Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People’s Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI’s and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer’s disease, Parkinson’s diseases, COVID-19, and diabetes. The author keyword analysis identified “deep learning”, “convolutional neural network”, and “classification” as dominant research themes. Conclusions: AI’s transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.

Background: The worldwide effects of the COVID-19 pandemic have been profound, and the Arab world has not been exempt from its wide-ranging consequences. Within this context, social media platforms such as Twitter have become essential for sharing information and expressing public opinions during this global crisis. Careful investigation of Arabic tweets related to COVID-19 can provide invaluable insights into the common topics and underlying sentiments that shape discussions about the COVID-19 pandemic. Objective: This study aimed to understand the concerns and feelings of Twitter users in Arabic-speaking countries about the COVID-19 pandemic. This was accomplished through analyzing the themes and sentiments that were expressed in Arabic tweets about the COVID-19 pandemic. Methods: In this study, 1 million Arabic tweets about COVID-19 posted between March 1 and March 31, 2020, were analyzed. Machine learning techniques, such as topic modeling and sentiment analysis, were applied to understand the main topics and emotions that were expressed in these tweets. Results: The analysis of Arabic tweets revealed several prominent topics related to COVID-19. The analysis identified and grouped 16 different conversation topics that were organized into eight themes: (1) preventive measures and safety, (2) medical and health care aspects, (3) government and social measures, (4) impact and numbers, (5) vaccine development and research, (6) COVID-19 and religious practices, (7) global impact of COVID-19 on sports and countries, and (8) COVID-19 and national efforts. Across all the topics identified, the prevailing sentiments regarding the spread of COVID-19 were primarily centered around anger, followed by disgust, joy, and anticipation. Notably, when conversations revolved around new COVID-19 cases and fatalities, public tweets revealed a notably heightened sense of anger in comparison to other subjects. Conclusions: The study offers valuable insights into the topics and emotions expressed in Arabic tweets related to COVID-19. It demonstrates the significance of social media platforms, particularly Twitter, in capturing the Arabic-speaking community’s concerns and sentiments during the COVID-19 pandemic. The findings contribute to a deeper understanding of the prevailing discourse, enabling stakeholders to tailor effective communication strategies and address specific public concerns. This study underscores the importance of monitoring social media conversations in Arabic to support public health efforts and crisis management during the COVID-19 pandemic.

تناقش هذه الورقة البحثيّة اختباريّاً التجربة الأردنيّة الأولى لتطبيق قانون الدفاع رقم (13) لسنة 1992، ومدى التقيد بالفلسفة الحقوقيّة لحماية الحقوق والحريات العامة أثناء تطبيق هذا القانون للحد من آثار جائحة كورونا على مختلف القطاعات، والسوابق القضائيّة ذات الصلة، وتقديم مجموعة من التوصيات المقترحة في سبيل تعميق النهج الحقوقيّ وطنيّاً، ومواءمة التشريعات الوطنيّة مع المبادئ الدستوريّة العالميّة والحقوقيّة وفق المعايير الدوليّة لحقوق الإنسان. وقد خلصت هذه الورقة البحثية إلى نتيجة مفادها خلو الدستور الأردنيّ بالمطلق من إحاطة اللّجوء إلى تطبيق قانون الدّفاع وإعلان حالة الأحكام العرفيّة بضوابط دستوريّة تكفل حماية الحقوق والحرّيات العامّة إبان مواجهة الحالات الاستثنائيّة على غرار الأصول المعياريّة الواردة في الدّساتير المُقارنة، وقدّمت مجموعة توصيات؛ بإعادة النّظر في التّنظيم الدستوريّ لإعلان حالة الطوارئ أو إعلان الأحكام العُرفيّة بموجب المادتين (124،125)، وتعديل قانون الدّفاع رقم (13) لسنة 1992 بإضافة مادة صريحة تحدّد القيمة القانونيّة لأوامر الدّفاع، واعتبارها قرارات إداريّة تنظيميّة قابلة...

The transformation of information dissemination and public discourse was heavily influenced by the rapid rise of social media platforms, especially Twitter, and this was particularly evident in times of conflict and war. The huge explosion of social media usage has, in turn, created a large amount of data that can be analyzed through different methods such as text mining and natural language processing. In this paper, we employ topic modeling to extract and analyze the topics of discussion around the Russian-Ukraine conflict in the Middle East. The analysis is facilitated by collecting dialectical Arabic tweets specifically containing terms relating to the Ukraine-Russia conflict. Investigation and comprehension of the dominant themes and views carried by the discussion are held through comparative research of two important topic modeling tools: BERTopic and LDA. From our findings, the influence of social media in forming public opinion, the spread of information, and the creation of a discourse regarding the Russian-Ukrainian war in the Middle East becomes visible. The topic modeling in our study presents the broad spectrum of views and themes emerging through social media discourse. This comprehensive perspective assists in the understanding of the intricate complexities surrounding this geopolitical conflict and offers a deep dive into the multifaceted nature of the matter.

Query expansion is the process of reformulating the original query by adding relevant words. Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system. Adding words that can positively impact the quality of the search query or are informative enough play an important role in returning or gathering relevant documents that cover a certain topic can result in improving the efficiency of the information retrieval system. Typically, query expansion techniques are used to add or substitute words to a given search query to collect relevant data. In this paper, we design and implement a pipeline of automated query expansion. We outline several tools using different methods to expand the query. Our methods depend on targeting emergent events in streaming data over time and finding the hidden topics from targeted documents using probabilistic topic models. We employ Dynamic Eigenvector Centrality to trigger the emergent events, and the Latent Dirichlet Allocation to discover the topics. Also, we use an external data source as a secondary stream to supplement the primary stream with relevant words and expand the query using the words from both primary and secondary streams. An experimental study is performed on Twitter data (primary stream) related to the events that happened during protests in Baltimore in 2015. The quality of the retrieved results was measured using a quality indicator of the streaming data: tweets count, hashtag count, and hashtag clustering.