Artificial Intelligence Tools Research Articles

Analyzing postoperative complications in colorectal cancer surgery: a systematic review enhanced by artificial intelligence.

Colorectal cancer stands as a predominant cause of cancer-related mortality worldwide. Despite progressive strides in surgical methodologies, the specter of postoperative complications is very large, significantly impacting both morbidity and mortality rates. This review aims to meticulously examine existing scholarly works to gauge the prevalence, severity, and therapeutic approaches to postoperative complications arising from colorectal cancer surgeries. Employing a systematic approach, this study reviewed 135 peer-reviewed publications from the period of 2000-2023. The corpus was organized into categories reflective of the postoperative complications discussed: anastomotic leakage, port-site metastases, small bowel adhesions and obstructions, thrombosis, ileus, postoperative infections, urinary dysfunctions, and cardiovascular dysfunctions. Advanced artificial intelligence tools were leveraged for in-depth literature searches and semantic analyses to pinpoint research lacunae. The analysis revealed that anastomotic leakage and postoperative infections garnered the majority of academic focus, representing 35% and 25% of the studies, respectively. Conversely, port-site metastases and cardiovascular dysfunctions were less frequently examined, accounting for merely 5% and 3% of the literature. The reviewed studies indicate a disparity in the reported prevalence rates of each complication, oscillating between 3% and 20%. Furthermore, the review identified a dearth of evidence-based management protocols, underscored by a pronounced heterogeneity in treatment guidelines. The literature is replete with analyses on anastomotic leakage and postoperative infections; however, there exists a glaring scarcity of exhaustive research on other postoperative complications. This review emphasizes the pressing need for uniform treatment guidelines and spotlights areas in dire need of further research, aiming at the comprehensive enhancement of patient outcomes following colorectal cancer surgery.

Application and renovation evaluation of Dalian's industrial architectural heritage based on AHP and AIGC.

This paper takes the example of industrial architectural heritage in Dalian to explore design scheme generation methods based on generative artificial intelligence (AIGC). The study compares the design effects of three different tools using the Analytic Hierarchy Process (AHP). It first establishes the key indicator weights for the renovation of industrial architectural heritage, with the criterion layer weights as follows: building renovation 0.230, environmental landscape 0.223, economic benefits 0.190, and socio-cultural value 0.356. Among the goal layer weights, the highest weight is for the improvement of living quality at 0.129, followed by resident satisfaction at 0.096, and educational and display functions at 0.088, while the lowest is for renovation costs at only 0.035. The design schemes are generated using Stable Diffusion, Mid Journey, and Adobe Firefly tools, and evaluated using a weighted scoring method. The results show that Stable Diffusion excels in overall image control, Mid Journey demonstrates strong artistic effects, while Adobe Firefly stands out in generation efficiency and ease of use. In the overall score, Stable Diffusion leads the other two tools with scores of 6.1 and 6.3, respectively. Compared to traditional design processes, these tools significantly shorten the design workflow and cycle, improving design quality and efficiency while also providing rich creative inspiration. Overall, although current generative artificial intelligence tools still have limitations in understanding human emotions and cultural differences, with continuous technological iteration, this method is expected to play a larger role in the design field, offering more innovative solutions for the renovation of industrial architectural heritage.

A Study on evaluating the impact of Artificial Intelligence in HR Functions at Collabera Digital

Artificial Intelligence (AI) has quickly spread to a number of areas, including HR. The purpose of this study is to evaluate how AI has affected Collabera Digital Bangalore's HR operations. The research aims to comprehend the advantages, difficulties, and general impact of AI on HR procedures including hiring, on-boarding, performance management, and employee development through a thorough examination of AI tools and methods applied within the organisation. A mixed-approaches strategy will be used in the study, integrating quantitative and qualitative research methods. Interviews with HR specialists and staff members will yield qualitative data about their perspectives on AI and their experiences with it. To gauge the observable effects of implementing AI, quantitative data will be collected via surveys and performance indicators. The research's conclusions will offer insightful information to companies thinking about integrating AI into HR tasks, assisting them in comprehending the possible advantages, difficulties, and best practices for a successful integration.

Integrating predictive modeling and causal inference for advancing medical science

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Health Care Professionals' Experience of Using AI: Systematic Review With Narrative Synthesis.

