Application Of Artificial Intelligence Research Articles

Guidelines for glaucoma imaging classification, annotation, and quality control for artificial intelligence applications

Glaucoma is an eye disease characterized by pathologically elevated intraocular pressure, optic nerve atrophy, and visual field defects, which can lead to irreversible vision loss. In recent years, the rapid development of artificial intelligence (AI) technology has provided new approaches for the early diagnosis and management of glaucoma. By classifying and annotating glaucoma-related images, AI models can learn and recognize the specific pathological features of glaucoma, thereby achieving automated imaging analysis and classification. Research on glaucoma imaging classification and annotation mainly involves color fundus photography (CFP), optical coherence tomography (OCT), anterior segment optical coherence tomography (AS-OCT), and ultrasound biomicroscopy (UBM) images. CFP is primarily used for the annotation of the optic cup and disc, while OCT is used for measuring and annotating the thickness of the retinal nerve fiber layer, and AS-OCT and UBM focus on the annotation of the anterior chamber angle structure and the measurement of anterior segment structural parameters. To standardize the classification and annotation of glaucoma images, enhance the quality and consistency of annotated data, and promote the clinical application of intelligent ophthalmology, this guideline has been developed. This guideline systematically elaborates on the principles, methods, processes, and quality control requirements for the classification and annotation of glaucoma images, providing standardized guidance for the classification and annotation of glaucoma images.

Exploring Hardware-Efficient Architectures of Golomb–Rice Decoder for TinyML Applications

Deep Neural Networks (DNNs) have revolutionized Artificial Intelligence (AI) applications by tackling real-world problems. Yet, their deployment on Internet of Things (IoT) edge devices remains challenging due to limited resources. The energy-intensive process of accessing millions of parameters during DNN inference has become a significant bottleneck. While weight compression offers a potential solution, existing hardware decompression units struggle to maintain power, area, energy efficiency. This research introduces two innovative decoders: an Interleaved-memory-based Parallel (ImbP) GR decoder and a scalable Tree-Unary-based Parallel (TubP) GR decoder. By integrating these decoders with an industrial-strength Neural Network (NN) accelerator, their performance was evaluated using three TinyML benchmarks. Comparative analysis revealed that the TuP GR decoder outperforms the ImP GR decoder regarding performance metrics and hardware efficiency. The scalable TubP GR decoder achieves remarkable efficiency, offering 4-weight and 8-weight decoding capabilities that consume 0.43[Formula: see text]mW and 0.79[Formula: see text]mW, respectively, while delivering impressive throughput rates of 888[Formula: see text]MBps and 1.3[Formula: see text]GBps.

Artificial Intelligence in the Oil and Gas Industry: Applications, Challenges, and Future Directions

This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges.

AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies

Background: The integration of artificial intelligence (AI) into cervical cancer diagnostics has shown promising advancements in recent years. AI technologies, particularly in the analysis of cytological images, offer potential improvements in diagnostic accuracy and screening efficiency. However, challenges regarding model generalizability, explainability, and operational integration into clinical workflows persist, impeding widespread adoption. Aim: This narrative review aims to critically evaluate the current state of AI in cervical cancer diagnostic cytology, identifying trends, key developments, and areas requiring further research. It also explores the potential for AI to improve diagnostic processes, alongside examining international guidelines and consensus on its adoption. Methods: A narrative review was conducted through a comprehensive search of PubMed and Scopus databases. Thirty studies published between 2020 and 2025 were selected based on their relevance. Results: The literature review reveals a growing interest in the application of AI for cervical cancer diagnostics, particularly in the automated interpretation. However, large-scale clinical adoption remains limited. Most studies are experimental or application-based in controlled settings. Consensus efforts and specific recommendations for this domain are still limited and not specific. Key barriers include limited model generalizability, lack of explainability, challenges in integration into clinical workflows, and regulatory and infrastructural constraints. Conclusions: A sustainable and meaningful integration of AI in cervical cancer diagnostics requires a unified framework that addresses both technical challenges and operational needs, supported by context-specific strategies and broader consensus-building efforts.

Knowledge and perception of artificial lntelligence education among undergraduate healthcare students.

