Detecting body dysmorphic disorder in the age of algorithms

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Introduction: Body dysmorphic disorder (BDD) is a psychiatric disorder that is characterized by a preoccupation with an imagined or slight defect. It is an underrecognized part yet relatively common and is considered a severe mental disorder that occurs globally. BDD is more common than is realized and causes significant distress and occupational impairment. Hence, it is important to understand the magnitude of occupational impairment in college students with BDD. Before identifying the occupational impairment in college-going students, we need to understand the prevalence of BDD amongst them. Aim: To measure the prevalence of BDD in undergraduate Medical and Allied Health college-going students. Settings and Design: Cross-sectional quantitative study design was used to conduct the study. Medical and Allied Health Students of Goa Medical College, Goa. Methods and Material: A convenient sampling method was used. A self-report, body image questionnaire was used, which has a sensitivity of 95.1% and a specificity of 71.6%. Statistical Analysis used: Data obtained and entered in a Google Excel sheet and analyzed in IBM Statistical Package for Social Sciences version 24.0 (SPSS, Inc., Chicago, IL, USA). Results: Out of the 378 students surveyed, 72.5% were female. Among the student population, 6.9% were found to have BDD. The male-to-female ratio among those with BDD was 1:5. Additionally, it was observed that approximately 61.5% of students who were under 21 years of age were likely to have BDD. Furthermore, it was noted that 45.8% of the total students surveyed experienced slight distress due to their physical features, while approximately 47.1% of students reported that they always avoided certain situations or activities because their features caused them distress. Additionally, 44.4% of the total student population expressed being slightly preoccupied with their physical features and thinking about them frequently. Moreover, approximately 25.1% of the overall student population stated that their features slightly affected their ability to work or study, indicating interference with their academic or professional pursuits. Conclusion: The data suggests that a significant portion of the surveyed population, particularly females, are susceptible to BDD. The disorder appears to affect a substantial percentage of students, with a higher prevalence among those under 21 years of age. Additionally, a considerable number of students report experiencing distress, avoidance behaviors, preoccupation with their physical features, and interference with their work or study due to these concerns. These findings highlight the importance of addressing mental health and body image issues among students and may warrant further investigation and support for those affected by BDD.

Selfies are self-taken images of people that can reflect human feelings and can disseminate different messages. It is thought that technology has played a big part in the evolution of what is termed nowadays “selfie addiction”. The goal of this study is to examine the prevalence and correlation of Selfie Addiction and Body Dysmorphic Disorder (BDD) using two scales – the Psychometric scale for selfie addiction and the Body Dysmorphic Disorder Questionnaire (BDDQ). This study examines these two phenomena amongst preclinical medical students of King Abdulaziz University (KAU), Jeddah, Saudi Arabia.This was across-sectional research, which involved 317 preclinical medical students from King Abdulaziz University (KAU), Jeddah, Saudi Arabia. The psychometric scale for selfie addiction is a Likert Scale with ten statements, while the BDDQ constitutes of four statements. The data was collected between 16/2/2021 and 31/10/2021. Ethical approval and informed consent were obtained at the beginning of the study.There were a total of 317 responses. The overall prevalence of selfie addiction was 13.88% with females having higher rates than males (22.5% versus 11.4%), and more senior students having a higher prevalence than their junior colleagues (19.5% versus 7.8%). Also, being single was associated with higher scores of (14.0%) compared to the married group (0%). The average score for selfie addiction was 18.66 which is below the cutoff score of 30. The prevalence of BDD was (5.67%), and the average score of BDDQ was 1.03 with a range of 0-4 and standard deviation of 1.3. There was no significant correlation between BDD and demographic variables. There was a very weak correlation between BDD and selfie addiction; the Pearson Correlation value was (r = 0.144, p= 0.01). However, a Chi square comparing those who were addicted to selfies versus not (based on the cutoff score) showed a significant difference in BDD scores. (p=0.044) indicating possible correlation. Results show that the prevalence of selfie addiction was 13.88% which seems significant. BDD had a lower prevalence of 5.67%. Factors related to higher rates of selfie addiction included being a more senior student, being single and female. In contrast, these demographic factors did not seem to be related to BDD. Finally, there was a weak correlation between BDD and selfie addiction. More research is needed to study the correlation between the two domains in different populations.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

