Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.

COVID-19 remains globally a highly infectious disease targeting multiple organs. Rehabilitation is increasingly valued among the supportive care fields to combat COVID-19 as currently definitive curative treatment remains largely absent. This narrative review is to address rehabilitation related topics associated with the treatment of COVID-19 patients. Nosocomial spread remains a high risk for healthcare workers, with comparable high ratios of exposed workers suffering from the disease with more severe clinical course. Primary principle of rehabilitation is to protect rehabilitation physicians and cover all person-to-person interactions. Translating perspectives are encouraged through each multidisciplinary approach. Rehabilitation for the outpatient remains a potential beneficial approach. Artificial intelligence can potentially provide aid and possible answers to important problems that may emerge involving COVID-19. The real value of rehabilitation in COVID-19 may be very impactful and beneficial for patient’s physical and mental health.

Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application.There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies.It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.

Extracellular vesicles (EVs) are a class of spherical vesicles that are produced by active secretion of cells and encapsulated by phospholipid bilayers. In recent years, numerous studies have shown that EVs play pivotal roles in the regulation of intercellular communication between colorectal cancer (CRC) cells and target cells, and can regulate the proliferation, metastasis, and infiltration of tumor cells by regulating the microenvironment of tumor cells. EVs carry specific molecular substances in source CRC cells and are expected to serve as new molecular markers for the detection of cancers. This review highlights the current state of research and progress of potentially incorporating EVs in the diagnosis and treatment of CRC.