Artificial Intelligence in Support of Web Development

BackgroundThere has been a resurgence of Artificial Intelligence (AI) on a global scale in recent times, resulting in the development of cutting-edge AI solutions within hospitals. However, this has also led to the creation of isolated AI solutions that are not integrated into clinical IT. To tackle this issue, a clinical Artificial Intelligence (AI) platform that handles the entire development cycle of clinical AI models and is integrated into clinical IT is required. This research investigates the integration of a clinical AI platform into the clinical IT infrastructure. This is demonstrated by outlining the stages of the AI model development cycle within the clinical IT infrastructure, illustrating the interaction between different IT system landscapes within the hospital with BPMN diagrams.MethodsInitially, a thorough analysis of the requirements is conducted to refine the necessary aspects of the clinical AI platform with consideration of the individual aspects of clinical IT. Subsequently, processes representing the entire development cycle of an AI model are identified. To facilitate the architecture of the AI platform, BPMN diagrams of all the identified processes are created. Clinical use cases are used to evaluate the processes using the FEDS framework.ResultsOur BPMN process diagrams cover the entire development cycle of a clinical AI model within the clinical IT. The processes involved are Data Selection, Data Annotation, On-site Training and Testing, and Inference, with distinctions between (Semi-Automated) Batch Inference and Real-Time Inference. Three clinical use cases were assessed to evaluate the processes and demonstrate that this approach covers a wide range of clinical AI use cases.ConclusionsThe evaluations were executed successfully, which indicate the comprehensive nature of our approach. The results have shown that different clinical AI use cases are covered by the BPMN diagrams. Our clinical AI platform is ideally suited for the local development of AI models within clinical IT. This approach provides a basis for further developments, e.g., enabling the training and deployment of an AI model across multiple sites or the integration of security- and privacy-related aspects.

Despite their promises, digital innovations have scarcely translated to technologies used in routine clinical practice, making the identification of barriers to successful implementation a research priority. Low levels of transdisciplinary skills represent such a barrier but so far, this has not been evaluated and compared between information technology (IT) and health care specialists. In this study, we evaluated the level of digital health literacy among IT and health care specialists. An anonymous questionnaire was distributed to staff at a breast cancer unit and an IT department of two German universities in December 2020. The survey questionnaire consisted of the previously validated eHealth Literacy Assessment Toolkit and additional questions with respect to age, profession, and career stage. Mann-Whitney or Wilcoxon rank-sum tests and two-sample chi-square tests were used for the analysis. The survey was completed by 113 individuals: 70 (61.9%) IT specialists and 43 (38.1%) health care specialists. Health care specialists scored significantly higher on the health-related scales and IT specialists scored significantly higher on the digitally related scales. No single participant identified themselves to have the highest level of literacy on all survey questions (n = 0 of 113; 0%). Only one person (n = 1 of 113; 0.9%) consistently reported a high or the highest level of literacy. Although IT and health care specialists showed great literacy in their respective disciplines, only few individuals combined both digital and health care literacy. Multidisciplinary teams and transdisciplinary curricula are crucial to bridge skill gaps between disciplines and to drive the implementation of digital health initiatives.

The paper presents the results of the research study conducted on students at the Faculty of Humanities and Social Sciences in Zagreb (FHSS), Croatia, at the end of 2024, focusing on their experiences with artificial intelligence (AI) tools, and its use in academic activities, students' trust in AI, and the future use of AI by students. While over 81 % of respondents reported using AI tools for study purposes, more than 89% did not receive any education about AI and expressed a need for such education. Generative AI tools for text creation are the most frequently used type of AI tools, but students still require additional knowledge to use them effectively. AI tools are also utilized for seeking advice, finding information, being available 24/7, and assisting in writing student papers, though not yet on a massive scale. Teachers remain the most trusted source for information and for recommending AI tools. Despite known shortcomings of AI tools such as content errors, potential costs and ethical or privacy concerns, students expressed their intention to continue using AI tools in the future.

