 304, 274–282 (2022).

    Article 
    PubMed 

    Google Scholar 

  • DeBenedectis, C. M. et al. Health care disparities in radiology—a review of the current literature. J. Am. Coll. Radiol. 19, 101–111 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Gauriau, R. et al. A deep learning-based model for detecting abnormalities on brain MR images for triaging: preliminary results from a multisite experience. Radiol. Artif. Intell. 3, e200184 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Barbano, C. A., Brunello, M., Dufumier, B. & Grangetto, M. Anatomical foundation models for brain MRIs. Pattern Recognition Letters 199, 178–184 (2026).

    Article 

    Google Scholar 

  • OpenAI. GPT-4 technical report. Preprint at (2023).

  • Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10684–10695 (2022).

  • Dosovitskiy, A. et al. An image is worth 16 × 16 words: transformers for image recognition at scale. In 9th International Conference on Learning Representations (OpenReview.net, 2021).

  • Darcet, T., Oquab, M., Mairal, J. & Bojanowski, P. Vision transformers need registers. In The Twelfth International Conference on Learning Representations (eds Kim, B. et al.) 2632–2652 (2024).

  • Zhang, K. et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181, 1423–1433.e11 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tiu, E. et al. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat. Biomed. Eng. 6, 1399–1406 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bannur, S. et al. Learning to exploit temporal structure for biomedical vision-language processing. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 15016–15027 (2023).

  • Wang, Y. et al. Enhancing vision-language models for medical imaging: bridging the 3D gap with innovative slice selection. Neural Inf. Process. Syst. 37, 99947–99964 (2024).

    Google Scholar 

  • Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850–862 (2024).

  • Radford, A. et al. Language models are unsupervised multitask learners. OpenAI blog 1, 9 (2019).

  • Liu, H., Li, C., Wu, Q. & Lee, Y. J. Visual instruction tuning. In Proc. 37th International Conference on Neural Information Processing Systems 34892–34916 (2023).

  • Eslami, S., Meinel, C. & De Melo, G. PubMedCLIP: how much does CLIP benefit visual question answering in the medical domain? In Findings of the Association for Computational Linguistics: EACL 2023 (eds Vlachos, A. & Augenstein, I.) 1151–1163 (ACL, 2023).

  • Zhang, S. et al. BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. Preprint at (2023).

  • Moor, M. et al. Med-Flamingo: a multimodal medical few-shot learner. In Proc. 3rd Machine Learning for Health Symposium (eds Hegselmann, S. et al.) 353–367 (PMLR, 2023).

  • Kaplan, J. et al. Scaling laws for neural language models. Preprint at (2020).

  • Guo, C., Pleiss, G., Sun, Y. & Weinberger, K. Q. On calibration of modern neural networks. In Proc. 34th International Conference on Machine Learning 1321– 1330 (PMLR, 2017).

  • Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014).

    Article 
    PubMed 

    Google Scholar 

  • Petersen, R. C. et al. Alzheimer’s disease neuroimaging initiative (ADNI) clinical characterization. Neurology 74, 201–209 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Marcus, D. S. et al. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19, 1498–1507 (2007).

    Article 
    PubMed 

    Google Scholar 

  • Lee, J. et al. Deep learning-based brain age prediction in normal aging and dementia. Nat. Aging 2, 412–424 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bashyam, V. M. et al. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain 143, 2312–2324 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Baid, U. et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. Preprint at (2021).

  • Rudie, J. D. et al. The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI dataset. Radiol. Artif. Intell. 6, e230126 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Oermann, E. et al. Longitudinal deep neural networks for assessing metastatic brain cancer on a massive open benchmark. Nat. Commun. 15, 8170 (2024).

  • Liu, C.-F. et al. A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke. Sci. Data 10, 548 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337–1340 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ribeiro, M. T., Singh, S. & Guestrin, C. ‘Why should I trust you?’ Explaining the predictions of any classifier. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Krishnapuram, B. et al.) 1135–1144 (2016).

  • Smith, J. S. et al. Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. J. Clin. Oncol. 26, 1338–1345 (2008).

    Article 
    PubMed 

    Google Scholar 

  • Waite, S., Scott, J. & Colombo, D. Narrowing the gap: imaging disparities in radiology. Radiology 299, 27–35 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Barocas, S., Hardt, M. & Narayanan, A. Fairness and Machine Learning: Limitations and Opportunities (MIT Press, 2023).