There has been a substantial increase in the development of artificial intelligence (AI) tools for clinical decision support. Historically, these were mostly knowledge-based systems, but recent advances include non-knowledge-based systems using some form of machine learning. The ability of health care professionals to trust technology and understand how it benefits patients or improves care delivery is known to be important for their adoption of that technology. For non-knowledge-based AI tools for clinical decision support, these issues are poorly understood. The aim of this study is to qualitatively synthesize evidence on the experiences of health care professionals in routinely using non-knowledge-based AI tools to support their clinical decision-making. In June 2023, we searched 4 electronic databases, MEDLINE, Embase, CINAHL, and Web of Science, with no language or date limit. We also contacted relevant experts and searched reference lists of the included studies. We included studies of any design that reported the experiences of health care professionals using non-knowledge-based systems for clinical decision support in their work settings. We completed double independent quality assessment for all included studies using the Mixed Methods Appraisal Tool. We used a theoretically informed thematic approach to synthesize the findings. After screening 7552 titles and 182 full-text articles, we included 25 studies conducted in 9 different countries. Most of the included studies were qualitative (n=13), and the remaining were quantitative (n=9) and mixed methods (n=3). Overall, we identified 7 themes: health care professionals' understanding of AI applications, level of trust and confidence in AI tools, judging the value added by AI, data availability and limitations of AI, time and competing priorities, concern about governance, and collaboration to facilitate the implementation and use of AI. The most frequently occurring are the first 3 themes. For example, many studies reported that health care professionals were concerned about not understanding the AI outputs or the rationale behind them. There were issues with confidence in the accuracy of the AI applications and their recommendations. Some health care professionals believed that AI provided added value and improved decision-making, and some reported that it only served as a confirmation of their clinical judgment, while others did not find it useful at all. Our review identified several important issues documented in various studies on health care professionals' use of AI tools in real-world health care settings. Opinions of health care professionals regarding the added value of AI tools for supporting clinical decision-making varied widely, and many professionals had concerns about their understanding of and trust in this technology. The findings of this review emphasize the need for concerted efforts to optimize the integration of AI tools in real-world health care settings. PROSPERO CRD42022336359; https://tinyurl.com/2yunvkmb.

Getting to know ChatGPT: How business students feel, what they think about personal morality, and how their academic outcomes affect Oman's higher education

The present study develops an integrative framework that investigates the relationship between the acceptability of ChatGPT among business students and their attitude, intention to adopt, and performance. This is achieved by examining the moderating role of business students' moral values and religious ethics. Using data collected from 312 university business students in Oman, we show that perceived usefulness (PU), perceived ease of use (PEOU), and perceived convenience (PC) have a positive effect on their attitudes toward ChatGPT. Business students' attitudes toward ChatGPT have a strong positive influence on their adoption intentions. Notably, university business students’ ChatGPT adoption intentions increased their academic performance. Remarkably, business students' Personal Morality and religion-related ethics trigger them to experience regret or a sense of responsibility for their actions that violate academic integrity or ethical standards in their studies. Establishing explicit ethical standards and procedures for the usage of artificial intelligence (AI) tools such as ChatGPT in educational settings is vital for higher educational institutions (HEIs). This research adds to the theoretical intervention of investigating how personal morality and religion-related ethics interact with AI tools such as ChatGPT, which can add to ethical decision-making theories. The current study has significant implications for theory as well as for practice.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

Let’s chat! Integrating ChatGPT in medical student assignments to enhance critical analysis

What is the educational challenge?: Advances in artificial intelligence (AI) are overtaking the progress in teaching and assessment. Traditional assignments, such as written reports and posters, have become obsolete as these can be created almost entirely using AI tools. As educators, it is imperative that we devise out-of-the-box assignments to give students the opportunity to demonstrate their understanding, ability to synthesize and think critically, while promoting the ethical and innovative use of AI. What are the proposed solutions?: In place of a traditional literature review essay, our junior medical students were given a structured assignment that required the use of ChatGPT to write a report, a critical analysis of the output, and the creation of a final report using their own research combined with the AI output. How was the solution implemented?: Students were provided with clear guidelines in writing and in the form of an in-person presentation, in addition to guidance during the process of the assignment. What lessons were learned that are relevant to a wider audience?: Using ChatGPT as part of constructing an essay and critically analyzing its output was a beneficial experience for the students, allowing them to recognize the utility and limitations of the widely available version of the AI platform. It also provided them with a sense of openness to new technologies that will support their academic journey. This structure could be generalized for use in a wider scope, to promote the effective and ethical use of AI. What are the next steps?: Implementation of such strategies to develop various types of assessments involving student coursework is at the center of revamping our medical school’s assessments. This successful endeavor represents the spark of innovation at our school which is constantly evolving to address the ever-changing needs of our medical programs.

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

Radiology, as a highly technical and information-rich medical specialty, is well-suited for artificial intelligence (AI) product development, and many FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end-user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.

Identifying AI-Generated Research Papers: Methods and Considerations

Recent advancements in natural language generation (NLG) have revolutionized content creation, enabling artificial intelligence (AI) tools to produce coherent and seemingly authentic texts, including scholarly papers. While AI-generated content offers efficiencies in speed and volume, concerns over authenticity, ethical implications, and academic integrity persist. This review explores methods and considerations for identifying AI-generated research papers, emphasizing the need to distinguish between human-authored and AI-generated content to uphold scholarly standards and ensure transparency in research. Key detection techniques include textual analysis, metadata examination, and content evaluation. Ethical concerns regarding AI's role in research are also discussed, underscoring the importance of ongoing research to refine identification methodologies and maintain research integrity.