Background Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of ChatGPT-4o and Google Gemini in addressing commonly asked questions about BCMs. Methods Thirty questions, derived from the American College of Obstetrics and Gynecologists (ACOG) website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a five-point rubric assessing Relevance, Completeness, and Lack of False Information (RCL). Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon Signed-Rank test, Friedman test, and Kruskal-Wallis test, was performed to compare metrics. Results ChatGPT-4o and Google Gemini provided high-quality responses to birth control-related queries, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, both categorized as "very good" to "excellent." ChatGPT-4o demonstrated higher scores in the lack of false information, based on descriptive statistics (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini outperformed in relevance, with a statistically significant difference (4.53 ± 0.57 vs. 4.30 ± 0.70, p = 0.035, large effect size). Completeness scores were comparable (p = 0.655). Statistical analyses revealed no significant differences in overall performance (p = 0.548), though Google Gemini demonstrated a potential trend of stronger performance in the "Other Topics" category. Within-model variability showed ChatGPT-4o had more pronounced differences among metrics (moderate effect size, Kendall's W = 0.357), while Google Gemini exhibited smaller variability (Kendall's W = 0.165). These findings suggest that both platforms offer reliable and complementary tools for addressing knowledge gaps in contraception, with nuanced strengths that warrant further exploration. Conclusions ChatGPT-4o and Google Gemini provided reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools, in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

Background Histological remission is an important target for Ulcerative Colitis (UC) treatment; however, scoring of histological images is time-consuming and prone to inter and intra-observer variability. Thus, a need exists for an accurate, reproducible, and reliable automated method. Previously, we demonstrated an Artificial Intelligence (AI) Tool using image processing and machine learning algorithms to measure histological disease activity using the Nancy index consistently and accurately.1 Here, we aim to enhance the capabilities of the AI Tool, by adding substantially more population-diversified training data while maintaining accuracy and robustness of results. Methods Eight global sites submitted 600 UC histological images. These were added to the 200 images previously used to train and validate the AI Tool. The 800-image dataset was divided into 2 groups: 90% used for training, 10% for testing. The novel AI algorithms were trained using state-of-the-art image processing and machine learning techniques based on deep learning and feature extraction. Cell and tissue regions of each training image were manually annotated, measured, and assigned a Nancy Index independently by 3 histopathologists, and used to further train the AI using over 43,000 characterisations. The AI Tool fully characterises histological images, identifying tissue types, cell types, cell numbers and locations, and automatically measures the Nancy Index for each image. Intra Class Correlation (ICC) and Confusion Matrix analyses were performed to evaluate the AI Tool and assess accuracy. Results The average ICC was 92.1% among the histopathologists and 91.1% between histopathologists and AI Tool, compared with 88.3% and 87.2% in the previous study.1 Confusion matrix analysis (Table 1) demonstrated the strongest correlation at the extremes of the Nancy Index, with 80% correlation between predicted and true labels for Nancy Scores of 0 or 4. When 2 adjacent scores were combined, correlations were stronger: 96% for a true Nancy score of 0 being predicted as 0 or 1, and 100% for a true Nancy score of 2 being predicted as 2 or 3. Conclusion By adding a larger number of images to the AI Tool training data, the robustness of the AI Tool was substantially improved while maintaining accuracy. The continued high correlation of AI Tool performance with the histopathologists reinforces the potential role for the AI Tool for IBD clinical applications. Fully characterising whole slides could standardise and validate an AI-driven scoring system for histology slides in IBD, eliminating the subjectivity of the human pathologist in assessment of disease activity.

Background: Artificial intelligence (AI) tools have demonstrated success in US medical licensing examinations; however, their utility in infection prevention and control (IPC) remains unknown. Methods: The program of hospital epidemiology handles consultation calls and records each question and answer. Using 2022 data, we selected 31 frequently asked questions. We utilized four AI tools, including Chat GPT-3.5 and 4.0, Bing AI, and OpenEvidence, to generate answers. We predefined scales (Table 1) to capture responses by three reviewers, including two hospital epidemiologists and one infection preventionist. The mean score of ≥ 3 and ≥ 4 was considered acceptable in accuracy and completeness, respectively. We reported the percentage of responses with acceptable accuracy and completeness out of assessed questions for each category. Results: Among 31 questions, 16 were associated with isolation duration, 9 with healthcare personnel (HCP) exposure, 4 with cleaning contaminated rooms, and 2 with patient exposure. Regarding accuracy, most AI tools performed worse in questions about isolation duration, ranging between 75% and 93.8%. All AI tools, except OpenEvidence, had a 100% accuracy rate for HCP and patient exposure. All AI tools had a 100% accuracy rate for contaminated room handling. The highest overall acceptable accuracy rate was observed in Chat GPT-3.5. Regarding completeness, most AI tools performed worse in questions about isolation duration, ranging between 44% and 75%. All AI tools, except OpenEvidence, had a 100% completeness rate for contaminated rooms and patient exposure. The highest overall acceptable completeness rate was observed in Bing AI (Table 2). Conclusions: All AI tools provided reasonable answers to commonly asked IPC-related questions, although, there were variations among different tools used. AI could be used to supplement the infection control program, especially if resources are limited.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

