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Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

BackgroundLateral flow tests (LFT) are point-of-care rapid antigen tests that allow isolation and control of disease outbreaks through convenient, practical testing. However, studies have shown significant variation in their diagnostic accuracy. We conducted a systematic review of the diagnostic accuracy of LFTs for the detection of severe acute respiratory syndromecoronavirus 2 (SARS-CoV-2) to identify potential factors affecting their performance. MethodsA systematic search of online databases was carried out to identify studies assessing the sensitivity and specificity of LFTs compared with polymerase chain reaction (PCR) tests. Data were extracted and used to calculate pooled sensitivity and specificity. Meta-regression analysis was conducted to identify covariates influencing diagnostic accuracy. ResultsIn total, 76 articles with 108,820 test results were identified for analysis. Pooled sensitivity and specificity were 72% (95% confidence interval (CI): 0.68-0.76) and 100% (95% CI: 0.99-1.00), respectively. Staff operation of the LFT showed a statistically significant increase in sensitivity (p=0.04) and specificity (p=0.001) compared with self-operation by the test subjects. The use of LFTs in symptomatic patient subgroups also resulted in higher test sensitivity. ConclusionLFTs display good sensitivity and extremely good specificity for SARS-CoV-2 antigen detection; they become more sensitive in patients with symptoms and when performed by trained professionals.

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