  • Rajpurkar, P. & Topol, E. J. A clinical certification pathway for generalist medical AI systems. Lancet 405, 20 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Ivanovic, V. et al. Impact of shift volume on neuroradiology diagnostic errors at a large tertiary academic center. Acad. Radiol. 30, 1584–1588 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Babiarz, L. S. & Yousem, D. M. Quality control in neuroradiology: discrepancies in image interpretation among academic neuroradiologists. AJNR Am. J. Neuroradiol. 33, 37–42 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wu, M. Z., McInnes, M. D. F., Macdonald, D. B., Kielar, A. Z. & Duigenan, S. CT in adults: systematic review and meta-analysis of interpretation discrepancy rates. Radiology 270, 717–735 (2014).

    Article 
    PubMed 

    Google Scholar 

  • Azizi, S. et al. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nat. Biomed. Eng. 7, 756–779 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Moor, M. et al. Med-Flamingo: a multimodal medical few-shot learner. In Machine Learning for Health (ML4H) 353–367 (PMLR, 2023).

  • Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Blankemeier, L. et al. Merlin: a vision language foundation model for 3D computed tomography. Preprint at (2024).

  • Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kickingereder, P. et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 20, 728–740 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Wood, D. A. et al. A self-supervised text-vision framework for automated brain abnormality detection. Preprint at (2024).

  • Ghosh, S., Poynton, C. B., Visweswaran, S. & Batmanghelich, K. Mammo-CLIP: a vision language foundation model to enhance data efficiency and robustness in mammography. In Proc. International Conference on Medical Image Computing and Computer-assisted Intervention 632–642 (Springer, 2024).

  • van den Oord, A., Vinyals, O. & Kavukcuoglu, K. Neural discrete representation learning. In Advances in Neural Information Processing Systems, Vol. 30 (eds Guyon, I. et al.) (2017).

  • Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image generation with CLIP latents. Preprint at (2022).

  • Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).

    Google Scholar 

  • Chien, A. et al. AI-assisted summarization of radiologic reports: evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical. AJNR Am. J. Neuroradiol. 45, 244–248 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ranjit, M., Ganapathy, G., Manuel, R. & Ganu, T. Retrieval augmented chest X-ray report generation using OpenAI GPT models. In Proc. Machine Learning for Healthcare Conference (eds Deshpande, K. et al.) 650–666 (PMLR, 2023).

  • Adams, L. C. et al. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 307, e230725 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems (eds Guyon, I. et al.) Vol. 30 (Curran Associates, Inc., 2017).

  • Monti, S., Tamayo, P., Mesirov, J. & Golub, T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003).

    Article 

    Google Scholar 

  • Kondepudi, A. et al. Foundation models for fast, label-free detection of glioma infiltration. Nature 637, 439–445 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Cheng, J., Wang, Z. & Pollastri, G. A neural network approach to ordinal regression. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) 1279–1284 (2008).

  • Saratxaga, C. L. et al. MRI deep learning-based solution for Alzheimer’s disease prediction. J. Pers. Med. 11, 902 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, J., Li, D., Savarese, S. & Hoi, S. BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In Proc. International Conference on Machine Learning 19730–19742 (PMLR, 2023).

  • Chen, Q. & Hong, Y. MedBLIP: bootstrapping language-image pre-training from 3D medical images and texts. In Proc. Asian Conference on Computer Vision (eds Cho, M. et al.) 2404–2420 (2024).

  • Liu, H., Li, C., Li, Y. & Lee, Y. J. Improved baselines with visual instruction tuning. In Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition 26296–26306 (2024).

  • Li, C. et al. LLaVA-Med: training a large language-and-vision assistant for biomedicine in one day. In Advances in Neural Information Processing Systems, Vol. 36 (eds Oh, A. et al.) 28541–28564 (Curran Associates, Inc., 2023).

  • Zhu, C., Wang, T., Zhang, W., Pang, J. & Liu, X. LLaVA-3D: a simple yet effective pathway to empowering LMMs with 3D-awareness. In Proc. IEEE/CVF International Conference on Computer Vision 4295–4305 (2025).

  • Hardt, M., Price, E. & Srebro, N. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, Vol. 29 (eds Lee, D. et al.) (2016).

  • Vaidya, A. et al. Demographic bias in misdiagnosis by computational pathology models. Nat. Med. 30, 1174–1190 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • link

    Exit mobile version