Researchers at INFN (National Institute for Nuclear Physics) face challenges from basic to hard science use cases (e.g., big-data latest generation experiments) in many areas: High Energy Physics (HEP), Astrophysics, Quantum Computing, Genomics, etc. Machine Learning (ML) adoption is ubiquitous in these areas, requiring researchers to solve problems related to the specificity of applications (e.g., tailored models and intricate domain knowledge), but also requiring solving general infrastructure-level and ML-workflow related problems. As the demand for ML solutions continues to rise across the diverse research domains, there exists a critical need for an innovative approach to accelerate ML adoption. In this regard we introduce an Artificial Intelligence (AI) platform designed as an application-agnostic Machine Learning as a Service (MLaaS) solution. The platform provides a paradigm shift by offering a flexible and generalized infrastructure that decouples the ML development process from the specific use cases. The AI platform is implemented as a software layer on top of our cloud platform: the INFN Cloud, a dedicated, geographically distributed infrastructure which offers composable, scalable, and open-source solutions. The AI platform leverages INFN Cloud resources and principles, gathering and orchestrating technologies to support end-to-end scalable ML solutions: Kubernetes, Kubeflow, KServe, KNative, Kueue, Horovod, etc., ensuring support for several ML frameworks: TensorFlow, PyTorch, Apache MXNet, XGBoost, etc. This paper describes the platform’s design and principles, as well as some selected use cases from Natural Language Processing and HEP domains that can take advantage of the “aaS” approach.

The integration of artificial intelligence (AI) tools in the workflow of scholarly writing, including scientific and medical writing, offers transformative advantages while raising critical ethical and practical concerns. Among the pros, AI substantially enhances efficiency by automating several time-consuming tasks such as literature review, data synthesis and text editing. In particular, tools such as ChatGPT, Grammarly, and SciSpace Copilot remarkably empower researchers when they draft, refine, and format manuscripts, increasing precision and speed. In addition, AI tools may foster inclusivity by assisting non-native English speakers with seamless translations and also enabling interdisciplinary collaboration, thereby hopefully democratizing access and boosting scientific communication and cooperation. However, the rapid adoption of novel AI tools brings significant challenges. First, there is a distinct risk of perpetuating biases in training datasets, and other key issues include ambiguity in authorship accountability and the potential erosion of critical thinking skills. In addition, AI tools could be purposefully misused to generate mock datasets and fraudulent papers (e.g. by paper mills), and this clearly poses a threat to academic integrity. This challenge is all too pressing given that traditional plagiarism detection tools often fall short against sophisticated AI-generated content. We hereby explore the pros and cons of AI on medical writing, poignantly leveraging Sergio Leone’s The Good, The Bad, and The Ugly movie as a metaphor. By providing general concepts as well as focusing on several key AI tools, we are hopeful this overview may prove useful to anyone wishing to conscientiously adopt AI tools for scientific writing. In addition, we make a compelling case for transparent guidelines, robust and freely available detection mechanisms, and ongoing critical oversight to ensure AI will be able to serve as a catalyst for innovation and dissemination without compromising the credibility of scholarly endeavors.

The rapid integration of Artificial Intelligence (AI) tools, such as ChatGPT, DeepSeek, etc., into academic and professional settings has raised important questions about their influence on human cognition. This study examines the relationship between reliance on AI tools and critical thinking skills, with a particular focus on cognitive load. A quantitative survey design was employed, using structured questionnaires to collect data from 350 university students across diverse disciplines. Measures assessed the extent of AI usage, perceived cognitive load, and levels of critical thinking. AI tool use and critical thinking while controlling for demographic and educational factors. Findings indicate that frequent use of AI tools is associated with significantly lower reported cognitive load during complex tasks (β = –0.37, p < .01) but also with slightly lower self-assessed critical thinking skills (β = –0.21, p < .05). Results suggest that while AI support can reduce mental effort, it may inadvertently discourage deeper engagement with information. Implications for education and workplace training include encouraging deliberate reflection and structured critical analysis when integrating AI tools.