Artificial intelligence (AI) is gaining recognition for its ability to enhance patient outcomes in healthcare. Therefore, integrating AI into the undergraduate curriculum is essential to equip students with foundational knowledge before graduation. This study investigated the knowledge and perceptions of AI education among undergraduate healthcare students. This cross-sectional study targeted students from various health colleges in Saudi Arabia and was conducted from January 1, 2024, to April 30, 2024. Data were collected using a validated questionnaire comprising three sections: demographics, knowledge, and perceptions of AI. We utilized a convenience sampling technique to distribute the survey. Descriptive analysis was performed to assess the baseline characteristics, and a multivariable logistic regression model was used to identify the covariates that significantly influence knowledge scores. A total of 303 students participated in our study, including 142 pharmacy and 161 non-pharmacy students. Non-pharmacy students demonstrated significantly greater knowledge of AI than pharmacy students (p = 0.0084). Positive perceptions of AI were more prevalent than negative perceptions among all participants (p = 0.0062). Pharmacy and non-pharmacy students held significantly different knowledge of advanced AI skills (p = 0.0019) and the importance of AI applications (p = 0.0259). Additionally, there were significant differences in perceptions regarding the belief that AI-powered medical devices would help healthcare providers reduce medical errors (p = 0.0279). Healthcare students recognize the benefit of AI in enhancing patient outcomes but lack practical knowledge of its medical applications. They believe integrating AI education into their curriculum is essential for acquiring skills before graduation. Therefore, enhancing AI training may foster innovation, enhance patient outcomes, and align with the evolving demands of the healthcare sector.

Application of Artificial Intelligence in Higher Education An Evaluation of Opportunities and Challenges: A Prospective Study

The current study aims to reveal possible opportunities for applying artificial intelligence in higher education, and to identify the most important challenges that such application faces and the maximum benefits that can be acquired from employing artificial intelligence techniques in developing the educational process and the educational services in higher education institutions. This study followed the descriptive analytical approach in addressing the topic of applying artificial intelligence in higher education by reviewing the literature and previous studies that addressed this topic. The study concluded that it is possible to benefit from the application of artificial intelligence in higher education in the fields of teaching, learning, administration, research, and others, if the requirements of this application are dealt with effectively and the facilities that support the use of artificial intelligence techniques are provided. The study recommends confronting the challenges that weaken the effectiveness of the application of artificial intelligence in higher education. The most important challenges in this regard are: the lack of educational policies related to the application of artificial intelligence in education, the weakness of the infrastructure needed to supports the use of artificial intelligence techniques, the need to train academics and learners on how to deal with new applications, and the lack of due awareness of the importance of artificial intelligence applications and its role in developing higher education. KEY WORDS: artificial intelligence, higher education, opportunities, challenges, foreseeing the future

Novel Artificial Intelligence Applications in Energy: A Systematic Review

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis.

Application of real-time artificial intelligence to cataract surgery

Background/aims Artificial intelligence (AI) in Ophthalmology has yet to be applied to real-time cataract surgery. This work explores a new AI tool, developed for phacoemulsification, and evaluates its potential uses. First, our study aimed to demonstrate the use of AI in phase recognition and analysis of phacoemulsification. Second, to evaluate the application of real-time AI to live cataract surgery. Methods Phase I: surgical video recordings of adult patients undergoing cataract surgery at Moorfields Eye Hospital were captured. The AI, via Touch Surgery Ecosystem, was developed and used to segment surgery into phases based on the International Council of Ophthalmology-Ophthalmology Surgical Competency Assessment Rubric tool. Phase II: having demonstrated the AI’s functionality in phase I, a further group of phacoemulsification patients was recruited into a live surgery study arm. Three auxiliary screens were deployed in the operating theatres, displaying phase detection and phase relevant information in real time. Results Phase I: 192 videos were analysed between March 2020 and March 2021. Average case duration for consultants (n=68), advanced trainees (n=59) and junior trainees (n=65) was 11.18, 17.54 and 21.36 min, respectively. Efficiency benchmarks were determined using the median metric values for advanced trainee and consultant cases, respectively. Phase II: efficiency metrics for 74 cases with screen deployment and 26 without were compared. With real-time AI, consultant surgeons had a significant decrease in case duration. Conclusions We demonstrate the first use of a fully independent AI platform for analysing efficiency metrics in cataract surgery. Real-time AI has the potential to improve operative efficiency and surgical team training.

