Emergence Of Artificial Intelligence Research Articles

Artificial intelligence and machine learning in optics: tutorial

Across the spectrum of scientific inquiry and practical applications, the emergence of artificial intelligence (AI) and machine learning (ML) has comprehensively revolutionized problem-solving methodologies. This tutorial explores key aspects of AI/ML and their remarkable role in augmenting the capabilities of optics and photonics technologies. Beginning with fundamental definitions and paradigms, the tutorial progresses to classical machine learning algorithms, with examples employing support vector machines and random forests. Extensive discussion of deep learning encompasses the backpropagation algorithm and artificial neural networks, with examples demonstrating the applications of dense and convolutional neural networks. Data augmentation and transfer learning are examined next as effective strategies for handling scenarios with limited datasets. Finally, the necessity of alleviating the burden of data collection and labeling is discussed, motivating the investigation of unsupervised and semi-supervised learning strategies as well as the utilization of reinforcement learning. By providing a structured exploration of AI/ML techniques, this tutorial equips researchers with the essential tools to begin leveraging AI’s transformative potential within the expansive realm of optics and photonics.

Recent Developments in Technology for Sorting Plastic for Recycling: The Emergence of Artificial Intelligence and the Rise of the Robots

Plastics recycling is an important component of the circular economy. In mechanical recycling, the recovery of high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. In chemical recycling, certain types of plastics should be removed first as they negatively affect the process. Such sortation of plastic objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical for any sorting is the proper identification of the plastic type. Spectroscopy is used to this end, increasingly augmented by machine learning (ML) and artificial intelligence (AI). Recent developments in the application of ML/AI in plastics recycling are highlighted here, and the state of the art in the identification and sortation of plastic is presented. Commercial equipment for sorting plastic recyclables is identified from a survey of publicly available information. Automated sorting equipment, ML/AI-based sorters, and robotic sorters currently available on the market are evaluated regarding their sensors, capability to sort certain types of plastics, primary application, throughput, and accuracy. This information reflects the rapid progress achieved in sorting plastics. However, the sortation of film, dark plastics, and plastics comprising multiple types of polymers remains challenging. Improvements and/or new solutions in the automated sorting of plastics are forthcoming.

Update in venous thromboembolism in cancer: Lessons from multi-centre registries

Cancer-associated thrombosis is a common problem in cancer patients and one of the leading causes of death in this population. Randomised clinical trials have shown that both low-molecular-weight heparins and direct oral anticoagulants are the treatments of choice for cancer-associated thrombosis. Despite this, small sample sizes, poor representation of some patient subgroups and lack of information about real-world clinical situations are some of the limitations of randomised trials. To overcome these problems, registries have been established to collect real-world data from patients with cancer-associated thrombosis, offering new evidence and information to supplement the findings of randomised clinical trials. However, few registries have focused exclusively on cancer patients, and some have excluded various subgroups of patients. Additionally, data collection and processing are another major challenge in analysing registry results, where the emergence of artificial intelligence will play a fundamental role.

Examining the role of ChatGPT in the management of distal radius fractures: insights into its accuracy and consistency.

The optimal management of distal radius fractures remains a challenge for orthopaedic surgeons. The emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), especially ChatGPT, affords significant potential in improving healthcare and research. This study aims to assess the accuracy and consistency of ChatGPT's knowledge in managing distal radius fractures, with a focus on its capability to provide information for patients and assist in the decision-making processes of orthopaedic clinicians. We presented ChatGPT with seven questions on distal radius fracture management over two sessions, resulting in 14 responses. These questions covered a range of topics, including patient inquiries and orthopaedic clinical decision-making. We requested references for each response and involved two orthopaedic registrars and two senior orthopaedic surgeons to evaluate response accuracy and consistency. All 14 responses contained a mix of both correct and incorrect information. Among the 47 cited references, 13% were accurate, 28% appeared to be fabricated, 57% were incorrect, and 2% were correct but deemed inappropriate. Consistency was observed in 71% of the responses. ChatGPT demonstrates significant limitations in accuracy and consistency when providing information on distal radius fractures. In its current format, it offers limited utility for patient education and clinical decision-making.

Accuracy of Automated Forensic Dental Age Estimation Lab (F-DentEst Lab) on Large Malaysian Dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80% training and the remaining 20% for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.

ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction

Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicating the situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationships between nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlations of nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.

