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Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model\u2019s performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings.<\/jats:p>","DOI":"10.3390\/jpm14080856","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T03:46:36Z","timestamp":1723607196000},"page":"856","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3503-6942","authenticated-orcid":false,"given":"Md Abu","family":"Sufian","sequence":"first","affiliation":[{"name":"IVR Low-Carbon Research Institute, Chang\u2019an University, Xi\u2019an 710018, China"},{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wahiba","family":"Hamzi","sequence":"additional","affiliation":[{"name":"Laboratoire de Biotechnologie Sant\u00e9 et Environnement, Department of Biology, University of Blida, Blida 09000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2122-3741","authenticated-orcid":false,"given":"Tazkera","family":"Sharifi","sequence":"additional","affiliation":[{"name":"Data Science Architect-Lead Technologist, Booz Allen Hamilton, Texas City, TX 78226, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadia","family":"Zaman","sequence":"additional","affiliation":[{"name":"Department of Physiology, Queen Mary University, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3019-9754","authenticated-orcid":false,"given":"Lujain","family":"Alsadder","sequence":"additional","affiliation":[{"name":"Department of Physiology, Queen Mary University, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Physiology, Queen Mary University, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Hakim","sequence":"additional","affiliation":[{"name":"Department of Physiology, Queen Mary University, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boumediene","family":"Hamzi","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematical Sciences, California Institute of Technology, Caltech, CA 91125, USA"},{"name":"The Alan Turing Institute, London NW1 2DB, UK"},{"name":"Department of Mathematics, Imperial College London, London SW7 2AZ, UK"},{"name":"Department of Mathematics, Gulf University for Science and Technology (GUST), Mubarak Al-Abdullah 32093, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"ref_1","first-page":"e67","article-title":"Artificial intelligence for chest X-ray image analysis: A review","volume":"3","author":"Rajpurkar","year":"2021","journal-title":"Lancet Digital Health"},{"key":"ref_2","unstructured":"World Health Organization (2020). 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