The integration and usage of Artificial Intelligence (AI) tools in academic research writing has ushered in a paradigm shift, fundamentally transforming traditional research methods. AI tools have reshaped scholarly writing by enhancing accuracy, efficiency, and productivity. This study investigated the level of awareness, adoption, challenges, perceived benefits, and effectiveness of AI tools among research scholars from India and the United Arab Emirates (UAE), via a structured questionnaire administered to 174 research scholars across various academic disciplines in leading universities. The study identified various generative AI platforms used by scholars and explored key metrics, such as brainstorming, generation of ideas, grammar and style checking, literature discovery, paraphrasing, citation management, and plagiarism detection. The findings revealed a strong positive response related to awareness and adoption rates as well as significant differences based on geographic location, gender, academic discipline, and years of research experience. Despite acknowledging the perceived benefits and effectiveness of AI tools, concerns and challenges related to ethical usage, accuracy and bias, academic integrity, overdependence, and technical difficulties exist. The study provides various recommendations for AI use in academic writing, including the need for AI literacy training, institutional support, and policies related to ethical standards, and offers important implications for academic institutions, AI tools developers, research supervisors, and policy makers, fostering the effective integration of AI in academic research practices, following an ethical framework.

Purpose: Artificial Intelligence (AI) is rapidly transforming various sectors, including education, and significantly impacting students' academic experiences from school to college. This study explores how BAMS undergraduate students perceive the use of AI and AI-based tools for educational purposes and assess their prakriti. Method: This cross-sectional observational study was conducted among undergraduate BAMS students of Pt. Khushilal Sharma, Govt. Ayurveda College and Institute campus, Bhopal, Madhya Pradesh State, Central India, 200 undergraduate students aged 18 to 30. The collected data were analyzed using the software IBM SPSS Statistics Version 29.0.2.0(20). Data analysis used descriptive statistics (frequency, percentages, mean, standard deviation, etc.) as appropriate and inferential statistics (reliability, validity, and regression). Result: Among 200 undergraduate students, 33% were male and 67% were female. The mean age of students was 23.3±1.92(SD) years. 40% students belonged to Vatapittaja, 34% to Pittakaphaja, 22% to Kaphavataja, and 4% to Vatapitttakaphaja prakriti. The finding indicated that 78.5% of students were familiar with AI concepts. ChatGPT, Grammarly, and Google Assistant emerged as the most frequently used AI tools. Only 6.5% of students use AI tools daily for academic purposes. Most of the students were satisfied with using AI tools for study purposes. Conclusion: The surveyed students had little formal or informal experience with AI tools and limited awareness of AI's potential applications in education. However, they generally held positive opinions about ChatGPT, Grammarly, and AI and were hopeful about AI's future role in medical education and health care.

This article explores the concepts of information technologies and artificial intelligence. It delves into the history of artificial intelligence development and outlines its role in various stages of company operations. The article identifies the complexity of the employee recruitment, adaptation, and training processes and suggests that automation using information technologies and artificial intelligence can significantly enhance efficiency. The paper details how the effectiveness of recruitment, training, and adaptation processes can be improved by leveraging information technologies and artificial intelligence tools. It evaluates the advantages and disadvantages of employing these technologies in company operations. Artificial intelligence facilitates effective employee selection, performance evaluation, task adjustment, and assignment of training courses based on performance analysis. Moreover, its use minimizes subjectivity in dealing with potential employees, as artificial intelligence and information technologies enable the assessment of employee performance based on clear data. However, drawbacks include a tendency for errors in fundamental aspects and the technology's inherent imperfections, necessitating continuous supervision by management. Information technologies contribute to streamlining the personnel selection process, making it faster and simpler. Recruitment software aids in processing a larger number of candidates and automatically evaluating their knowledge levels. Additionally, training management systems help create a knowledge base that simplifies the employee training process. The methodology for implementing information technologies and artificial intelligence in the recruitment and training department is outlined, along with potential problems and solutions. It is concluded that artificial intelligence and information technologies have a substantial positive impact on the efficiency of employee recruitment, adaptation, and training processes and the overall effectiveness of the recruitment and training department.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.