Utilizing ChatGPT on Designing English Language Teaching (ELT) Materials in Indonesia: Opportunities and Challenges

This study aims to examine the impact of artificial intelligence (AI) on pragmatic English language teaching in universities. The scope of the study is limited to the opportunities and challenges of English language teaching. This study was carried out using a mixed method of conducting surveys and in-depth interviews. A questionnaire link using Google Forms from Spreadsheet was sent to 6 English lecturers via WhatsApp group and personal contact. The study sample consisted of 6 English lecturers from state and private universities in Central Java. Observation, questionnaire analysis, and interview transcripts show that the emergence of artificial intelligence (AI) technology has created opportunities and challenges to teaching and learning English. AI has transformed English language teaching in higher education. The findings and discussion elaborate some novelties of this study; 1) the profile of utilizing ChatGPT in pragmatics ELT teaching, portrays that ChatGPT roles in teaching scenarios play 3 major sessions, like; designing creative and innovative pragmatics teaching materials, setting up the midterm test of pragmatics project, and lastly, assessing and evaluating the pragmatics final test project. 2) The opportunities of using ChatGPT for lecturers, ChatGPT is one of the most sophisticated AI tools available today, and it can write nearly anything that an instructor wants it to. ChatGPT can help lecturers with a variety of tasks, such as creating course outlines, student progress reports, test and quiz questions, and grading rubrics; developing more inclusive and accessible learning activities; interacting with students and their families; and contributing text for websites or bulletin boards to maintain a connection between students and their families. 3) The challenges of applying ChatGPT for lecturers; ChatGPT can provide immediate responses to student questions, ChatGPT can adapt to individual student needs, ChatGPT can increase accessibility and inclusivity by providing language support for students with diverse learning needs, ChatGPT can serve as a valuable tool for language practice and strengthening, ChatGPT can encourage active learning by encouraging students to ask questions, engage in discussions, and seek clarification on challenging concepts, Integrating ChatGPT into language teaching can inspire innovative teaching approaches, such as blended learning models, flipped classrooms, and gamified learning experiences, that leverage technology to improve student learning outcomes

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.

Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05). The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.

The Role of AI in Drug Discovery.

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support.

Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Significant advances have been made in the control of DC/DC converters, owing to the effective combination of linear and nonlinear approaches. The above-mentioned approaches have greatly improved the efficiency as well as stability of the converter, even when faced with changing conditions. Nevertheless, the emergence of artificial intelligence has introduced new perspectives in the domain. The objective of the article is to examine two separate methodologies for controlling a boost-buck converter: using a nonlinear approach, especially backstepping, and utilizing artificial intelligence through fuzzy logic. The main aim of this work is to illustrate the inherent stability and robustness of fuzzy logic controllers compared to backstepping control in managing effectively various variations and challenges encountered in converter control.

Future of Pharmaceutical Industry: Role of Artificial Intelligence, Automation, and Robotics

Background The future of smart factories and pharmaceutical industries has evolved significantly since the 19th century. Computers have been used in the pharmaceutical field since the 1980s with the emergence of artificial intelligence (AI). In addition, automation and robotics are used in the pharmaceutical industry to improve the efficiency of pharmaceutical development and production. Purpose The present review article covers the future roles of AI, automation and robotics in pharmaceutical industries. Methods The current review article employed a comprehensive search strategy across relevant databases, including PubMed, Scopus and Web of Science, utilizing keywords such as AI, Automation, Robotics and Pharmaceutical Industries. The articles considered a focus on recent advancements and emerging trends in the intersection of AI, automation and robotics within pharmaceutical sectors. Results AI, incorporating predictive machine learning and reasoning techniques, aids in the preclinical identification of molecules and forecasting potential lead compounds before conducting clinical trials. automation offers significant benefits in monitoring and predictive maintenance of production lines, power distribution and control machines. Robotic process automation improves efficiency by connecting computer terminals to handle various manufacturing process elements. Conclusion Artificial intelligence, automation and Robotics have sparked innovations in the healthcare business, benefiting the global ecosystem and healthcare delivery. Incorporating these advanced tools in pharmaceutical industries from raw material selection to final product development could improve the quality and safety of the pharmaceutical products and reduce the time and cost.

Oropharyngeal Cancer Staging Health Record Extraction Using Artificial Intelligence

Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intelligence in medicine, data abstraction may be associated with reduced costs but increased efficiency and accuracy of cancer staging. To evaluate an algorithm using an artificial intelligence engine capable of extracting essential information from medical records of patients with oropharyngeal cancer and assigning tumor, nodal, and metastatic stages according to American Joint Committee on Cancer eighth edition guidelines. This retrospective diagnostic study was conducted among a convenience sample of 806 patients with oropharyngeal squamous cell carcinoma. Medical records of patients with staged oropharyngeal squamous cell carcinomas who presented to a single tertiary care center between January 1, 2010, and August 1, 2020, were reviewed. A ground truth cancer stage dataset and comprehensive staging rule book consisting of 135 rules encompassing p16 status, tumor, and nodal and metastatic stage were developed. Subsequently, 4 distinct models were trained: model T (entity relationship extraction) for anatomical location and invasion state, model S (numerical extraction) for lesion size, model M (sequential classification) for metastasis detection, and a p16 model for p16 status. For validation, results were compared against ground truth established by expert reviewers, and accuracy was reported. Data were analyzed from March to November 2023. The accuracy of algorithm cancer stages was compared with ground truth. Among 806 patients with oropharyngeal cancer (mean [SD] age, 63.6 [10.6] years; 651 males [80.8%]), 421 patients (52.2%) were positive for human papillomavirus. The artificial intelligence engine achieved accuracies of 55.9% (95% CI, 52.5%-59.3%) for tumor, 56.0% (95% CI, 52.5%-59.4%) for nodal, and 87.6% (95% CI, 85.1%-89.7%) for metastatic stages and 92.1% (95% CI, 88.5%-94.6%) for p16 status. Differentiation between localized (stages 1-2) and advanced (stages 3-4) cancers achieved 80.7% (95% CI, 77.8%-83.2%) accuracy. This study found that tumor and nodal staging accuracies were fair to good and excellent for metastatic stage and p16 status, with clinical relevance in assigning optimal treatment and reducing toxic effect exposures. Further model refinement and external validation with electronic health records at different institutions are necessary to improve algorithm accuracy and clinical applicability.

Artificial Intelligence in Education: Bibliometric and Systematic Literature Review from 2019 – 2024

In the industrial age of 5.0, technology can be used to make it easier for humans to do their everyday work. The development of information technology today is proven by the emergence of artificial intelligence (AI). Research related to AI in the field of education to this day still continues to be studied and researched. Therefore, there is a need for a literary study to find out the trends of research on artificial intelligence in the field of education in order to facilitate further researchers in determining the themes of their research. This research aims to collect, identify, evaluate, analyze, interpret, and conclude similar research that deals with artificial intelligence in education. The research method uses library study with bibliometric analysis and SLR techniques. The results of this research are divided into four sections according to the research questions studied in 98 articles relevant to research topics. The four components are the benefits of AI, the application of artificial intelligence, the positive and negative impacts of AI, and the challenges to be faced with AI.

Decoding Complexity: A Mathematical Framework for Enhanced Translation Comprehension

Machine translation tools have demonstrated substantial progress in enhancing translation accuracy since the emergence of artificial intelligence. However, challenges persist in reasoning (or the lack thereof), considering contexts, addressing specific word games, and interpreting very long or very short sentences—those exceeding 50 and falling below 7 words (Bowker, 2023 : 893). Additionally, accurately translating technical or specialized terms and their variations remains a hurdle. This research introduces a categorical mathematical formalization of the comprehension stages in translation, along with a model for calculating acceptances (specific meanings of words) during the verification of meaning hypotheses. The goal is to elucidate the comprehension process and integrate contextual considerations. The formalism delineates a series of fundamental cognitive operations involved in comprehension. Furthermore, it advocates for evaluating meaning hypotheses using logical modalities, particularly hypostases, described as phrases (groups of words)—a unit of discourse rather than language—signifying the structure of arguments conveying the speaker's knowledge. The strength of our proposed mathematical model lies in its independence from both source and target languages, as well as the subjectivity of text authors or translators. Additionally, the assessment of meaning hypotheses relies on verifiable logical modalities, ensuring a reliable, explicable, and controllable outcome.

Exploring the effects of intelligent personal assistants on bilingual education teachers’ L2 speaking proficiency

ABSTRACT Communication in a second and foreign language (L2) is regarded as the most challenging and anxiety-inducing language skill for L2 learners and teachers. The emergence of artificial intelligence and automatic speech recognition (ASR) has opened new avenues for enhancing L2 interaction, with intelligent personal assistants (IPAs) showing significant potential. While prior research has predominantly focused on learners, using IPAs for bilingual education (BE) teachers has received relatively little attention. The study investigated the effects of IPAs (i.e. Google Assistant) on BE teachers’ L2 speaking proficiency and their perceptions of IPA-assisted L2 speaking. Thirty Taiwanese BE teachers who were L2 English speakers participated in a semester-long IPA-mediated interaction program. These teachers interacted with Google Assistant in their daily lives. Both quantitative (English speaking proficiency tests) and qualitative data (semi-structured interviews) were collected and analyzed. The results revealed that Google Assistant significantly promoted BE teachers’ English-speaking proficiency. Most teachers confirmed that Google Assistant could establish an authentic environment that facilitates communication, increases English-speaking opportunities, enhances practice frequency, and provides scaffolding and immediate feedback, which contribute to their English speaking proficiency. Integrating Google Assistant into lesson preparation proved valuable, enabling teachers to access curriculum-related information efficiently and increasing their motivation to engage with IPAs.

Transforming equipment management in oil and gas with AI-Driven predictive maintenance

The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

Computer vision to enhance healthcare domain: An overview of features, implementation, and opportunities

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one of the growing modern AI technologies. The distribution and administration of medications are about to change by using CV for medication management. This system scans pharmaceutical labels and keeps track of the process from delivery to administration using cameras, sensors, and computer algorithms. In order to assure accuracy in medicine delivery and dose, the system also makes it easier for doctors, nurses, and chemists to communicate. The computer vision-driven medication management system can significantly lower the number of medical mistakes that result from inaccurate or missing prescriptions, improper doses, or simply forgetting to take a particular drug. An exhaustive literature review has been done to identify work related to the research objectives. This paper is about CV and their need in healthcare. Various tasks associated with CV in the healthcare domain are discussed. Targeted healthcare goals through CV traits are briefed. Finally, the significant applications of CVs in healthcare were identified and discussed. Nowadays, CV has practical uses in healthcare. Its methods are widely used since they have shown excellent utility in several medical contexts, including medical imaging and surgical planning. The CV is used to study how to program computers to comprehend digital pictures. Numerous medical applications utilise this technology, such as automated abnormality identification, illness diagnosis, and surgical procedure guiding. CV is expanding quickly and has enormous promise to enhance healthcare. Some of the many CV applications in the healthcare sector include patient identification systems, medical picture analysis, surgical simulation and illness diagnosis.

Transforming academic librarianship through AI reskilling: Insights from the GPT-4 exploration program

The emergence of Artificial Intelligence (AI) in academic librarianship necessitates a corresponding evolution in librarians' competencies. This case study examines the GPT-4 Exploration Program at the University of New Mexico's College of University Libraries and Learning Sciences, which aimed to foster a culture of continuous learning and innovation by providing hands-on experience with advanced AI technology. Drawing on Malcolm Knowles' Adult Learning Principles, the program's structure and implementation are analyzed, highlighting the diverse range of projects undertaken by participants. The study reveals that effective AI reskilling involves cultivating a culture of continuous learning, adaptability, and collaborative exploration, anchored in a practical, hands-on approach. Participants reported significant improvements in AI literacy and confidence in applying AI tools to their work. The program's success demonstrates the potential of well-structured reskilling initiatives in empowering library professionals to navigate the evolving landscape of AI in academic libraries. The case study offers insights and strategies for other academic libraries seeking to develop AI reskilling initiatives, emphasizing the importance of fostering a supportive learning environment, addressing technical and ethical challenges, and ensuring the long-term sustainability of such programs. The GPT-4 Exploration Program serves as a blueprint for integrating AI into library services and operations.

Criteria for journalistic quality in the use of artificial intelligence

This study aims to assess the persistence of traditional journalistic quality features within the contemporary digital ecosystem. Digital advancements have fundamentally altered media creation, notably with the emergence of artificial intelligence (AI) in newsrooms. AI holds potential for substantial shifts in journalism, impacting business models, dissemination methods, and professional practices. Present in 75% of newsrooms, AI streamlines tasks, allowing more creative time for journalists. Ethical and quality concerns persist, particularly regarding AI’s ability to meet journalism’s established quality standards. This article aims to investigate the incorporation of these quality criteria in news articles generated by artificial intelligence (AI). Focus group and in-depth interviews were used as methodological techniques, involving ten experts. It is concluded that journalistic ethics have remained intact despite the disruptive technological advances in recent decades. However, there is a need to integrate these ethics with new criteria associated with the tools being used. Therefore, it is necessary to consider criteria from a dual perspective: both social and technological.