Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma

Lung cancer exhibits a significantly higher prevalence and fatality rate in comparison to other prevalent malignancies on a global scale. Prompt identification of worrisome lung cyst is crucial in the battle against lung cancer. The increasing accessibility of clinical data, such as low-dose computed tomography (LDCT), histology entire slide images, electronic health records, and sensor signals from medical Internet of Things (IoT) devices, has elevated the significance of artificial intelligence technologies in the management of pulmonary carcinoma. This survey offers a comprehensive examination of the existing and emerging artificial intelligence techniques utilized in the battle against lung malignancies. We explore innovative artificial intelligence techniques for the detection and diagnosis of lung nodules in CT scans, in addition to the commonly used deep learning models. Federated deep learning models are specifically engineered to leverage data from several centers while giving utmost importance to privacy and confidentiality. Multi-modal deep learning models seek to integrate clinical and visual data from diverse sources. Additionally Developers create interpretable deep learning models to offer clinicians a clear understanding of the model's internal mechanisms. Furthermore, it will aid clinicians in improving their comprehension of the developing AI technology.