Exploring AI-Driven Pneumonia Diagnosis Using Chest Radiographs : A Review

Deep learning has made immense strides in medical imaging. The use of CNN’s, Vision Transformers (ViT), and hybrid networks have enabled the detection of pneumonia or lung disease with > 97-99% accuracy. According to this scholarly article published in Nature, using ResNet50, DenseNet121, EfficientNet, and hybrid ViT models can provide a highly reliable diagnosis across many datasets containing chest X-ray images of pneumonia patients (not listing dataset names here). Building upon these milestones, PneumoTrack combines an AI classification engine with real-time automation workflows for providing trustworthy diagnoses. The key areas for future research include increasing dataset diversity, developing extremely small clinical models; building explainability into the machine learning algorithms that are used to provide safety information across all channels related to drug interactions; among other topics to improve transparency, trustworthiness, and uptake of Mid/Late stage technologies from laboratory to clinical settings. Future studies may also focus on increasing model generalization, and having multiple human patient records included in the overall patient profile, and delivering their insights using cloud-based platforms for real-time clinical use. In conclusion, this work illustrates the transformative impact of artificial intelligence on medical imaging and demonstrates it has proven viable for providing automated pneumonia diagnoses.

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