Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

Gastric cancer (GC) is the second most common cause of cancer-related death worldwide and a serious public health concern. This high death rate is mostly caused by late-stage diagnoses, which lead to poor treatment outcomes. Radiation immunotherapy and targeted therapies are becoming increasingly popular in GC treatment, in addition to surgery and systemic chemotherapy. In this review, we have focused on both in vitro and in vivo research, which presents a summary of recent developments in targeted therapies for gastric cancer. We explore targeted therapy approaches, including integrin receptors, HER2, Claudin 18, and glutathione-responsive systems. For instance, therapies targeting the integrin receptors such as the αvβ3 and αvβ5 integrins have shown promise in enhancing diagnostic precision and treatment efficacy. Furthermore, nanotechnology provides novel approaches to targeted drug delivery and imaging. These include glutathione-responsive nanoplatforms and cyclic RGD peptide-conjugated nanoparticles. These novel strategies seek to reduce systemic toxicity while increasing specificity and efficacy. To sum up, the review addresses the significance of personalized medicine and advancements in gastric cancer-targeted therapies. It explores potential methods for enhancing gastric cancer prognosis and treatment in the future.