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ClimateBoth readings

Automation and Sustainability—The Impact of AI on Energy Consumption and Other Key Features of Industry 4.0/5.0 Technologies

Automation and sustainability are closely intertwined in the evolution of Industry 4.0 and 5.0, where artificial intelligence (AI) plays a key role in transforming energy consumption and production efficiency. For Industry 4.0, AI-based automation has optimized production, logistics, and resource management, reducing waste and improving throughput through predictive analytics and intelligent control systems. These systems have enabled energy-efficient production lines by automatically adjusting processes to mini…

Applied Sciences

Evidence-backed gain

Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States

Evidence-backed gain

Challenges of Artificial Intelligence Development in the Context of Energy Consumption and Impact on Climate Change

Evidence-backed problem

Bias in medical AI: Implications for clinical decision-making

Evidence-backed gain

Digital Twins’ Advancements and Applications in Healthcare, Towards Precision Medicine

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental inf…

Both readings

Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Resea…

Both readings

Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

We are witnessing a novel era of creativity where anyone can create digital content via prompt-based learning (known as prompt engineering). This article investigates prompt engineering as a novel creative skill for creating AI art with text-to-image generation. In three consecutive studies, we explore whether crowdsourced participants can (1) discern prompt quality, (2) write prompts, and (3) refine prompts. We find that participants could evaluate prompt quality and crafted descriptive prompts, but they lacked…

Both readings

Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However,…

Labor

Artificial Intelligence and Its Role in Shaping Organizational Work Practices and Culture

The advent of Artificial Intelligence (AI) is profoundly transforming organizational landscapes, significantly influencing work practices and triggering cultural shifts. This study explores the role of AI in reshaping organizational work practices and examines the resulting cultural transformation. Through a systematic literature review, this study synthesizes existing research to provide a comprehensive understanding of AI’s impact on organizational landscapes. A systematic literature review was conducted, anal…

Science

A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications

Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolut…

Health

Davinci xi: FDA injury report involving AI

The FDA received a injury report involving Davinci xi, made by Intuitive surgical, inc. A review of a clinical literature article titled "usefulness of artificial intelligence for surgical support in robot-assisted distal pancreatectomy: a preliminary case report" was conducted. the article reported an event involving a da vinci-assisted distal pancreatectomy where the dissection line on the lower margin of the pancreas was falsely recognized and pancreatic tissue was damaged. the estimated blood loss…

Health

Prediction of bronchopulmonary dysplasia seven days after birth using respiratory and oxygenation timeseries with machine learning

Accurate prediction of bronchopulmonary dysplasia (BPD) development would allow targeted early treatment. This study aims to develop a machine learning (ML) model incorporating respiratory and oxygenation timeseries to predict BPD development within 1 week after birth. Data was collected retrospectively from a neonatal intensive care (2009-2015). Readily available clinical data and respiratory and oxygenation timeseries (mode of respiratory support, FiO2, SpO2) were gathered. Descriptive features were extracted…

Health

Clinical applications of artificial intelligence in hypertension management: current evidence and future perspectives

Hypertension remains the leading modifiable risk factor for cardiovascular morbidity and mortality worldwide, with persistently inadequate blood pressure control despite guideline-directed therapy. The rapid expansion of digital health data and computational capacity has positioned artificial intelligence (AI) as a promising tool for improving hypertension management through enhanced risk prediction, phenotyping, and individualized care. However, important challenges related to external validation, interpretabil…

Health

Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions

Ischemic stroke management is time-sensitive, and lesion segmentation supports treatment selection, prognostication, and reproducible quantification. Deep learning (DL) aims to accelerate and standardize lesion delineation to augment neuroimaging workflows. We conducted a narrative review of DL-based ischemic stroke lesion segmentation studies published from 2020 to 2025. PubMed, Google Scholar, Scopus, and IEEE Xplore were searched; ~ 500 records were identified, and 40 full-text studies were included after scr…

Health

Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department

Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (ED). Standardized clinical scoring systems, including the Wells and revised Geneva scores, form the cornerstone of initial risk stratification but have limited specificity, leading to unnecessary D‑dimer testing and frequent overuse of CT pulmonary angiography (CTPA). This study aimed to develop explainable machine‑learning (XML) models as a complementary decision‑support laye…