Privacy Considerations Research Articles

Virtually possible: strategies for using telehealth in eating disorder treatment learned from the COVID-19 pandemic

ABSTRACT The COVID-19 pandemic resulted in an abrupt shift from in-person to virtual treatment, and clinicians continue to offer telehealth due to its advantages. Telehealth may be a viable, effective, and safe treatment modality for many clients with eating disorders. We consider contemporary issues regarding the use of telehealth in eating disorder treatment and identify strategies to enhance its delivery. First, we emphasize key factors when choosing therapy delivery (telehealth, in-person, or hybrid). Second, we address telehealth-specific planning, preparation, safety, and privacy considerations. Third, we discuss how eating disorder assessment and evidence-based interventions can be adapted for telehealth delivery. Fourth, we raise telehealth-specific challenges related to group-based delivery and the therapeutic alliance offering alternative avenues for connection and engagement. We conclude with a discussion of how additional research is needed to refine the presented strategies, develop new strategies, and assess their efficacy and effectiveness.

Leveraging Analytics and Big Data for Informed Decision-Making: A Comprehensive Review

Abstract–The advent of Big Data and advanced analytics technologies has revolutionized the way organizations collect, process, and utilize data to drive decision-making. This research paper provides a comprehensive review of the role of analytics and Big Data in contemporary business, highlighting their significance, applications, challenges, and future prospects. By examining various case studies and industry examples, we aim to illustrate how organizations across different sectors harness the power of analytics and Big Data to gain competitive advantages and address complex challenges. The paper also discusses the ethical and privacy considerations associated with the utilization of large-scale data analytics. Keywords: Big Data, Analytics, Decision-Making, Data Privacy, Data Warehousing, Business Intelligence, Predictive Analytics.

ChatGPT: ethical concerns and challenges in academics and research.

The emergence of artificial intelligence (AI) has presented several opportunities to ease human work. AI applications are available for almost every domain of life. A new technology, Chat Generative Pre-Trained Transformer (ChatGPT), was introduced by OpenAI in November 2022, and has become a topic of discussion across the world. ChatGPT-3 has brought many opportunities, as well as ethical and privacy considerations. ChatGPT is a large language model (LLM) which has been trained on the events that happened until 2021. The use of AI and its assisted technologies in scientific writing is against research and publication ethics. Therefore, policies and guidelines need to be developed over the use of such tools in scientific writing. The main objective of the present study was to highlight the use of AI and AI assisted technologies such as the ChatGPT and other chatbots in the scientific writing and in the research domain resulting in bias, spread of inaccurate information and plagiarism. Experiments were designed to test the accuracy of ChatGPT when used in research and academic writing. The information provided by ChatGPT was inaccurate and may have far-reaching implications in the field of medical science and engineering. Critical thinking should be encouraged among researchers to raise awareness about the associated privacy and ethical risks. Regulations for ethical and privacy concerns related to the use of ChatGPT in academics and research need to be developed.

Digital Health App to Address Disparate HIV Outcomes Among Black Women Living in Metro-Atlanta: Protocol for a Multiphase, Mixed Methods Pilot Feasibility Study.

Cisgender Black women in the southern United States are at heightened risk for HIV and adverse sexual and reproductive health outcomes. Mobile health interventions that target HIV risk while being adapted to the needs and lived experiences of Black women are remarkably limited. The study aims to refine SavvyHER, a mobile app for HIV prevention, with Black women residing in high HIV incidence areas of Georgia and evaluate the feasibility, acceptability, and usability of SavvyHER. This paper describes the procedures implemented to conduct this research. Community-based participatory research tenets guide this multiphase study to finalize the development of what we hypothesize will be an effective, sustainable, and culturally relevant HIV prevention and optimal sexual health and reproductive wellness app for Black women. This multiphased, mixed methods study consists of 3 phases. The first phase entails focus groups with Black women to understand their preferences for the functionality and design of a beta prototype version of SavvyHER. In the second phase, an app usability pretest (N=10) will be used to refine and optimize the SavvyHER app. The final phase will entail a pilot randomized controlled trial (N=60) to evaluate the app's feasibility and usability in preparation for a larger trial. Findings from preliminary focus groups revealed educational content, app aesthetics, privacy considerations, and marketing preferred by Black women, thus informing the first functional SavvyHER prototype. As we adapt and test the feasibility of SavvyHER, we hypothesize that the app will be an effective, sustainable, and culturally relevant HIV prevention, sexual health, and reproductive wellness tool for Black women. The findings from this research substantiate the importance of developing health interventions curated for and by Black women to address critical HIV disparities. The knowledge gained from this research can reduce HIV disparities among Black women through a targeted intervention that centers on their health needs and priorities. DERR1-10.2196/42712.

