While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not just algorithmic accuracy. This paper, based on an Australian field study (observations and gastroenterologist interviews), highlights a critical disconnect: current development prioritizes machine learning model performance, overlooking essential aspects of user interface design, workflow integration, and overall user experience. Industry interactions reveal a similar emphasis on data and algorithms. To realize AI's full potential, the HCI community must champion user-centered design, ensuring these systems are usable, support endoscopist expertise, and enhance patient outcomes.

Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation. Our dataset can be downloaded from \url{ https://doi.org/10.7303/syn26376615}.

Laird Harrison writes about science, health and culture. His work has appeared in national magazines, in newspapers, on public radio and on websites. He is at work on a novel about alternate realities in physics. Harrison teaches writing at the Writers Grotto.

One of the NHS's leading hospital trusts has begun using artificial intelligence to help detect cancer in the gullet, which kills 8,000 Britons a year. It is hoped the technology will increase the number of cases of cancer in the oesophagus that doctors spot. Oesophageal cancer is one of the deadliest forms of cancer. It is hard to detect, particularly in its early stages, and many people who get it die soon after their diagnosis. Fewer than one in five of those diagnosed are still alive five years later.

An artificial intelligence tool being developed by Duke scientists can be added to the standard toilet to help analyze patients' stool and give gastroenterologists the information they need to provide appropriate treatment for chronic issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). The work is being done by Duke University's Center for Water, Sanitation, Hygiene and Infectious Disease (WaSH-AID), and was presented Saturday at the virtual conference Digestive Disease Week 2021. "Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, associate professor of medicine at Duke University and one of the lead authors on the study. "Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process," Fisher said. "The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."

A research has found that an artificial intelligence tool under development at Duke University can be added to the standard toilet to help analyse patients' stool and give gastroenterologists the information they need to provide appropriate treatment. The research was selected for presentation at Digestive Disease Week (DDW) 2021. The new technology could assist in managing chronic gastrointestinal issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). "Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, one of the lead authors on the study and associate professor of medicine at Duke University Durham, North Carolina. "Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process. The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."

Artificial intelligence tool can be used for long-term tracking and management of chronic gastrointestinal ailments. An artificial intelligence tool under development at Duke University can be added to the standard toilet to help analyze patients' stool and give gastroenterologists the information they need to provide appropriate treatment, according to research that was selected for presentation at Digestive Disease Week (DDW) 2021. The new technology could assist in managing chronic gastrointestinal issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). "Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, one of the lead authors on the study and associate professor of medicine at Duke University Durham, North Carolina. "Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process. The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."

Michael Wallace has performed hundreds of colonoscopies in his 20 years as a gastroenterologist. He thinks he's pretty good at recognizing the growths, or polyps, that can spring up along the ridges of the colon and potentially turn into cancer. Sometimes the polyps are flat and hard to see. Other times, doctors just miss them. "We're all humans," says Wallace, who works at the Mayo Clinic.

Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are known tools for diagnosing gastrointestinal (GI) tract disorders. Localizing frames provide valuable information about the anomaly location and also can help clinicians determine a more appropriate treatment plan. There are many automated algorithms to detect the anomaly. However, very few of the existing works address the issue of localization. In this study, we present a combination of meta-learning and deep learning for localizing both endoscopy images and video. A dataset is collected from 10 different anatomical positions of human GI tract. In the meta-learning section, the system was trained using 78 CE and 27 WCE annotated frames with a modified Siamese Neural Network (SNN) to predict the location of one single image/frame. Then, a postprocessing section using bidirectional long short-term memory is proposed for localizing a sequence of frames. Here, we have employed feature vector, distance and predicted location obtained from a trained SNN. The postprocessing section is trained and tested on 1,028 and 365 seconds of CE and WCE videos using hold-out validation (50%), and achieved F1-score of 86.3% and 83.0%, respectively. In addition, we performed subjective evaluation using nine gastroenterologists. The results show that the computer-aided methods can outperform gastroenterologists assessment of localization. The proposed method is compared with various approaches, such as support vector machine with hand-crafted features, convolutional neural network and the transfer learning-based methods, and showed better results. Therefore, it can be used in frame localization, which can help in video summarization and anomaly detection.