Navigating the AI Frontier: A guide for ethical academic writing

The rapid integration of artificial intelligence (AI) into various sectors has raised significant ethical concerns, particularly in academic writing. This article examines the impact of generative AI (GAI) tools, such as ChatGPT, on the academic writing process. While these tools offer the potential to enhance efficiency and creativity, they also present challenges related to authorship, plagiarism, and academic integrity. The article explores key concerns, including privacy, validity, and bias, associated with GAI tools. It further provides guidelines for the ethical use of AI in academic manuscripts, emphasizing the importance of transparency and the careful documentation of AI contributions. By adhering to these ethical principles, scholars can harness AI's capabilities to advance academic research while maintaining the integrity of scholarly publishing.

Choosing the best artificial intelligence tools for human resource management

The article presents a comprehensive review and comparative analysis of modern artificial intelligence (AI)-based platforms used in human resource (HR) management. Tools such as HireVue, Pymetrics, Eightfold.ai, Workday, and Textio are reviewed. Their functionality, usability, integration with existing systems, data security, cost, and user support are discussed. The article provides practical recommendations for selecting optimal AI solutions, taking into account the specific needs and priorities of different organizations. It also explores the prospective directions of AI development in HR and the ethical aspects of their implementation.

Using Artificial Intelligence tools to create a smart university to employ digital transformation in the sustainability of higher education development Applied study, White Nile University - Sudan, 2023

The study aimed to introduce artificial intelligence and design a smart technology educational environment that works to develop digital capabilities and smart digitization in universities by using artificial intelligence methods to achieve the maximum level of education according to different capabilities and skills, raise the value of higher education and improve the quality of education. The problem of the study lies in the presence of real challenges in The field of education, due to the lack of interest in developing innovative tools to meet the needs of society. The study relied on the descriptive analytical approach to define and analyze the conceptual framework for building a smart university that has all the elements of development and electronic renaissance. The study reached the most important results, including the impact of digital transformations on smart universities, and digital transformation achieves many goals. For students and faculty members, digital transformation helps faculty members cope with the increasing number of students, provides thousands of educational sites, and the most important recommendations of the study are preparing an integrated vision between the components of digital transformation and applying it to the smart university, providing the necessary insurance and security of information in the smart digital environment.

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium.

The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

The Role of Artificial Intelligence (AI) and machine learning in social work practice

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.

AI and Thinkable-Assisted learning media for physical education: a descriptive study on collaborative lecturer education

This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills. This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills.

Evolving Artificial Intelligence (AI) at the Crossroads: Potentiating Productive vs. Declining Disruptive Cancer Research.

Artificial intelligence (AI), encompassing several tools and platforms such as artificial "general" intelligence (AGI) and generative artificial intelligence (GenAI), has facilitated cancer research, enhancing productivity in terms of research publications and translational value for cancer patients. AGI tools, such as ChatGPT, assist preclinical and clinical scientists in identifying tumor heterogeneity, predicting therapy outcomes, and streamlining research publications. However, this perspective review also explores the potential of AI's influence on cancer research with regard to its impact on disruptive sciences and discoveries by preclinical and clinical scientists. The increasing reliance on AI tools may compromise biological intelligence, disrupting abstraction, creativity, and critical thinking. This could contribute to the declining trend of disruptive sciences, hindering landmark discoveries and innovations. This perspective review narrates the role of different forms of AI in the potentiation of productive cancer research and the potential disruption of disruptive sciences due to AI's influence.

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

From Real-World Data to Causally Interpretable Models: A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer.

Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009-2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient's risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.

An Artificial Intelligence-assisted Diagnostic System Improves Upper Urine Tract Cytology Diagnosis.

To evaluate efficacy of the AIxURO system, a deep learning-based artificial intelligence (AI) tool, in enhancing the accuracy and reliability of urine cytology for diagnosing upper urinary tract cancers. One hundred and eighty-five cytology samples of upper urine tract were collected and categorized according to The Paris System for Reporting Urinary Cytology (TPS), yielding 168 negative for High-Grade Urothelial Carcinoma (NHGUC), 14 atypical urothelial cells (AUC), 2 suspicious for high-grade urothelial carcinoma (SHGUC), and 1 high-grade urothelial carcinoma (HGUC). The AIxURO system, trained on annotated cytology images, was employed to analyze these samples. Independent assessments by a cytotechnologist and a cytopathologist were conducted to validate the initial AIxURO assessment. AIxURO identified discrepancies in 37 of the 185 cases, resulting in a 20% discrepancy rate. The cytotechnologist achieved an accuracy of 85% for NHGUC and 21.4% for AUC, whereas the cytopathologist attained accuracies of 95% for NHGUC and 85.7% for AUC. The cytotechnologist exhibited overcall rates of roughly 15% and undercall rates of greater than 50%, while the cytopathologist showed profoundly lower miscall rates from both undercall and overcall. AIxURO significantly enhanced diagnostic accuracy and consistency, particularly in complex cases involving atypical cells. AIxURO can improve the accuracy and reliability of cytology diagnosis for upper urine tract urothelial carcinomas by providing precise detection on atypical urothelial cells and reducing subjectivity in assessments. The integration of AIxURO into clinical practice can significantly ameliorate diagnostic outcomes, highlighting the synergistic potential of AI technology and human expertise in cytology.