Objectives
This study assessed changes in the visibility of the National Health Information Portal (NHIP), operated by the Korea Disease Control and Prevention Agency, across conventional search portals and generative artificial intelligence (AI) platforms.
Methods
We analyzed 150 health-related keywords derived from NHIP traffic data, Daum search logs, and chronic disease categories across 7 platforms: Naver, Google, Bing, Daum, ChatGPT, Perplexity, and Gemini. Searches were conducted in September 2025 and February 2026; Gemini was assessed only in the second survey. NHIP exposure was defined as appearance within the top 10 organic search results, whereas AI citation was defined as explicit source attribution. Changes were tested using McNemar's test.
Results
Organic search exposure for the NHIP increased on Naver (64.0% to 73.3%, p=0.018) and decreased on Bing (72.0% to 61.3%, p=0.021). AI-summary citation rates increased more substantially on Naver AI Briefing (8.0% to 62.7%, p<0.001), Bing AI Summary (6.7% to 44.7%, p<0.001), Google AI Overview (30.0% to 48.0%, p=0.001), and ChatGPT (16.7% to 38.0%, p<0.001). Gemini cited the NHIP for 96.0% of keywords. For dyslipidemia-related keywords (n=10), Google and ChatGPT showed no NHIP exposure, whereas Gemini cited the NHIP for all keywords.
Conclusion
AI citation represents a new dimension of health information accessibility. To address visibility gaps and marked platform divergence, public health portals should implement a 3-level optimization strategy encompassing machine-readable infrastructure, AI-friendly content formatting, and continuous multi-platform monitoring.

Objectives
Medication adherence remains a major challenge in the management of type 2 diabetes (T2D), especially in middle-income countries such as Morocco. With the rapid development of artificial intelligence, large language models, including ChatGPT, may offer new opportunities for clinical research through simulated patient profiles. This study examined the feasibility of using ChatGPT to generate culturally contextualized virtual profiles of Moroccan patients with T2D and to apply the validated Moroccan Arabic dialect version of the general medication adherence scale (GMAS). Methods: This exploratory pilot simulation study used ChatGPT-4 to generate 11 virtual profiles based on representative sociodemographic and clinical characteristics of Moroccan patients with T2D. Each profile was administered the validated Moroccan Arabic dialect version of the GMAS. Results: Four profiles (36.4%) showed high adherence, 6 (54.5%) displayed moderate adherence, and 1 (9.1%) exhibited low adherence. Expert evaluations were consistent, with all ratings ≥3 on a 4-point scale, supporting the credibility and cultural appropriateness of the profiles. The simulated patterns reflected real-world trends, with higher adherence among profiles treated with oral antidiabetic drugs, covered by health insurance, and having shorter disease duration, and lower adherence among profiles using insulin, lacking insurance, and having longer disease duration. Conclusion: ChatGPT generated credible, culturally adapted patient profiles that enabled application of a validated psychometric tool in a simulated environment. This rapid, low-cost approach may support exploratory research, healthcare training, and intervention design. Comparative studies using real patient data are needed to confirm validity.

Objectives
Artificial intelligence (AI) is reshaping healthcare by improving diagnosis and treatment planning, increasing operational efficiency, and streamlining administrative workflows. This paper integrates findings from an extensive PubMed search (2015–2025) with bibliometric analysis using RStudio and VOSviewer to investigate the comparative applications of AI methods in healthcare, collaborative networks, and emerging trends.
Methods
A total of 1,243 records were identified through the PubMed search, and after removal of 143 duplicates, 1,100 records were screened. Following full-text assessment and exclusion of ineligible studies, 986 articles were included in the final bibliometric analysis.
Results
The main research areas included robotic-assisted surgery, predictive analytics, diagnostic imaging, and precision medicine, with particular emphasis on the prevalence of machine learning and deep learning in imaging and the increasing application of natural language processing to unstructured medical information.
Conclusion
The review emphasizes the need for greater budgetary allocation to scalable and pragmatic AI technologies and for interdisciplinary cooperation among researchers, industry, and healthcare providers. Despite this growth, challenges such as algorithmic bias, data integration, and ethical concerns persist. The paper also highlights the importance of equitable collaboration, accountable AI, and multinational partnerships in ensuring that AI can be used ethically and efficiently in healthcare over the long term to improve patient care and biomedical innovation. It does so by mapping international and regional trends, identifying the most influential authors, institutions, and funding sources, and evaluating methodological approaches.

This review examines how biotechnology advances (CRISPR/Cas9, next-generation targeted therapies, nanotechnology-based drug delivery, and immunotherapies) can be applied to address cancer drug resistance worldwide. It also considers the economic burden of resistance, inequities in access to biotechnology solutions, and ethical concerns surrounding rapid innovation, particularly in low-resource settings. A narrative review synthesized evidence from basic science studies, clinical trials, translational research, and policy analyses. Evidence was prioritized for 2015–2025 publications. The synthesis highlights resistance biology and evaluates how precision medicine, biomarker-guided treatment, and high-throughput drug screening can inform individualized regimens and rational combinations. Breakthroughs in gene editing, targeted inhibitors, nanocarriers, and immune engineering can counter key resistance mechanisms, including resistance-conferring mutations, altered drug transport, immune evasion, and tumor microenvironment–mediated protection. Despite progress, implementation barriers remain substantial: high drug and development costs, limited molecular diagnostics and manufacturing capacity, and regulatory and governance challenges that can delay adoption and widen disparities, particularly in low- and middle-income countries. Integrating biotechnology innovations within precision medicine frameworks may improve treatment selection and patient outcomes. Maximizing public health impact requires affordability and financing strategies, robust ethical oversight, timely regulatory pathways, and coordinated global collaboration to ensure access to effective therapies across health systems worldwide.

Objectives
The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.