Outsourced and Privacy-Preserving Collaborative k-Prototype Clustering for Mixed Data via Additive Secret Sharing

Outsourced cloud computing can be considered as an effective way to overcome the data island among users and relieve the pressure of limited resources. However, due to the concerns about trust in cloud servers, outsourcing the users’ data and model training task has considerable privacy disclosure risks. This paper presents a PriKPM scheme by using additive secret sharing (ASS), so as to implement the privacy-preserving k-prototype clustering for mixed data (i.e., including numerical and categorical attributes). In PriKPM, data samples are randomly split into two shares and delivered offline to two collaborative servers. We design a secure initialization method for determining the location and number of cluster centers. Then, both servers securely calculate the mixed distance between samples and cluster centers, and execute the samples partion and cluster updating operations. An efficient and secure comparison protocol is developed to offer flexibly the “less than or equal” and “equal” functions during the entire clustering process. Furthermore, theoretical analysis proves the effectiveness and security of PriKPM. Sufficient experiments demonstrate that PriKPM is computationally more efficient than existing secure clustering works. PriKPM can achieve the approximate accuracy of the plaintext k-prototype clustering scheme.

Learning Analytics in Supporting Student Agency: A Systematic Review

Student agency, or agency for learning, refers to an individual’s ability to act and cause changes during the learning process. Recently, learning analytics (LA) has demonstrated its potential in promoting agency, as it enables students to take an active role in their learning process and supports the development of their self-regulatory skills. Despite the growing interest and potential for supporting student agency, there have yet to be any studies reviewing the extant works dealing with the use of LA in supporting student agency. We systematically reviewed the existing related works in eight major international databases and identified 15 articles. Analysis of these articles revealed that most of the studies aimed to investigate student or educators’ agency experiences, propose design principles for LA, and to a lesser extent, develop LA methods/dashboards to support agency. Of those studies developing LA, none initially explored student agency experiences and then utilized their findings to develop evidence-based LA methods and dashboards for supporting student agency. Moreover, we found that the included articles largely rely on descriptive and diagnostic analytics, paying less attention to predictive analytics and completely overlooking the potential of prescriptive learning analytics in supporting agency. Our findings also shed light on nine key design elements for effective LA support of student agency, including customization, decision-making support, consideration of transparency and privacy, and facilitation of co-design. Surprisingly, we found that no studies have considered the use of LA to support student agency in K–12 education, while higher education has been the focal point of the LA community. Finally, we highlighted the fields of study and data visualization types that the studies mostly targeted and, more importantly, identified eight crucial challenges facing LA in its support of student agency.

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.

Legal Considerations in Machine-Assisted Decision-Making: Planning and Building as a Case Study

The rise of automated decision-making in government poses both benefits and challenges. This article identifies and examines legal considerations relevant to governments and businesses in automating existing decision-making processes, focussing on planning permits and building approvals as a case study. It examines issues of transparency, algorithmic bias, privacy, data and intellectual property rights, as well as the implications of the use of generative Artificial Intelligence (AI). It also considers legal issues including whether decisions can be automated and if so, whether they are susceptible to judicial and administrative review; legal liability for damage caused by the use of AI in government decision-making; and the admissibility of AI-generated information. It is argued that although the use of AI provides significant benefits in terms of speed, efficiency and quality of decision-making, attention to the considerations of transparency, responsibility, privacy, liability and admissibility is required to minimise the risks of utilising AI systems.

Big Data and Innovative Research Methods

Big data and innovative research methods are two rapidly evolving trends that are transforming how we conduct research in sport management. Considering the natural relationship between social media, which is widely recognized as a major big-data source, and sport, this commentary centers on contemporary research method applied to social media data. In doing so, it discusses contemporary innovative techniques for social media data, focusing on exploring ways to access social media data, the natural language-processing techniques used, the challenges they address, the strengths and limitations of different techniques, and the ethical and privacy considerations associated with their use. Furthermore, the commentary demonstrates that using sentiment-analysis tools (e.g., Syuzhet, Bing, and AFFIN) is appropriate and efficient in analyzing sport’s social media data. Thus, a rigorous application of contemporary innovative techniques can significantly shape the future of sport management research. However, researchers must exercise caution when considering the source and preprocessing of the data prior to applying advanced analytical techniques.

