# Leveraging Large-Scale Public Data for Artificial Intelligence-Driven Chest X-Ray Analysis and Diagnosis

## TL;DR

This paper shows how AI models trained on large public chest X-ray datasets can help diagnose thoracic conditions effectively, even with noisy data.

## Contribution

The novel contribution is demonstrating the effectiveness of general-purpose deep learning models trained on diverse, large-scale public datasets for robust CXR diagnosis.

## Key findings

- EfficientNet achieved the highest diagnostic performance with an AUC of 0.8944.
- Larger and more diverse datasets improved model generalizability and diagnostic accuracy.
- Tuberculosis diagnosis remained challenging due to limited high-quality training samples.

## Abstract

Background: Chest X-ray (CXR) imaging is crucial for diagnosing thoracic abnormalities; however, the rising demand burdens radiologists, particularly in resource-limited settings. Method: We used large-scale, diverse public CXR datasets with noisy labels to train general-purpose deep learning models (ResNet, DenseNet, EfficientNet, and DLAD-10) for multi-label classification of thoracic conditions. Uncertainty quantification was incorporated to assess model reliability. Performance was evaluated on both internal and external validation sets, with analyses of data scale, diversity, and fine-tuning effects. Result: EfficientNet achieved the highest overall area under the receiver operating characteristic curve (0.8944) with improved sensitivity and F1-score. Moreover, as training data volume increased—particularly using multi-source datasets—both diagnostic performance and generalizability were enhanced. Although larger datasets reduced predictive uncertainty, conditions such as tuberculosis remained challenging due to limited high-quality samples. Conclusions: General-purpose deep learning models can achieve robust CXR diagnostic performance when trained on large-scale, diverse public datasets despite noisy labels. However, further targeted strategies are needed for underrepresented conditions.