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In this application, users can upload a chest X-ray image and get back the AI-generated probabilities of various lung pathologies. This app is based on TorchXRayVision, an open-source software library for working with chest X-ray datasets and deep learning models (including pre-trained ones). It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition, a number of classification and representation learning models with different architectures, trained on different data combinations, are available through the library to serve as baselines or feature extractors. In the case of researchers addressing clinical questions it is a waste of time for them to train models from scratch. To address this, TorchXRayVision provides pre-trained models which are trained on large cohorts of data and enables 1) rapid analysis of large datasets 2) feature reuse for few-shot learning. In the case of researchers developing algorithms it is important to robustly evaluate models using multiple external datasets. Metadata associated with each dataset can vary greatly which makes it difficult to apply methods to multiple datasets. TorchXRayVision provides access to many datasets in a uniform way so that they can be swapped out with a single line of code. These datasets can also be merged and filtered to construct specific distributional shifts for studying generalization. Twitter: @torchxrayvision

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Use Cases Limitations Evidence Owner's Insight
  • Algorithm Development and Testing: AI researchers can employ the application to test and refine algorithms, using its outputs as benchmarks against other AI models or human expert analysis. This could be especially valuable in academic settings where access to diverse datasets and pre-trained models accelerates research.

  • Case Study Analysis In Educational Settings: Medical students and educators can utilize the application to analyze chest X-rays in a simulated environment, allowing them to gain insights into AI's potential in identifying various pathologies. This educational tool can enhance learning by providing immediate feedback on the AI's assessment, which students can compare against established clinical diagnoses.

  • Data Analysis in Epidemiological Studies: The application can be used in epidemiological research to analyze population health trends, particularly in studying the prevalence of certain lung pathologies. Researchers can use the AI-generated probabilities to identify patterns or anomalies in large sets of chest X-ray data.

While the Torch XRay Vision application offers insights into potential pathologies presented in Chest X-Ray images, it is crucial to note that the application is labeled as "Not For Medical Use" in its GitHub repository. It is intended primarily for research and educational purposes and must not be used as a substitute for professional medical advice, diagnosis, or treatment. Clinicians and researchers are advised to exercise professional judgment and caution when interpreting the application's output: - While the application provides scores indicating the presence of pathologies, it requires medical expertise to correctly interpret these results. - The performance of the models is dependent on the diversity and quality of the datasets they were trained on and may perform differently for different populations. - The application is designed to augment the diagnostic process, not replace the expert judgment of radiologists. Clinicians should always verify AI-generated probabilities with a thorough clinical assessment.

The TorchXRayVision library is reputable in the health AI community; the following peer-reviewed article detailing it has been cited by 54+ different papers: The library’s source code can be found at; see for detailed documentation.

I’m Dan Caron, the main developer of this application. As the founder of Health Universe, I’m deeply committed to the mission of democratizing health AI and machine learning. Our platform hosts an expansive collection of high-quality models that cater to a diverse audience, ranging from clinicians and patients to frontline researchers.

I created this app with the vision of showcasing the transformative potential of AI in the interpretation of medical images. By providing medical students and practitioners with a hands-on tool that encapsulates the power of AI, we aim to foster an environment where AI-assisted diagnostics is not a distant future but an accessible reality.

Your insights and feedback on this app are greatly appreciated. If you have any thoughts or feedback on this app, feel free to leave a comment below.

Peer reviewed

Warning: This application or model has been peer reviewed, but still may occasionally produce unsafe outputs.

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  • Diagnostics & Imaging


Daniel Caron

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