Body dysmorphic disorder (BDD) is a preoccupation with a slight or imagined flaw in appearance that causes significant distress and impairment in daily functioning. The disease is more prevalent among patients who seek aesthetic procedures as compared with population standards or individuals that are not interested in aesthetic surgery. Several studies have indicated that BDD symptoms typically worsen after an aesthetic procedure because the preoccupation shifts to a different body area. This review discusses the demographic and clinical features, psychiatric comorbidity, assessment, differential diagnosis, and management of BDD. Components of the assessment include the interview, patient observation in the office, and questionnaires. The article includes a detailed discussion on questionnaires, especially those that are most useful in the dermatology or cosmetic practice. Ethical considerations in the management of BDD are discussed. BDD should not be missed by health providers because of the associated high morbidity that includes an increased suicidality. The cosmetic provider's approach should motivate BDD patients to participate in treatment, a combination of psychotherapy and pharmacotherapy.

Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources from PubMed/MEDLINE, IEEE Xplore, arXiv, Web of Science, and Scopus, covering publications up to 14 February 2025. AI models have demonstrated potential in triage, enabling rapid differentiation of appendicitis from other causes of abdominal pain. In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. These tools also predict disease severity, aiding decisions between conservative management and surgery. Radiomics further enhances diagnostic precision by analyzing imaging data. Intraoperatively, AI applications are emerging to support real-time decision-making, assess procedural steps, and improve surgical training. Postoperatively, ML models predict complications such as abscess formation and sepsis, facilitating early interventions and personalized recovery plans. This is the first comprehensive review to examine AI's role across the entire appendicitis treatment process, including triage, diagnosis, severity prediction, intraoperative assistance, and postoperative prognosis. Despite its potential, challenges remain regarding data quality, model interpretability, ethical considerations, and clinical integration. Future efforts should focus on developing end-to-end AI-assisted workflows that enhance diagnosis, treatment, and patient outcomes while ensuring equitable access and clinician oversight.

Body dysmorphic disorder (BDD) is a mental health disorder characterized by an excessive preoccupation with perceived flaws or defects in one's appearance. This study aimed to assess the prevalence of BDD, identify the predictors of BDD, explore the associations of BDD with social anxiety and social phobia, and examine the effect of social media on BDD. In this cross-sectional study that was conducted between January and March 2023, Lebanese adult females completed an online survey that included questions about sociodemographic characteristics, cosmetic interventions, BDD, social media use, and Social Interaction Anxiety Scale (SISA-6)/Social Phobia Scale (SPS-6). The collected information was analyzed using descriptive statistics and regression analysis. Statistical significance was set at p value <0.05. A sample of 1048 were eligible for analysis and the mean age was 29.94 ± 10.01 years. It was shown that 141 (13.5%) participants were BDD positive. The results of the multivariable logistic regression showed that only females living in urban areas (aOR = 2.351 [95% CI 1.502-3.680], p < 0.001), unmarried (aOR = 1.672 [95% CI 1.041-2.684], p = 0.033), reporting higher social media use score (aOR = 1.526 (95% CI 1.401-1.661), p < 0.001), and higher SIAS/SPS score (aOR = 1.040 (95% CI 1.022-1.060), p < 0.001) were significantly positively associated with BDD. This study found a high BDD prevalence (13.5%) among Lebanese adult females compared with other regions and was associated with anxiety and increased social media platforms use. Thishighlights the need for a comprehensive approach in the assessment of BDD, along with preventive measures to address the negative impacts of social media on well-being and promote healthy body image.

