Artificial intelligence (AI) is making notable advancements in the medical field, particularly in bone fracture detection. This systematic review compiles and assesses existing research on AI applications aimed at identifying bone fractures through medical imaging, encompassing studies from 2010 to 2023. It evaluates the performance of various AI models, such as convolutional neural networks (CNNs), in diagnosing bone fractures, highlighting their superior accuracy, sensitivity, and specificity compared to traditional diagnostic methods. Furthermore, the review explores the integration of advanced imaging techniques like 3D CT and MRI with AI algorithms, which has led to enhanced diagnostic accuracy and improved patient outcomes. The potential of Generative AI and Large Language Models (LLMs), such as OpenAI’s GPT, to enhance diagnostic processes through synthetic data generation, comprehensive report creation, and clinical scenario simulation is also discussed. The review underscores the transformative impact of AI on diagnostic workflows and patient care, while also identifying research gaps and suggesting future research directions to enhance data quality, model robustness, and ethical considerations.

Bacterial classification is a vital step in medical diagnosis. This procedure normally has several stages. An early stage involves inspecting the morphology of the bacterial colonies. Traditionally, a bacterial colony expert inspects the sample to determine the type of bacteria through visual inspection or molecular biology techniques. With advances in image processing, specifically, the use of deep and transfer learning techniques, and the wide availability of cameras, we applied deep and transfer learning techniques to address this task without requiring expert knowledge or sample shipping. We used a convolutional neural network (CNN) to identify different bacterial colonies based on their appearance in images captured by cell phone cameras. In this paper, we collected a dataset that contains images of different bacteria taken by cell phone cameras with various settings. Thus, images of two classes of bacterial colonies were obtained in King Abdulaziz City for Science and Technology. The dataset contains 8,043 images. The experimental results show that our application has high accuracy without requiring expert inspections.

Domain Adaptation aims at adapting the knowledge learned from a domain (source-domain) to another (target-domain). Existing approaches typically require a portion of task-relevant target-domain data a priori. We propose an approach, zero-shot deep domain adaptation (ZDDA), which uses paired dual-domain task-irrelevant data to eliminate the need for task-relevant target-domain training data. ZDDA learns to generate common representations for source and target domains data. Then, either domain representation is used later to train a system that works on both domains or having the ability to eliminate the need to either domain in sensor fusion settings. Two variants of ZDDA have been developed: ZDDA for classification task (ZDDA-C) and ZDDA for metric learning task (ZDDA-ML). Another limitation in Existing approaches is that most of them are designed for the closed-set classification task, i.e., the sets of classes in both the source and target domains are ‘`known." However, ZDDA-C is also applicable to the open-set classification task where not all classes are ``known" during training. Moreover, the effectiveness of ZDDA-ML shows ZDDA’s applicability is not limited to classification tasks. ZDDA-C and ZDDA-ML are tested on classification and metric-learning tasks, respectively. Under most experimental conditions, ZDDA outperforms the baseline without using task-relevant target-domain-training data.

In this paper, we present an approach for navigating a robotic wheelchair that provides users with multiple levels of autonomy and navigation capabilities to fit their individual needs and preferences. We focus on three main aspects: (i) egocentric computer vision based motion control to provide a natural human-robot interface to wheelchair users with impaired hand usage; (ii) techniques that enable user to initiate autonomous navigation to a location, object or person without use of the hands; and (iii) a framework that learns to navigate the wheelchair according to its user’s, often subjective, criteria and preferences. These contributions are evaluated qualitatively and quantitatively in user studies with several subjects demonstrating their effectiveness. These studies have been conducted with healthy subjects, but they still indicate that clinical tests of the proposed technology can be initiated.

Motivated by the emerging needs to improve the quality of life for the elderly and disabled individuals who rely on wheelchairs for mobility, and who might have limited or no hand functionality at all, we propose an egocentric computer vision based co-robot wheelchair to enhance their mobility without hand usage. The co-robot wheelchair is built upon a typical commercial power wheelchair. The user can access 360 degrees of motion direction as well as a continuous range of speed without the use of hands via the egocentric computer vision based control we developed. The user wears an egocentric camera and collaborates with the robotic wheelchair by conveying the motion commands with head motions. Compared with previous sip-n-puff, chin-control and tongue-operated solutions to hands-free mobility, this egocentric computer vision based control system provides a more natural human robot interface. Our experiments show that this design is of higher usability and users can quickly learn to control and operate the wheelchair. Besides its convenience in manual navigation, the egocentric camera also supports novel user-robot interaction modes by enabling autonomous navigation towards a detected person or object of interest. User studies demonstrate the usability and efficiency of the proposed egocentric computer vision co-robot wheelchair.

Recent development in Natural Language Processing (NLP) techniques has encouraged NLP-based application in various field including business, legal and health. An important process for all NLP projects is text preprocessing which is a process that modifies text data before using them in a machine learning model. Usually text preprocessing process includes cleaning, filtering, removing and replacing some texts to increase model accuracy, robustness, reduce data size or preserve privacy. Named entities recognizer (NER) is an NLP tool which finds Named Entities in text such as: names, organization, addresses, numbers and date. In this work, we create a preproccessing approach that uses NER to find named entities and, then, use them to improve accuracy and preserve privacy instead of removing them or letting them became noise to our data. Experiments for text classification task using our approach have been conducted on several datasets some of which was collected in-house. Experiments indicate that using this approach enhances classifier accuracy and reduces dimensionality while, also, preserve privacy.