Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

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

Advancements in eHealth Data Analytics through Natural Language Processing and Deep Learning

The healthcare environment is commonly referred to as "information-rich" but also "knowledge poor". Healthcare systems collect huge amounts of data from various sources: lab reports, medical letters, logs of medical tools or programs, medical prescriptions, etc. These massive sets of data can provide great knowledge and information that can improve the medical services, and overall the healthcare domain, such as disease prediction by analyzing the patient's symptoms or disease prevention, by facilitating the discovery of behavioral factors for diseases. Unfortunately, only a relatively small volume of the textual eHealth data is processed and interpreted, an important factor being the difficulty in efficiently performing Big Data operations. In the medical field, detecting domain-specific multi-word terms is a crucial task as they can define an entire concept with a few words. A term can be defined as a linguistic structure or a concept, and it is composed of one or more words with a specific meaning to a domain. All the terms of a domain create its terminology. This chapter offers a critical study of the current, most performant solutions for analyzing unstructured (image and textual) eHealth data. This study also provides a comparison of the current Natural Language Processing and Deep Learning techniques in the eHealth context. Finally, we examine and discuss some of the current issues, and we define a set of research directions in this area

Artificial Intelligence in Invoice Recognition: a Systematic Literature Review

In the era marked by a flourishing economy and rapid advancements in information technology, the proliferation of invoice data has accentuated the urgent need for automated invoice recognition. Traditional manual methods, long relied upon for this task, have proven to be inefficient, error-prone, and incapable of coping with the rising volume of invoices. This research endeavours to addresses the imperative of automating invoice recognition by exploring, assessing, and advancing cutting-edge algorithms, techniques, and methods within the domain of Artificial Intelligence (AI). This research conducts a comprehensive Systematic Literature Review (SLR) to investigate Computer Vision (CV) approaches, encompassing image preprocessing, Layout Analysis (LA), Optical Character Recognition (OCR), and Information Extraction (IE). The objective is to provide valuable insights into these fundamental components of invoice recognition, emphasizing their significance in achieving accuracy and efficiency. This exploration aims to contribute to the development of more effective automated systems for extracting information from invoices, addressing the challenges posed by diverse formats and content. The results indicate that in LA, the combination of Mask Region-based Convolutional Neural Networks (M-RCNN) and Feature Pyramid Network (FPN) achieves goods results. In OCR, algorithms like Convolutional Recurrent Neural Network (CRNN), You Only Look Once version 4 (YOLOv4) and models inspired by M-RCNN and Faster Region-based Convolutional Neural Network (F-RCNN) with ResNetXt-101 as the backbone demonstrate remarkable performance. When it comes to IE, algorithms inspired by F-RCNN and Region Proposal Network (RPN), Grid Convolutional Neural Network (G-CNN) and Layer Graph Convolutional Networks (LGCN), and Gated Graph Convolutional Network (GatedGCN) consistently deliver the best results