ArticleNature machine intelligence2023
Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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The trial behind it
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Who cites it
44 citing papers in PubMed.
- OrgLine: A versatile pipeline for organoid morphometry using detector-guided prompts.Cell reports methods · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.ArXiv · 2026Article
- Cooperative integrated surgical robots for high-performance targeted therapies.Innovation (Cambridge (Mass.)) · 2026Review
- Investigating robot control policy learning for autonomous x-ray-guided spine procedures.International journal of computer assisted radiology and surgery · 2026Article
- Physics-Based Growth and Remodeling Modeling for Virtual Abdominal Aortic Aneurysm Evolution and Growth Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Privacy-Preserving Latent Diffusion-Based Synthetic Medical Image Generation.IEEE transactions on medical imaging · 2026Article
- A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Automated surgical instrument recognition in laparoscopic cholecystectomy videos: a novel two-step deep learning approach with virtual image synthesis.Surgical endoscopy · 2026Article
- RINet: synthetic data training for indirect estimation of clinical reference distributions.Journal of biomedical informatics · 2026Article
- Effects of axial malrotation on posterior tibial slope measurement: a digitally reconstructed radiograph study enabling automated quality assessment.Knee surgery & related research · 2026Article
- Synthetic X‑ray‑driven tracking and control of miniature medical devices.Nature machine intelligence · 2026Article
- 3D path planning for robot-assisted vertebroplasty from arbitrary Bi-plane X-ray via differentiable rendering.Frontiers in robotics and AI · 2026Article
- FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Scaling deep learning for material imaging with a pseudo 3D model for domain transfer.Nature communications · 2025Article
- Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition.Nature communications · 2025Article
- Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.Medical education online · 2025Article
- Scattering-free lanthanide-ionic liquid scintillators for high-resolution and adaptive X-ray imaging.Nature communications · 2025Article
- Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.Abdominal radiology (New York) · 2025Review
- An approach for cancer outcomes modelling using a comprehensive synthetic dataset.Physical and engineering sciences in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.