ArticleNature communications2022
Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Survey-based insights on overcoming barriers in healthcare innovation ecosystems.Nature biotechnology · 2026Article
- AI-Assisted segmentation and volumetric reconstruction of radiographs through multi-angular scintillation imaging.Nature communications · 2026Article
- Defining operational safety in clinical artificial intelligence systems.NPJ digital medicine · 2026Article
- Large language models as cost-conscious decision aids in emergency medicine: protocol support for imaging in lower back pain.Emergency radiology · 2026Article
- Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation.Findings of ACL. NAACL · 2025Article
- Comparative Analysis of M4CXR, an LLM-Based Chest X-Ray Report Generation Model, and ChatGPT in Radiological Interpretation.Journal of clinical medicine · 2024Article
- [Explainable & Safe Artificial Intelligence in Radiology].Journal of the Korean Society of Radiology · 2024Review
- Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.Investigative ophthalmology & visual science · 2024Article
- Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.BMC medical imaging · 2024Article
- Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT.Diagnostics (Basel, Switzerland) · 2023Article
- Severity of error in hierarchical datasets.Scientific reports · 2023Article
- A systematic review and meta-analysis on ChatGPT and its utilization in medical and dental research.Heliyon · 2023Article
- A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry.OTA international : the open access journal of orthopaedic trauma · 2023Article
- Evaluating GPT as an Adjunct for Radiologic Decision Making: GPT-4 Versus GPT-3.5 in a Breast Imaging Pilot.Journal of the American College of Radiology : JACR · 2023Article
- Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study.Journal of medical Internet research · 2023Article
- Tackling prediction uncertainty in machine learning for healthcare.Nature biomedical engineering · 2023Review
- There is no such thing as a validated prediction model.BMC medicine · 2023Article
- Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.Scientific reports · 2022Article
- A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such public databases; one approach, for example, focused on labor-intensive, manual labeling of subsets of these datasets to be used to train new models. In this study, we describe a method for standardized, automated labeling based on similarity to a previously validated, explainable AI (xAI) model-derived-atlas, for which the user can specify a quantitative threshold for a desired level of accuracy (the probability-of-similarity, pSim metric). We show that our xAI model, by calculating the pSim values for each clinical output label based on comparison to its training-set derived reference atlas, can automatically label the external datasets to a user-selected, high level of accuracy, equaling or exceeding that of human experts. We additionally show that, by fine-tuning the original model using the automatically labelled exams for retraining, performance can be preserved or improved, resulting in a highly accurate, more generalized model.
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