Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

Most colorectal cancers arise from adenomatous polyps and sessile serrated lesions. Screening colonoscopy and therapeutic polypectomy can potentially reduce colorectal cancer burden by early detection and removal of these polyps, thus decreasing colorectal cancer incidence and mortality. Most endoscopists are skilled in detecting and removing the vast majority of polyps endoscopically during a routine colonoscopy. Polyps can be considered “complex” based on size, location, morphology, underlying scar tissue, which are not amenable to removal by conventional endoscopic polypectomy techniques. They are technically more challenging to resect and carry an increased risk of complications. Most of these polyps were used to be managed by surgical intervention in the past. Rapid advancement in endoscopic resection techniques has led to a decreasing role of surgery in managing these complex polyps. These endoscopic resection techniques do require an expert in the field and advanced equipment to perform the procedure. In this review, we discuss various advanced endoscopic techniques for the management of complex polyps.

The first case of coronavirus disease 2019 (COVID-19) was reported in December 2019 in China. World Health Organization declared it a pandemic on March 11, 2020. It has caused significant morbidity and mortality worldwide. Persistent symptoms and serious complications are being reported in patients who survived COVID-19 infection, but long-term sequelae are still unknown. Several vaccines against COVID-19 have been approved for emergency use around the globe. These vaccines have excellent safety profiles with few reported side effects. Drug-induced hepatotoxicity is mainly seen with different drugs or chemicals. There are only a few reported cases of hepatotoxicity with vaccines. We present a case of liver injury after administration of the vaccine against the COVID-19 infection.

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal diseases encountered in primary care and gastroenterology clinics. Most cases of GERD can be diagnosed based on clinical presentation and risk factors; however, some patients present with atypical symptoms, which can make diagnosis difficult. An esophagogastroduodenoscopy can be used to assist in diagnosis of GERD, though only half of these patients have visible endoscopic findings on standard white light endoscopy. This led to the development of new advanced endoscopic techniques that enhanced the diagnosis of GERD and related complications like squamous cell dysplasia, Barrett's esophagus, and early esophageal adenocarcinoma. This is conducted by improved detection of subtle irregularities in the mucosa and vascular structures through optical biopsies in real-time. Management of GERD includes lifestyle modifications, pharmacological therapy, endoscopic and surgical intervention. Minimally invasive endoscopic intervention can be an option in selected patients with small hiatal hernia and without complications of GERD. These endoscopic interventions include endoscopic fundoplication, endoscopic mucosal resection techniques, ablative techniques, creating mechanical barriers, and suturing and stapling devices. As these new advanced endoscopic techniques are emerging, data surrounding the indications, advantages and disadvantages of these techniques need a thorough understanding.