AI-based Personalization of Pempek Bunan's Marketing Content on Social Media

This research focuses on the application of artificial intelligence (AI) in personalizing marketing content on social media by Pempek Bunan, a small and medium-sized enterprise (MSME) based in Palembang. By using AI to manage its digital marketing strategy, Pempek Bunan can tailor its content based on customer preferences, increase consumer engagement, and streamline the process of creating relevant content. A qualitative descriptive research method was used to collect data through interviews and direct observation. This approach allows researchers to gain deeper insights into Pempek Bunan’s experience with AI and how this technology is adapted to meet local business needs. The findings of this research show that AI helps Pempek Bunan develop more personalized content, although challenges such as language style adjustments and creativity limitations still require human intervention. The social construction of technology (SCOT) theory is used to analyze the interactions between relevant social groups, like those between the marketing team and business owners, in the process of AI adoption. This study found that the success of AI adoption depends on negotiation and collaboration between groups to ensure that the technology meets local needs and preferences. This process reflects interpretive flexibility, where AI is understood not only as a technical tool but also as a solution that can be adapted to different social contexts. Overall, the use of AI in Pempek Bunan’s digital marketing provides tangible benefits, including increased marketing effectiveness and work efficiency. However, the stabilization of this technology is still evolving, with human involvement necessary to ensure content relevance and the sustainability of personalized marketing strategies. This research is expected to provide new insights into the application of AI in MSMEs in Indonesia and open opportunities for the development of smarter and more effective marketing strategies in the future.

Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles

The use of dry powder inhalers (DPIs) represents a cornerstone in the treatment of chronic pulmonary diseases. However, suboptimal inhalation techniques, including inadequate airflow rates, have been a persistent concern for achieving effective therapeutic outcomes, as many patients remain unaware of their insufficient inhalation performance. As an effective strategy, a digital monitoring system, coupled with dry powder inhalers (DPIs), has emerged to estimate flow profiles and provide inhalation information. The estimation could be further facilitated by the application of artificial intelligence (AI). In this work, a novel digital system to primarily monitor pressure during DPI usage was successfully designed, and advanced machine learning (ML) techniques were then employed to estimate inhalation flow profiles based on the captured data. Four optimal machine learning models were selected for subsequent inhalation parameter prediction, given their superior generalization ability. By using these models, inhalation flow profiles could be successfully estimated, with an excellent accuracy of 97.7% for Peak Inspiratory Flow Rate (PIFR) and 95.2% for inspiratory capacity (IC). In summary, the pressure-based digital monitoring system empowered by AI techniques could be successfully applied to assess inhalation flow profiles with excellent accuracy.

Application of artificial intelligence in bioprospecting for natural products for biopharmaceutical purposes

Application of artificial intelligence in bioprospecting for natural products for biopharmaceutical purposes

Methodological aspects of liver fibrosis, dystrophies, and inflammatory lesions morphological verification with artificial intelligence

Non-tumorous liver lesions, including non-alcoholic fatty liver disease, alcoholic liver disease, and drug- and toxin-induced liver injuries, represent a significant clinical and research challenge due to their high prevalence and diagnostic complexity. Traditional morphological examination methods based on visual analysis of histological slides require a high level of specialist expertise, significant time investment, and are prone to subjective factors, reducing their accuracy and reproducibility. In recent years, artificial intelligence (AI) methods, particularly deep learning technologies and convolutional neural networks, have shown significant potential for automating the diagnosis and quantitative assessment of liver morphological changes. This review analyzes publications focusing on the application of AI for morphological verification of liver pathological alterations. Special attention is given to studies employing classification and segmentation models to assess conditions such as fibrosis, hepatocyte steatosis and ballooning degeneration, inflammatory infiltration, and necrosis. The review provides a detailed overview of datasets, annotation methods, and auxiliary tools used for model training and testing. The primary data sources were histological slides, predominantly stained with hematoxylin and eosin, with additional use of histochemical and immunohistochemical staining techniques. Most studies utilized scanned digital images, which ensured a high degree of reproducibility and automation. Performance metrics varied depending on the tasks, but classification models for steatosis and segmentation models for fibrosis often achieved accuracy exceeding 90%. However, significant variability was observed in the assessment of inflammatory infiltration and necrosis. The application of AI methods helps reduce inter-operator variability, shortens diagnostic time, and shifts from subjective semi-quantitative assessment methods to objective quantitative approaches. However, the integration of these technologies into clinical practice requires further algorithm refinement, data standardization, and improved annotation methods. Thus, AI opens new opportunities for the standardization and improvement of diagnostic accuracy, which is especially critical in the context of large-scale screening and monitoring of liver disease treatment outcomes