Background: Artificial intelligence (AI) is influencing pediatric dentistry by supporting diagnostic accuracy, optimizing treatment planning, and improving patient care, especially for children with special needs. Previous studies explored various aspects of AI in pediatric dentistry and special care dentistry, predominantly focusing on clinical implementation or technical advancements. However, no prior review has specifically addressed its application at the intersection of pediatric dentistry and special care dentistry, particularly with respect to ethical and environmental perspectives. Objective: This scoping review provides a comprehensive synthesis of AI technologies in pediatric dentistry with a dedicated focus on children with special health care needs. It aims to critically evaluate current applications and examine the clinical, ethical, and environmental implementation challenges unique to these populations. Methods: A structured literature search was conducted in PubMed, Scopus, and Web of Science from inception to August 2025, using predefined inclusion and exclusion criteria. Eligible studies investigated AI applications in pediatric dental care or special needs contexts. Studies were synthesized narratively according to thematic domains. Results: Sixty-five studies met the inclusion criteria. Thematic synthesis identified nine domains of AI application: (1) diagnostic imaging and caries detection, (2) three-dimensional imaging, (3) interceptive and preventive orthodontics, (4) chatbots and teledentistry, (5) decision support, patient engagement and predictive analytics, (6) pain assessment and discomfort monitoring, (7) behavior management, (8) behavior modeling, and (9) ethical considerations and challenges. The majority of studies were conducted in general pediatric populations, with relatively few specifically addressing children with special health care needs. Conclusions: AI in pediatric dentistry is most developed in diagnostic imaging and caries detection, while applications in teledentistry and predictive analytics remain emerging, and areas such as pain assessment, behavior management, and behavior modelling are still exploratory. Evidence for children with special health care needs is limited and seldom validated, highlighting the need for focused research in this group. Ethical deployment of AI in pediatric dentistry requires safeguarding data privacy, minimizing algorithmic bias, preventing overtreatment, and reducing the carbon footprint of cloud-based technologies.

This article dives into the ethical complexities of Artificial Intelligence (AI). It starts by laying the groundwork, defining both ethics and morality, and emphasizing how ethical principles can evolve over time.Next, it explores "applied ethics," which examines real-world dilemmas like genetic engineering, AI's impact on jobs, and environmental concerns. From there, it shifts its focus specifically to AI ethics, differentiating between two key aspects the Ethics of AI where broader framework considers ethical issues surrounding AI development, encompassing concerns like transparency, data security, privacy, and accountability and in Ethical AI where the concept focuses on building AI systems with the ability to make ethical choices. It emphasizes aligning these systems with ethical values, minimizing bias, and fostering user trust. The article delves deeper into specific ethical concerns in the AI landscape, such as transparency, data privacy, and issues of autonomy, intentionality, and responsibility. It explores their relevance and the challenges they pose for AI development. Furthermore, it examines the social implications of these ethical concerns. Key areas examined include automation and job displacement, equitable access to technology, and the potential impact of AI on democratic principles and civil liberties. The article highlights the potential consequences of AI-driven automation on employment, the need for inclusive digital access, and the importance of ethical considerations in governance and citizen rights amidst the growing influence of AI. The article serves as a comprehensive guide to understanding the ethical dimensions of AI. It covers fundamental ethical concepts, specific AI ethics considerations, the importance of building ethical AI systems, and the challenges and societal impacts of AI development. It's a valuable resource for anyone seeking to understand the ethical landscape surrounding Artificial Intelligence.

Artificial Intelligence (AI) already has, and is poised to have an even greater, profound impact on financial decision-making and team management. Its influence spans critical areas such as risk analysis, ethical considerations, and performance forecasting. AI enhances decision-making processes by rapidly analyzing vast datasets and uncovering insights that exceed human capabilities. However, this transformative potential brings significant ethical challenges, including algorithmic bias, transparency, and accountability. To ensure AI’s effective and responsible integration, robust ethical frameworks, human oversight, and collaboration with regulatory bodies are crucial. AI is also redefining industries by reshaping labor markets, accelerating automation, and transforming managerial roles. While AI drives efficiency and innovation, concerns around data protection, market stability, and workforce displacement persist. A hybrid approach, blending AI’s technological power with human creativity and empathy, is essential for sustainable and ethical adoption.

