Artificial Intelligence Expert Research Articles

Preserving manipulated and synthetic Deepfake detection through face texture naturalness

With the rapid development of deep learning and face recognition technology, AI(Artificial Intelligence) experts have rated Deepfake cheating as the top AI threat. It is difficult for the human eye to distinguish the fake face images generated by Deepfake. Therefore, it has become a popular tool for criminals to seek benefits. Deepfake can be mainly divided into two types, a manipulated Deepfake that falsifies images of others by targeting real faces, and a synthetic Deepfake using GAN to generate a new fake image. So far, seldom cybersecurity system is able to detect these two types simultaneously. In this article, we aim to propose a hybrid Deepfake detection mechanism (HDDM) based on face texture and naturalness degree. HDDM constructs a unique texture from a facial image based on CNN(Convolutional Neural Network) and builds a naturalness degree recognition model via DNN(Deep Neural Network) to help cheating detection. Experimental results have proved that HDDM possesses a sound effect and stability for synthetic and manipulated Deepfake attacks. In particular, the WildDeepfake simulation has demonstrated the possibility of applying HDDM to the real world.

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer.

We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Our AI model demonstrated a diagnostic performance equivalent to that of experts.

A short report on the current landscape of Artificial Intelligence (AI) in vascular surgery in Pakistan.

This study focuses on the current applications, potential, and challenges to Artificial Intelligence (AI) integration in vascular surgery with specific emphasis on its relevance in Pakistan. Despite the benefits of AI in vascular surgery, there is a substantial gap in its adoption in Pakistan compared to global standards. In our context with limited resources and a scarcity of vascular surgeons, AI can serve as a promising solution. It can enhance healthcare accessibility, improve diagnostic accuracy, and alleviate the workload on vascular surgeons. However, hurdles including the absence of a comprehensive vascular surgery database, a shortage of AI experts, and potential algorithmic biases pose significant challenges to AI implementation. Despite these obstacles, the study underscores the imperative for continued research, collaborative efforts, and investments to unlock the full potential of AI and elevate vascular healthcare standards in Pakistan.

Diagnostic accuracy of artificial intelligence versus dental experts in predicting endodontic outcomes: A systematic review

Abstract Introduction: In the rapidly evolving landscape of health care, artificial intelligence (AI) has emerged as a promising tool to enhance diagnostic accuracy across various medical disciplines. Within the realm of dentistry, one critical area of focus is endodontics, which involves the diagnosis and treatment of dental pulp diseases. This systematic review investigates the diagnostic precision of AI in contrast to dental experts when predicting endodontic outcomes. Materials and Methods: The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review was registered on the Open Science Framework database. A systematic search was performed of the literature on the application of AI versus dental experts in endodontics. The search was conducted on PubMed, Scopus, Web of Science, and OVID until December 2022. The QUADAS-2 tool was used to evaluate the risk of bias. Results: The initial search retrieved 972 records, of which eight articles were included in the study. The studies reported application of AI for working length determination, periapical lesions, pulp and tooth segmentation, apical periodontitis, vertical root fracture, and C-shaped canals. QUADAS-2 tool revealed a low risk of bias in five out of the eight included studies. Conclusion: AI models demonstrated a notable increase in accuracy and effectiveness in endodontic diagnosis and treatment planning. These results suggest that the integration of AI technology in endodontic diagnosis and treatment planning has immense potential to serve as a promising aid.

Medical doctor’s perception of artificial intelligence during the COVID-19 era: A mixed methods study

ABSTRACT Background: Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile. Objectives: The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education. Material and Methods: This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview. Results: The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others (P < 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic. Conclusion: Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified.

Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare

Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare

Comparing code-free deep learning models to expert-designed models for detecting retinal diseases from optical coherence tomography

BackgroundCode-free deep learning (CFDL) is a novel tool in artificial intelligence (AI). This study directly compared the discriminative performance of CFDL models designed by ophthalmologists without coding experience against bespoke models designed by AI experts in detecting retinal pathologies from optical coherence tomography (OCT) videos and fovea-centered images.MethodsUsing the same internal dataset of 1,173 OCT macular videos and fovea-centered images, model development was performed simultaneously but independently by an ophthalmology resident (CFDL models) and a postdoctoral researcher with expertise in AI (bespoke models). We designed a multi-class model to categorize video and fovea-centered images into five labels: normal retina, macular hole, epiretinal membrane, wet age-related macular degeneration and diabetic macular edema. We qualitatively compared point estimates of the performance metrics of the CFDL and bespoke models.ResultsFor videos, the CFDL model demonstrated excellent discriminative performance, even outperforming the bespoke models for some metrics: area under the precision-recall curve was 0.984 (vs. 0.901), precision and sensitivity were both 94.1% (vs. 94.2%) and accuracy was 94.1% (vs. 96.7%). The fovea-centered CFDL model overall performed better than video-based model and was as accurate as the best bespoke model.ConclusionThis comparative study demonstrated that code-free models created by clinicians without coding expertise perform as accurately as expert-designed bespoke models at classifying various retinal pathologies from OCT videos and images. CFDL represents a step forward towards the democratization of AI in medicine, although its numerous limitations must be carefully addressed to ensure its effective application in healthcare.

Clarify Artificial Intelligence (AI) decisions models rights in Intelectual Property (IP) system

The paper explores the relationships between Artificial Intelligence (AI) and Intellectual Property (IP) system of rights protection. The discussion clarify the characteristics of IP system, who is the registered owner, what is the registration object, and where the registration takes place. The research seeks WIPO's advice and the general trend of AI experts' discussions and tries to dig deep into definitions and meanings. The research also shows the mainstream explication why AIs not entitled as owner of an IP. In other case AI’s integrated into a process or a digital product a AI tool to solve a well-known problem, it is part of the organization's management and resources. Thus, innovation's certification belongs to the company or the public organization that sponsored it. The result of the research shows a summary framework of all the rights when used as a tool for decisions and risk of assuming AI as a system or as a model to support decisions specially for Public Administration.

Democratizing Artificial Intelligence in Anatomic Pathology.

Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

Educational Practices and the Use of Artificial Intelligence: A Multifaceted Analysis in the Current Context

Objetive: The research aims to examine current educational practices related to the understanding and utilization of artificial intelligence (AI) in the educational field. Theoretical framework: The research collects information related to the conceptual framework, detailing concepts involving the research variables; likewise, a compilation of background research from similar studies in the years 2022 and 2023 is conducted. Method: The research employs a mixed methodology, incorporating qualitative and quantitative data collection methods. A comprehensive review of academic literature and official documents is conducted to identify prevalent educational practices in the field of AI. Additionally, surveys and interviews are designed targeting teachers, students, and AI experts to gather detailed information about their experiences, knowledge, and perceptions regarding AI in education. Results and conclusion: The results of the study reveal a diverse range of educational practices related to AI. These practices include the use of AI tools for personalized learning and the integration of AI concepts into the school curriculum. Common challenges such as the lack of resources and adequate training for teachers, as well as ethical and privacy concerns surrounding AI use in the classroom, are identified. Research implications: The findings of this research have significant implications for educational practices related to AI. They underscore the need for enhanced teacher training, the development of ethical guidelines, and the promotion of digital literacy in educational settings. Additionally, the study highlights the importance of addressing challenges such as resource constraints and privacy concerns to facilitate the effective integration of AI into education. Originality/Value: This research contributes to the existing literature by providing insights into current educational practices regarding AI. By employing a mixed methodology approach, the study offers a comprehensive understanding of the subject, identifying common practices and challenges. Moreover, the research suggests practical recommendations for promoting the responsible use of AI in education and calls for further exploration of innovative approaches in this field.

The role of foreign MNEs in China’s twin transition: a study on the organization of green and digital innovation processes

PurposeThe purpose of this study is to shed light on the twin transition in China in the organization of innovation processes in artificial intelligence (AI) and green technology (GT) development and to understand the role of foreign multinationals in Chinese innovation systems.Design/methodology/approachA qualitative research approach is used by interviewing executives from German multinationals with expertise in AI and GT development and organization of innovation processes in China. In total, 11 semi-structured interviews were conducted with companies, and the data were analysed with a thematic qualitative text analysis.FindingsThe findings show that AI applications for GT are primarily developed in cross-company projects that are led by local and regional authorities through the organization of industrial districts and clusters. German multinationals are either being integrated, remaining autonomous or being excluded from these twin transition innovation processes.Originality/valueThis paper aims to fill the gap in the literature by providing one of the first qualitative approach towards twin transition innovation processes in China and exploring the integration of multinational enterprises in cluster organizations. To the best of the author’s knowledge, this is one of the first twin transition studies from this perspective in emerging economies.

A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

A common data model for the standardization of intensive care unit medication features.

Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

The obscure politics of artificial intelligence: a Marxian socio-technical critique of the AI alignment problem thesis

AbstractThere is a growing feeling that artificial intelligence (AI) is getting out of control. Many AI experts worldwide stress that great care must be taken on the so-called alignment problem, broadly understood as the challenge of developing AIs whose actions are in line with human values and goals. The story goes that ever more powerful AI systems are escaping human control and might soon operate in a manner that is no longer guided by human purposes. This is what we call the AI-out-of-control discourse which, in this paper, we critically examine and debunk. Drawing on complementary insights from political theory, socio-technical studies and Marxian political economy, we critique the supposed animistic and autonomous nature of AI, and the myth of the uncontrollability of AI. The problem is not that humanity has lost control over AI, but that only a minority of powerful stakeholders are controlling its creation and diffusion, through politically undemocratic processes of decision-making. In these terms, we reframe the alignment problem thesis with an emphasis on citizen engagement and public political participation. We shed light on the existing politics of AI and contemplate alternative political expressions whereby citizens steer AI development or stop it in the first place.

Revolutionizing Talent Acquisition in Indonesia's E-Commerce Industry: The Transformative Impact of AI and Machine Learning

This research explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) on talent acquisition within Indonesia's growing e-commerce sector. While acknowledging the transformative potential of these technologies, the study uncovers significant challenges in their integration into existing talent acquisition processes. These challenges include issues related to data quality, model accuracy, and system adaptability. The study emphasizes the difficulty of acquiring talent with expertise in AI and ML, given the increasing demand for skilled workers in the e-commerce boom. It explores strategies employed by companies to address this talent gap, such as upskilling existing staff and seeking external expertise. The research provides a nuanced understanding of AI and ML applications in the Indonesian e-commerce landscape, highlighting both benefits and obstacles. The insights derived from the study aim to offer actionable guidance for e-commerce firms, HR professionals, and researchers navigating the evolving landscape of AI, ML, and talent acquisition in Indonesia's digital marketplace.

Imaginaries of democratization and the value of open environmental data: Analysis of Microsoft's planetary computer

The proliferation of environmentally oriented programs within the tech industry, and the industry's coinciding efforts toward data and technology democratization, generate concerns about the status of environmental data within digital economy. While the accumulation of digital personal data has been a cornerstone of domination of the data analytics industry, many believe environmental data to be a source of “untapped potential.” The potential of environmental data, the argument goes, would benefit equally the digital economy, environmental sciences, and academic data and artificial intelligence experts. This article analyzes the proliferation of the rhetoric about open environmental data by focusing on Microsoft's Planetary Computer cloud computing program and computer vision experts who curate and use biodiversity data stored on Microsoft's servers. Through an analytical framework of sociotechnical imaginaries, the article draws connections between visions of future for environmental knowledge production and governance promoted by Microsoft and the work of computer vision experts intending to benefit from the potential of environmental data as machine learning training sets while at the same time helping environmental sciences. Although environmental data on the Planetary Computer is democratized, it nonetheless becomes a valued asset to data economy, but often with unintended consequences, such as enabling citizen science biodiversity data to be used by state surveillance apparatus. The article challenges the view that data's democratization is unproblematically serving environmental sciences by examining the consequences of imaginaries of democratization emerging from the data industry leaders and processes of nonmonetary valuation of environmental data by experts who curate these datasets.

Artificial intelligence development and dissemination impact on the sports industry labor market.

The objective of this study is to explore the impact of artificial intelligence (AI) development on the sports industry labor market, the ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles. It refers to the inductive approach in the spirit technological determinism theory. It is based on the literature review and written qualitative, semi-structured interviews (N = 14) with sports human resources, management, and technology professionals (purposive sampling). Analysis involved inductive coding and line-by-line analytics of the data. The labor market implications of AI in the sports industry are multifaceted. New job roles are likely to emerge, demanding a blend of AI expertise, data-analysis skills, and sports domain knowledge. Professionals in roles such as sports data analysts and marketing experts may find increasing opportunities. However, certain jobs undergo transformation as AI automates routine tasks. It requires individuals to upskill or transition to roles that require a deeper understanding of AI. This necessitates the creation of responsibilities focused on ethical AI governance and oversight. It is important to research the impact of AI dissemination on the sports industry labor market in a holistic manner because the effects of AI are complex and far-reaching. While there are potential benefits to the implementation of AI, there are also potential risks and challenges that need to be addressed, the implementation of AI in the sports industry could have broader social and ethical implications that need to be considered.

Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany

BackgroundNursing faces increasing pressure due to changing demographics and a shortage of skilled workers. Artificial intelligence (AI) offers an opportunity to relieve nurses and reduce pressure. The perception of AI by nurses is crucial for successful implementation. Due to a limited research state, our study aims to investigate nurses’ knowledge and perceptions of AI.MethodsIn June 2023, we conducted a cross-sectional online survey of nurses in Bavaria, Germany. A convenience sample via care facilities was used for the questionnaire oriented on existing AI surveys. Data analysis was performed descriptively, and we used a template analysis to evaluate free-text answers.Results114 (♀67.5 %, ♂32.5 %) nurses participated. Results show that knowledge about AI is limited, as only 25.2 % can be described as AI experts. German nurses strongly associate AI with (i) computers and hardware, (ii) programming-based software, (iii) a database tool, (iv) learning, and (v) making decisions. Two-thirds of nurses report AI as an opportunity. Concerns arise as AI is seen as uncontrollable or threat. Administration staff are seen as the biggest profiteers.ConclusionEven though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support. We suggest that nurses should be better prepared for AI in the future, e.g., through training and continuing education measures. Nurses are the working group that uses AI and are crucial for implementing nursing AI.

Can ChatGPT exceed humans in construction project risk management?

PurposeThe objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approachEmploying a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.FindingsChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/valueThis study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Capturing the full potential of Maltese language learning through ChatGPT

Chat Generative Pretrained Transformer (ChatGPT) is a state-of-the-art artificial intelligence (AI) language model developed by OpenAI. It employs advanced deep learning algorithms to generate text that mimics human language. Although ChatGPT has gained immense popularity in recent times, its significance is expected to rise further in shaping the future of technology and AI. This study investigates the potential of ChatGPT as a tool for enhancing the learning of Maltese for international adult students. The study involved 41 participants who embarked on a journey of exploration, employing ChatGPT for two weeks to support their Maltese learning. The study evaluated the effectiveness of ChatGPT through surveys and focus group discussions, which revealed that while ChatGPT was convenient and accessible, its ability to provide accurate responses to students' questions and support Maltese grammar, vocabulary, and conversational practice was limited. The participants expressed frustration with the ChatGPT's limitations in understanding and responding to Maltese language. The study emphasizes the need to fully unleash ChatGPT's potential by improving its training on Maltese language and collaborating with Maltese language and AI experts to better support Maltese language learning. The findings have important implications for the development of ChatGPT for less widely spoken languages like Maltese.