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A review of optimization strategies for deep and machine learning in diabetic macular edema

A. M. Mutawa, Khalid Sabti, Bibin Shalini Sundaram Thankaleela, Seemant Raizada • Frontiers in Artificial Intelligence (2026)

Diabetic macular edema (DME) is a primary contributor to visual impairment in diabetic patients, necessitating precise and prompt analysis for optimal treatment. Recent breakthroughs in deep learning (DL) and machine learning (ML) have yiel...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1684752

A bibliometric analysis of industry 4.0 digital manufacturing and smart technologies for sustainable development

Ferhat Yilmaz, Zeynep Ozguner • Discover Sustainability (2026)

Abstract This study provides a broad bibliometric analysis of Industry 4.0 as well as technology-based production systems within the appropriate scientific literature to target the description of the dynamic structure, intellectual foundati...

DOAJ Open Access (DOAJ) DOI: 10.1007/s43621-026-03275-w

<bold>CroSSHeteroFL</bold>: Cross-Stratified Sampling Composition-Fitting to Federated Learning for Heterogeneous Clients

Vo Phuc Tinh, Hoang Hai Son, Nguyen Hoang Nam, Duc Ngoc Minh Dang et al. • IEEE Access (2024)

In the large-scale deployment of federated learning (FL) systems, the heterogeneity of clients, such as mobile phones and Internet of Things (IoT) devices with different configurations, constitutes a significant problem regarding fairness, ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3475737

Artificial Intelligence and Deep Learning in Ophthalmology

Daniel Shu Wei Ting, Louis Robert Pasquale, Lily Peng, John Peter Campbell et al. • Springer eBooks (2018)

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to i...

OPENALEX Open Access DOI: https://doi.org/10.1007/978-3-030-64573-1_200 PMC: 6362807

Large language model bias auditing for periodontal diagnosis using an ambiguity-probe methodology: a pilot study

Teerachate Nantakeeratipat • Frontiers in Digital Health (2026)

BackgroundLarge Language Models (LLMs) in healthcare holds immense promise yet carries the risk of perpetuating social biases. While artificial intelligence (AI) fairness is a growing concern, a gap exists in understanding how these models ...

DOAJ Open Access (DOAJ) DOI: 10.3389/fdgth.2025.1687820

Artificial Intelligence-Driven Salivary Biomarkers for Detection and Risk Stratification of Oral Potentially Malignant Disorders and OSCC: A Protocol for a Systematic Review

Shazia Fathima J.H, Mohammed Sibgatullah, Mohmed Isaqali Karobari • International Journal of Qualitative Methods (2026)

Objective This systematic review protocol aims to synthesize evidence on artificial intelligence-driven analysis of salivary biomarkers for the detection, risk stratification and differentiation of OPMDs and OSCC, with particular attention ...

DOAJ Open Access (DOAJ) DOI: 10.1177/16094069261504893

AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction

Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang • JMIR Medical Informatics (2025)

This viewpoint article explores the transformative role of artificial intelligence (AI) in predicting perioperative hypoxemia through the integration of deep learning with multimodal clinical data, including lung imaging, pulmonary function...

DOAJ Open Access (DOAJ) DOI: 10.2196/73995

Optimizing Energy Consumption of Edge-Cloud Environments: A comparative Study Between PPO and PSO

Alejandro Espinosa, Xavier Samos, Daniel Ulied, Jordi Marias et al. • International Journal of Computational Intelligence Systems (2025)

Abstract As the usage of the edge-cloud continuum increases, Kubernetes presents itself as a solution that allows easy control and deployment of applications in these highly-distributed and heterogeneous environments. In this context, Artif...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44196-025-01073-2

Personalized ideological content recommendation via reinforcement learning as a digital therapeutic for anxiety reduction in youth

Yu Wang • Discover Artificial Intelligence (2026)

Abstract Adaptive digital therapies are needed with increasing rates of youth anxiety. However, existing systems lack personalization and dynamic learning. To solve this, the proposed research is to build a personalized ideological content ...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01840-w

Accuracy and reliability of Manus, ChatGPT, and Claude in case-based dental diagnosis

Ahmed A. Madfa, Abdullah F. Alshammari, Bassam A. Anazi, Yousef E. Alenezi et al. • Frontiers in Oral Health (2026)

IntroductionArtificial intelligence (AI), particularly large language models (LLMs), is transforming healthcare education and clinical decision-making. While models like ChatGPT and Claude have demonstrated utility in medical contexts, thei...

DOAJ Open Access (DOAJ) DOI: 10.3389/froh.2025.1686090

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period

Hansong Zhu, Si Chen, Weixia Qin, Joldosh Aynur et al. • BMC Infectious Diseases (2024)

Abstract Objective At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, th...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12879-024-09750-x

Artificial intelligence and echocardiography

Maryam Alsharqi, William J. Woodward, J. A. Mumith, D. C. Markham et al. • Echo Research and Practice (2018)

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experie...

OPENALEX cc-by DOI: https://doi.org/10.1530/erp-18-0056 PMC: 6280250

Managing Artificial Intelligence

Nicholas Berente, Bin Gu, Jan Recker, Radhika Santhanam • MIS Quarterly (2021)

Managing artificial intelligence (AI) marks the dawn of a new age of information technology management. Managing AI involves communicating, leading, coordinating, and controlling an ever-evolving frontier of computational advancements that ...

OPENALEX Open Access DOI: https://doi.org/10.25300/misq/2021/16274