Alaa Abd-alrazaq

Sema Helali, Lina Abu Nada, Sausan Al Kawas et al.

Background: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remains poorly understood. Three distinct model categories have emerged: language-generative models, discriminative vision foundation models, and dental-specific foundation models, with no unified review examining their relationships and collective limitations. Methods: Following PRISMA-ScR guidelines, we systematically searched four databases (PubMed, Google Scholar, Scopus, arXiv), screened independently by two reviewers. After applying inclusion/exclusion criteria, 97 studies (2020-2026) were included. We propose a two-dimensional classification framework organizing models by architectural paradigm and dental specialization degree. Results: Language-generative models excel at text-based tasks (clinical reasoning, licensing exams, patient communication) but show inconsistent performance on image-dependent diagnostics. Adapted SAM and CLIP variants achieve strong tooth segmentation and lesion detection results. Dental-specific models (DentVFM, DentVLM, OralGPT) demonstrate strongest performance on complex multimodal tasks. Integrated pipelines consistently outperform single-model approaches. A data asymmetry is observed: dental-specific pretraining concentrates almost entirely in the vision domain, reflecting scarce large-scale dental text corpora. Conclusions: General-purpose and dental-specific models play complementary roles; the most effective systems combine both within structured pipelines. Safe autonomous deployment requires resolving three persistent barriers: hallucination in generative models, limited annotated dental datasets, and absent standardized clinical evaluation benchmarks.

Hina Raja, Asim Munawar, Mohammad Delsoz et al.

Purpose: In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. Methods: We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate the effectiveness of LLMs in categorizing large number of ophthalmology papers without human intervention. Results: To evalute the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset. Conclusion: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval in other domains too. We performed trend analysis that enables the researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.

Abdullah Mushtaq, Muhammad Rafay Naeem, Ibrahim Ghaznavi et al.

Systematic Literature Reviews (SLRs) are foundational to evidence-based research but remain labor-intensive and prone to inconsistency across disciplines. We present an LLM-based SLR evaluation copilot built on a Multi-Agent System (MAS) architecture to assist researchers in assessing the overall quality of the systematic literature reviews. The system automates protocol validation, methodological assessment, and topic relevance checks using a scholarly database. Unlike conventional single-agent methods, our design integrates a specialized agentic approach aligned with PRISMA guidelines to support more structured and interpretable evaluations. We conducted an initial study on five published SLRs from diverse domains, comparing system outputs to expert-annotated PRISMA scores, and observed 84% agreement. While early results are promising, this work represents a first step toward scalable and accurate NLP-driven systems for interdisciplinary workflows and reveals their capacity for rigorous, domain-agnostic knowledge aggregation to streamline the review process.

Mahmood Alzubaidi, Marco Agus, Khalid Alyafei et al.

Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM Library, Google Scholar, and the Web of Science. We retrieved studies published between 2010 to 2021. Data extracted from studies were synthesized using a narrative approach. Out of 1269 retrieved studies, we included 107 distinct studies from queries that were relevant to the topic in the survey. We found that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound images (n=19). Classification is the most used method (n=42), followed by segmentation (n=31), classification integrated with segmentation (n=16) and other miscellaneous such as object-detection, regression and reinforcement learning (n=18). The most common areas within the pregnancy domain were the fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen (n=10), and lastly the fetus face (n=10). In the most recent studies, deep learning techniques were primarily used (n=81), followed by machine learning (n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI techniques played a crucial role in predicting fetal diseases and identifying fetus anatomy structures during pregnancy. More research is required to validate this technology from a physician's perspective, such as pilot studies and randomized controlled trials on AI and its applications in a hospital setting.

Amal Awadalla Ali, Asma Hamid ElFadl, Maha Fawzy Abujazar et al.

Contact tracing apps are powerful software tools that can help control the spread of COVID-19. In this article, we evaluated 53 COVID-19 contact tracing apps found on the Google Play Store in terms of their usage, rating, access permission, and user privacy. For each app included in the study, we identified the country of origin, number of downloads, and access permissions to further understand the attributes and ratings of the apps. Our results show that contact tracing apps had low overall ratings and nearly 40% of the included apps were requesting dangerous access permission including access to storage, media files, and camera permissions. We also found that user adoption rates were inversely correlated to access permission requirements. To the best of our knowledge, our article summarizes the most extensive collection of contact tracing apps for COVID-19. We recommend that future contact tracing apps should be more transparent in permission requirements and should provide justification for permissions requested to preserve the app users privacy.