The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.

Periodontitis is a severe infection that damages the bone surrounding teeth, making severity assessment challenging for dentists. Utilizing artificial intelligence (AI) techniques enhances diagnostic accuracy in radiographic analysis, enabling a more efficient and precise diagnosis. The aim of this research is to develop an AI-based diagnostic system comprising two deep learning stages, designed to efficiently detect and classify periodontitis from panoramic radiographs. In the proposed two-stage system, a binary classifier is first utilized to determine the presence of periodontal bone loss, which is then localized and classified by the second AI model. Based on our extensive research, we concluded that convolutional neural networks (CNNs) are the most effective type of neural network for addressing our problem. To ensure the accuracy and reliability of results, we developed two robust CNN models, YOLOv8 and MobileNet-v2, that have shown significant performance in similar applications. The primary model, MobileNet-v2, was utilized to detect periodontitis in panoramic radiographs, while the secondary model, YOLOv8, was developed to localize the affected regions and classify the severity level of the periodontitis. A custom dataset was created to comprise 817 panoramic images. Our developed YOLOv8 model achieved a testing precision of 0.74 and a recall of 0.7, while the MobileNet-v2 model achieved a testing accuracy of 0.88 and a recall of 1.0. These results were further validated by an oral radiology specialist to ensure their accuracy and reliability. Our proposed system is considered to be efficient, cost-effective, and easy to use compared to existing systems in the literature and similar software services available in the market for enhancing diagnosis of periodontitis in clinical practice.

License plate recognition is a pivotal technology in global vehicle surveillance systems, yet the development of effective systems for Egyptian license plates, particularly for motorcycles, has been constrained by the lack of a comprehensive dataset. Several Egyptian license plate recognition systems have been developed using deep learning models, image processing procedures, morphological operations, and other techniques, focusing exclusively on car license plates and not addressing the unique challenges of motorcycle license plate recognition. This paper presents a novel YOLOv8-based recognition system designed to address this significant research gap. The proposed system efficiently detects and tracks license plates in video streams, applies advanced image enhancement techniques, and leverages optical character recognition (OCR) to accurately extract alphanumeric characters. One of the key contributions of this work is the creation and annotation of a new dataset comprising over 1200 images of motorcycle license plates, specifically tailored for the YOLO format. The system demonstrates superior performance, achieving a 100% detection rate and an 80% recognition accuracy, surpassing existing algorithms in the field. The recognized license plates are systematically stored in a .csv file for future analysis, enhancing the system’s utility in real-world applications. This research sets a new benchmark for Egyptian license plate recognition, particularly in the underexplored domain of motorcycle plates, and provides a valuable dataset that can be utilized for further advancements in this area.