When AI fails, who gets the blame? Citizens’ attribution patterns in AI-induced public service failures

Globally, governments have increasingly implemented Artificial Intelligence (AI) in public service delivery and decision-making to replace human officials in the name of improving scalability, cost-effectiveness, and efficiency. However, few empirical studies have explored the challenges citizens face in seeking accountability when government AI agents fail. To fill this gap, this paper investigates how citizens perceive and attribute blame for AI-induced public service failures compared to those caused by human officials, addressing the potential ‘accountability deficit’ in AI governance. Using Weiner’s Attribution Theory as the framework, we conducted three scenario-based experiments with 516 participants. The results revealed that citizens generally blamed AI agents less than government departments due to lower perceptions of controllability over the service task compared to the same failures caused by human officials. However, when AI was identified as outsourced, their blame toward the government was significantly lower. Thus, our findings support the idea that the application of AI in public services introduces uncertainty into governmental reputation and accountability. Overall, this study contributes to the growing body of knowledge on AI in public services by underscoring the importance of developing ethical and legal governance frameworks to address potential accountability deficiencies and blame avoidance in public services using AI.

Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.

Recent advances in artificial intelligence (AI) have shown great potential in improving cardiovascular pharmacotherapy by optimizing drug selection, predicting therapeutic efficacy and adverse effects, ultimately improving patient outcomes. Leveraging techniques like machine learning and in silico modelling, AI can identify populations likely to benefit from specific treatments, expedite novel drug discovery and reduce costs. Computational methods can also facilitate the detection of drug interactions and tailor interventions based on real-world data, supporting personalized care. Artificial intelligence-based approaches also show promise in streamlining clinical trial design and execution, leveraging on real-time data on patient responsiveness, enhancing recruitment efficiency. However, in order to fully realize these benefits, robust validation across diverse patient populations is necessary to ensure accuracy and generalizability. In addition, addressing concerns regarding data quality, privacy, and bias is equally critical to avoid exacerbating existing healthcare disparities. Scientific societies and regulatory agencies must ultimately establish standardized frameworks for data management, model certification, and transparency, to enable safe and effective integration of AI into clinical practice. This manuscript aims at systematically reviewing the current state-of-the-art applications of AI in cardiovascular pharmacotherapy, describing their current potential in guiding treatment decisions, refine trial methodologies and support drug discovery.

Artificial Intelligence in Da'wah: Navigating the Opportunities and Threats of Digital Innovation

The development of artificial intelligence (AI) technology has opened up new opportunities in various sectors, including digital da’wah. This journal explores how AI innovations can be integrated into digital da’wah to increase the effectiveness and reach of Islamic messages. The study aims to analyze the opportunities and threats of utilizing AI in da’wah. It uses a qualitative method, which focuses on an in-depth understanding of social or cultural phenomena through descriptive and interpretative data collection with the type of research being a desk study. The results show that despite the challenges, the application of AI in digital da’wah offers great potential for innovation and increasing the impact of da’wah. The journal provides practical recommendations to overcome existing challenges and capitalize on existing opportunities, as well as an outlook on the future of AI integration in digital da’wah.

Artificial Intelligence for Moderate Da'wah Communication: Science Perspective

This research reveals that artificial intelligence (AI) can be used to develop moderate preaching that is inclusive and tolerant, as well as support dialogue between religious communities. Leveraging machine learning algorithms and data analysis, AI can help understand people’s needs, identify sensitive issues, and present contextually relevant material. AI also plays a role in creating interactive platforms that enable constructive discussions and reinforce moderate religious values. The research method used was a qualitative method with a descriptive analysis approach. Several research activities such as literature review, observation, and analysis were carried out in order to provide a comprehensive description of moderate da’wah activities using artificial intelligence. Using Miles and Huberman data analysis techniques. The research results show that the application of AI in moderate da’wah not only increases the reach and effectiveness of communication, but also contributes to the formation of positive narratives that can reduce extremism and promote tolerance in society. Thus, the integration of AI in moderate da’wah is a strategic step to create a more harmonious and peaceful social environment.