Artificial intelligence (AI) has the potential to transform medical informatics by supporting clinical decision-making, reducing diagnostic errors, and improving workflows and efficiency. However, successful integration of AI-based decision support systems depends on careful consideration of human-AI collaboration, trust, skill maintenance, and automation bias. This work proposes five central questions to guide future research in medical informatics and human-computer interface (HCI). We focus on AI-based clinical decision support systems, including computer vision algorithms for medical imaging (radiology, pathology), natural language processing for structured and unstructured electronic health record (EHR) data, and rule-based systems. Relevant data modalities include clinician-acquired images, EHR text, and increasingly, patient-generated content in telehealth contexts. We review existing evidence regarding diagnostic errors across specialties, the effectiveness and risks of AI tools in reducing perceptual and interpretive errors, and the human factors influencing diagnostic decision-making in AI-enabled contexts. We synthesize insights from medicine, cognitive science, and HCI to identify gaps in knowledge and propose five key questions for continued research. Diagnostic errors remain common across medicine, with AI offering potential to reduce both perceptual and interpretive errors. However, the impact of AI depends critically on how and when information is presented. Studies indicate that delayed or toggleable cues may outperform immediate ones, but attentional capture, overreliance, and bias remain significant risks. Explainable AI provides transparency but can also bias decisions. Long-term reliance on AI may erode clinician skills, particularly for trainees and in low-prevalence contexts. Historical failures of computer-aided diagnosis in mammography highlight these challenges. Effective AI integration requires human-centered and adaptive design. Five central research questions address: (1) what type and format of information AI should provide; (2) when information should be presented; (3) how explainable AI affects diagnostic decisions; (4) how AI influences automation bias and complacency; and (5) the risks of skill decay due to reliance on AI. Each question underscores the importance of balancing efficiency, accuracy, and clinician expertise while mitigating bias and skill degradation. AI holds promise for improving diagnostic accuracy and efficiency, but realizing its potential requires post-deployment evaluation, equitable access, clinician oversight, and targeted training. AI must complement, rather than replace, human expertise, ensuring safe, effective, and sustainable integration into diagnostic decision-making. Addressing these challenges proactively can maximize AI's potential across healthcare and other high-stakes domains.

Artificial intelligence (AI) is increasingly vital in e-commerce, especially investment management. AI can enhance decision-making processes, risk analysis, and trend forecasting. However, legal aspects must be considered to prevent abuses and ensure the legality and ethics of AI use in investment management. Data collection and analysis are crucial stages in investment management. Compliance with data protection legislation is essential when using AI for automated decision-making in investment processes. Ethical considerations must be considered as these decisions can impact individuals' financial interests. Therefore, it is crucial that such systems are transparent and adhere to ethical standards. Determining who is responsible for the outcomes of AI decisions made in investment management is also essential. Software developers, system operators, and financial advisors may use AI to support their decisions. However, using AI can make them vulnerable to cyberattacks and affect the security of financial operations. Therefore, it is crucial to develop systems with high data protection levels and check them for vulnerabilities. Investment management in e-commerce may have an international dimension, which raises questions about compliance with international standards and regulatory requirements in AI. Artificial intelligence in investment management in e-commerce offers numerous opportunities to improve process efficiency and accuracy. However, it is crucial to ensure that such applications comply with legal norms, ethical principles, and security requirements. Only this approach will maximise AI's benefits in investment management while ensuring the legality and ethics of technology use.

Although AI is a helpful tool for the evolving business environment, its uncontrolled and fast integration into business processes can bring legal and ethical challenges. This research paper delves into the legal and ethical considerations essential for effectively regulating AI in business environments. As AI systems increasingly influence decisionmaking processes and operational efficiencies, the need for comprehensive and forward-thinking regulation becomes imperative. Thus, in the aspects of ethics, algorithms are utilized to protect rights or prohibit discrimination and ensure the openness of AI’s decisions. This paper examines key legal issues such as data privacy, intellectual property rights, and liability. For example, while the General Data Protection Regulation (GDPR) provides a framework for data protection, it remains unclear how these provisions apply to AI's sophisticated data handling and automated decision-making processes.. Ethical considerations further complicate the regulatory landscape. AI systems can inadvertently perpetuate biases present in their training data, leading to discriminatory practices and fairness concerns. This paper explores the ethical implications of such biases and the need for transparency in AI algorithms to ensure that they operate in a manner consistent with societal values. To address these challenges, the paper proposes a regulatory approach. It advocates for the modernization of existing legal frameworks to better encompass AI-related issues, and the establishment of ethical standards to guide AI deployment. Indeed, this article will explore some legal and ethical aspects of using Artificial Intelligence in the business world. Ethical AI guidelines and policies will be discussed, annual checkers will be conducted, and ethical AI standards will be promoted. In addition, identifying potential opportunities and threats likely to emerge when AI is even more entrenched in organizational activities will be relevant. After reviewing this article, you should know how to harness AI's power while avoiding legal issues.

This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined.