Artificial intelligence for colorectal polyp detection: are we ready for prime time?

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Colonoscopy is protective against CRC through the detection and removal of neoplastic polyps. Unfortunately, the procedure is highly operator dependent with significant miss rates for polyps. Artificial intelligence (AI) and computer-aided detection software offers a promising solution by providing real-time assistance to highlight lesions that may otherwise be overlooked. Rapid advances have occurred in the field with recent prospective clinical trials demonstrating an improved adenoma detection rate (ADR) with AI assistance. Deployment in routine clinical practice is possible in the near future although further robust clinical trials are necessary and important practical challenges relating to real-world implementation must be addressed

Barriers and Pitfalls for Artificial Intelligence in Gastroenterology: Ethical and Regulatory issues

Artificial intelligence (AI)-based technologies are developing rapidly, offering great promise for gastroenterology and particularly endoscopy. However, there are complex barriers and pitfalls that must be considered before widespread real-world clinical implementation can occur. This review highlights major ethical concerns related to data privacy and sharing that are essential for the development of AI models, through to practical clinical issues such as potential patient harm, accountability, bias in decisions, and impact on workforce. Finally, current regulatory pathways are discussed, recognizing that these need to evolve to deal with unique new challenges, such as the adaptive and rapidly iterative nature of AI-based technologies, while striking a balance between ensuring patient safety and promoting innovation

Artificial intelligence in biliopancreatic endoscopy: Is there any role?

Artificial intelligence (AI) research in endoscopy is being translated at rapid pace with a number of approved devices now available for use in luminal endoscopy. However, the published literature for AI in biliopancreatic endoscopy is predominantly limited to early pre-clinical studies including applications for diagnostic EUS and patient risk stratification. Potential future use cases are highlighted in this manuscript including optical characterisation of strictures during cholangioscopy, prediction of post-ERCP acute pancreatitis and selective biliary duct cannulation difficulty, automated report generation and novel AI-based quality key performance metrics. To realise the full potential of AI and accelerate innovation, it is crucial that robust inter-disciplinary collaborations are formed between biliopancreatic endoscopists and AI researchers

The contribution of Chinese exports to climate change

Within 5 years, China's CO2 emissions have nearly doubled, and China may already be the world's largest emitter of CO2. Evidence suggests that exports could be a main cause for the rise in Chinese CO2 emissions; however, no systematic study has analyzed this issue, especially over time. We find that in 2005, around one-third of Chinese emissions (1700 Mt CO2) were due to production of exports, and this proportion has risen from 12% (230 Mt) in 1987 and only 21% (760 Mt) as recently as 2002. It is likely that consumption in the developed world is driving this trend. A majority of these emissions have largely escaped the scrutiny of arguments over “carbon leakage” due to the current, narrow definition of leakage. Climate policies which would make the developed world responsible for China's export emissions have both benefits and costs, and must be carefully designed to achieve political consensus and equity. Whoever is responsible for these emissions, China's rapidly expanding infrastructure and inefficient coal-powered electricity system need urgent attention

Designing Visual Markers for Continuous Artificial Intelligence Support

Colonoscopy, the visual inspection of the large bowel using an endoscope, offers protection against colorectal cancer by allowing for the detection and removal of pre-cancerous polyps. The literature on polyp detection shows widely varying miss rates among clinicians, with averages ranging around 22%--27%. While recent work has considered the use of AI support systems for polyp detection, how to visualise and integrate these systems into clinical practice is an open question. In this work, we explore the design of visual markers as used in an AI support system for colonoscopy. Supported by the gastroenterologists in our team, we designed seven unique visual markers and rendered them on real-life patient video footage. Through an online survey targeting relevant clinical staff (N = 36), we evaluated these designs and obtained initial insights and understanding into the way in which clinical staff envision AI to integrate in their daily work-environment. Our results provide concrete recommendations for the future deployment of AI support systems in continuous, adaptive scenarios

Real Time Monitoring for the Most Vulnerable: Pre-Primary Education in Bangladesh

One of the most unique examples of real?time monitoring supported by UNICEF is found in Bangladesh in the pre?primary education (PPE) programme operated by BRAC. Randomisation techniques are used for school selection by monitors as well as for intra?classroom sampling to test learning outcomes. Monitoring is a multi?level decentralised learning process that allows staff members to compare actual performance, outputs and results against standards. Monitoring duties are executed by the programme staff themselves as well as by the organisation. The intent is to promote internal programme learning, not just logical framework type reporting, and builds on the recognition that monitoring is only effective if it enables responses to programme implementation. The BRAC initiative demonstrates that monitoring with a real?time component can be central to a strategy emphasising learning outcomes. It also shows that ICTs are not a necessary ingredient of ‘real?time’ monitoring despite the current fashion in thinking

Adult and paediatric mortality patterns in a referral hospital in Liberia 1 year after the end of the war

The aim of this study was to describe and analyse hospital mortality patterns after the Liberian war. Data were collected retrospectively from January to July 2005 in a referral hospital in Monrovia, Liberia. The overall fatality rate was 17.2% (438/2543) of medical admissions. One-third of deaths occurred in the first 24h. The adult fatality rate was 23.3% (241/1034). Non-infectious diseases accounted for 56% of the adult deaths. The main causes of death were meningitis (16%), stroke (14%) and heart failure (10%). Associated fatality rates were 48%, 54% and 31% respectively. The paediatric fatality rate was 13.1% (197/1509). Infectious diseases caused 66% of paediatric deaths. In infants <1 month old, the fatality rate was 18% and main causes of death were neonatal sepsis (47%), respiratory distress (24%) and prematurity (18%). The main causes of death in infants > or =1 month old were respiratory infections (27%), malaria (23%) and severe malnutrition (16%). Associated fatality rates were 12%, 10% and 19%. Fatality rates were similar to those found in other sub-Saharan countries without a previous conflict. Early deaths could decrease through recognition and early referral of severe cases from health centres to the hospital and through assessment and priority treatment of these patients at arrival