Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Cancers

Upper gastrointestinal (UGI) tract pathology is common worldwide. With recent advancements in robotics, innovative diagnostic and treatment devices have been developed and several translational attempts made. This review paper aims to provide a highly pictorial critical review of robotic gastroscopes, so that clinicians and researchers can obtain a swift and comprehensive overview of key technologies and challenges. Therefore, the paper presents robotic gastroscopes, either commercial or at a progressed technology readiness level. Among them, we show tethered and wireless gastroscopes, as well as devices aimed for UGI surgery. The technological features of these instruments, as well as their clinical adoption and performance, are described and compared. Although the existing endoscopic devices have thus far provided substantial improvements in the effectiveness of diagnosis and treatment, there are certain aspects that represent unwavering predicaments of the current gastroenterology practice. A detailed list includes difficulties and risks, such as transmission of communicable diseases (e.g., COVID-19) due to the doctor-patient proximity, unchanged learning curves, variable detection rates, procedure-related adverse events, endoscopists' and nurses' burnouts, limited human and/or material resources, and patients' preferences to choose non-invasive options that further interfere with the successful implementation and adoption of routine screening. The combination of robotics and artificial intelligence, as well as remote telehealth endoscopy services, are also discussed, as viable solutions to improve existing platforms for diagnosis and treatment are emerging.

Marlicz Wojciech, Ren Xuyang, Robertson Alexander, Skonieczna-Żydecka Karolina, Łoniewski Igor, Dario Paolo, Wang Shuxin, Plevris John N, Koulaouzidis Anastasios, Ciuti Gastone


artificial intelligence, gastric cancer, gastroscopy, machine learning, robotic gastroscopy