Environmental impacts of artificial intelligence in health care: considerations and recommendations

Abstract Purpose (stating the main purposes and research question) Anthropogenic resource use contributes to pollution, violent conflict over scarce resources, loss of biodiversity, and diminished quality of life for humans. Moreover, the “safe” amount of carbon dioxide—350 parts per million—has been exceeded. The health care industry is responsible for 4–5% of total world emissions,[i] which is similar to the global food sector.[ii] Health care carbon emissions come from health care infrastructures, supply chains and health care delivery. Increasingly, health care delivery is reliant on technologies which require the use of artificial intelligence to provide supportive care, such as triage algorithms, electronic patient records, and robotics.[iii] While these technological innovations have advanced health care significantly, they also contribute to the negative effects on the environment, among others, through carbon emissions. The environmental impacts of artificial intelligence (AI) in health care—in particular—are understudied. This research seeks to fill this gap. Methods Our team ran an exploratory search in Scopus and PubMed to identify studies that integrate environmental sustainability, artificial intelligence, and health. Results Our research initially yielded 735 studies. 77 of these studies focused on an environmental concern of a health technology or AI-application in a health care setting, but most of the articles in this subset addressed lowering energy consumption of a specific technology, such as a sensor or monitoring technology. Conclusions While there have been studies looking at AI in health care; sustainability in AI; and sustainability in health care, little attention has been paid to the interface between all three. [i] Karliner, J., Slotterback, S., Boyd, R., Ashby, B., & Steele, K. 2019. Health Care’s Climate Footprint: How the Health Sector Contributes to the Global Climate Crisis and Opportunities for Action Healthcare Without HarmARUP; September. [ii] Pichler, P. P., Jaccard, I. S., Weisz, U., & Weisz, H. 2019 International Comparison of Health Care Carbon Footprints, Environmental Research Letters 14, no. 6: 064004. [iii] Khaliq, Abdul, Ali Waqas, Qasim Ali Nisar, Shahbaz Haider, and Zunaina Asghar. 2022. Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society 68: 101807.

Artificial Intelligence Application and Financial Inclusion Nexus on Sustainable Development Goals (SDGs) in Nigeria

This study evaluated Artificial Intelligence Applications and Financial Inclusion Nexus in Nigeria. Financial institutions have always had the problem of meeting the financial needs of the low-income group in society, though, they have done their best to meet their needs, their effort have not yielded good results. Getting individuals and businesses into the formal financial net has been a problem. The present SDGs as constituted rest more on inclusiveness, especially financial inclusion. The SDGs comprise an ambitious 17 goals. While the Sustainable Development Goals (SDGs) do not explicitly address financial inclusion, increased access to financial services is a crucial facilitator for achieving many goals. The financial institutions and products are not suited for the low-income group in society, in addition, rural dwellers do not have access to financial institutions and products. The study used survey instruments and interviews for data collection. That is. using the mixed research method with data from three hundred and eighty-four respondents selected from rural communities, financial institutions, and universities in South-West Nigeria. The data were analysed using descriptive statistics, confirmatory factor analysis, regression analysis, structural equation model (SEM), and narrative technique. The results indicate that AI applications have significantly impacted financial inclusion in South-West Nigeria. Based on this, the study recommended that financial institutions invest in building AI-enabled platforms in local languages that will respond to customers’ needs.

Prompt Engineering for Large Language Models: A Primer for Radiologists

Among artificial intelligence applications, large language models (LLMs) like ChatGPT have shown promising potential in streamlining workflows and facilitating research. This review article aims to provide basics and examples of prompt engineering in optimizing interactions with LLMs for radiologists, promoting precise, relevant, and useful LLM outputs.

The Application and Diagnostic Accuracy of Artificial Intelligence in Rhinology: A Review

The Application and Diagnostic Accuracy of Artificial Intelligence in Rhinology: A Review