Artificial intelligence in Physics Education: a comprehensive literature review

This paper presents a comprehensive literature review on the application of artificial intelligence (AI) in physics education. The study aims to explore the fundamental concepts of AI, its diverse applications in physics learning, and the benefits and challenges associated with its implementation. Through a systematic search of academic databases, a collection of relevant research articles, journals, conference papers, and books related to AI in physics education was obtained. The selected studies were analyzed and synthesized to develop a coherent framework for understanding the various ways AI is utilized in physics education, such as concept introduction, individualization, social interaction, and assessment. The findings underscore the positive impact of AI on enhancing conceptual understanding, providing personalized instruction, promoting social interaction, and improving assessment methods. However, challenges in terms of technical infrastructure, teacher training, data privacy, and ethical considerations were also identified. The paper concludes with recommendations for future research, addressing these challenges, and fostering effective implementation of AI technologies in physics education. This review provides valuable insights for educators, researchers, policymakers, and stakeholders seeking to leverage AI’s potential to revolutionize physics education.

From ChatGPT to Treatment: the Future of AI and Large Language Models in Surgical Oncology.

This paper explores the transformative potential of Large Language Models (LLMs) within the context of surgical oncology and outlines the foundational mechanisms behind these models. LLMs, such as GPT-4, have rapidly evolved in terms of scale and capabilities, with profound implications for their applications in healthcare. These models, rooted in the Generative Pretrained Transformer architecture, exhibit advanced natural language understanding and generation skills. Within surgical oncology, LLMs, when integrated into a Generalist Medical AI (GMAI) framework, hold great promise in offering real-time support throughout the cancer journey. However, alongside these opportunities, this paper underscores the importance of ethical, privacy, and efficacy considerations, especially in light of issues like data drift and potential biases. Collaborative efforts among healthcare providers, AI developers, and regulatory bodies are pivotal in ensuring responsible and effective use of LLMs in surgical oncology, thereby contributing to enhanced patient care and safety. As LLMs continue to advance, they are poised to become indispensable tools in the delivery of high-quality, efficient care in this specialized medical field.

Sustainable Development for Mobile Health Apps Using the Human-Centered Design Process.

Well-documented scientific evidence indicates that mobile health (mHealth) apps can improve the quality of life, relieve symptoms, and restore health for patients. In addition to improving patients' health outcomes, mHealth apps reduce health care use and the cost burdens associated with disease management. Currently, patients and health care providers have a wide variety of choices among commercially available mHealth apps. However, due to the high resource costs and low user adoption of mHealth apps, the cost-benefit relationship remains controversial. When compared to traditional expert-driven approaches, applying human-centered design (HCD) may result in more useable, acceptable, and effective mHealth apps. In this paper, we summarize current HCD practices in mHealth development studies and make recommendations to improve the sustainability of mHealth. These recommendations include consideration of factors regarding culture norms, iterative evaluations on HCD practice, use of novelty in mHealth app, and consideration of privacy and reliability across the entire HCD process. Additionally, we suggest a sociotechnical lens toward HCD practices to promote the sustainability of mHealth apps. Future research should consider standardizing the HCD practice to help mHealth researchers and developers avoid barriers associated with inadequate HCD practices.

A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity

This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is revolutionizing generative text. We provide a comprehensive analysis of its architecture, training data, and evaluation metrics and explore its advancements and enhancements over time. Additionally, we examine the capabilities and limitations of ChatGPT in natural language processing (NLP) tasks, including language translation, text summarization, and dialogue generation. Furthermore, we compare ChatGPT to other language generation models and discuss its applicability in various tasks. Our study also addresses the ethical and privacy considerations associated with ChatGPT and provides insights into mitigation strategies. Moreover, we investigate the role of ChatGPT in cyberattacks, highlighting potential security risks. Lastly, we showcase the diverse applications of ChatGPT in different industries and evaluate its performance across languages and domains. This paper offers a comprehensive exploration of ChatGPT’s impact on the NLP field.

The Techniques in Enhancing Bandwidth Load Balancing QoS at Local Area Network: A Review Paper

Bandwidth load balancing is a critical technique for improving the quality of service (QoS) in local area networks (LANs). Insufficient load balancing and QoS utilization can lead to network congestion, reduced performance, latency, packet loss, and an overall degradation of network performance. This paper presents an overview of load balancing techniques, load balancing algorithms, QoS parameters, load balancing strategies, and considerations for calculating bandwidth. The challenges of implementing load balancing algorithms and preferences for their implementation are discussed. Accurate network measurements and monitoring, coordination between network devices, security and privacy considerations, and careful evaluation of load balancing algorithms are among the challenges faced. The paper also discusses optimization and monitoring of bandwidth use, including options such as increasing bandwidth, bandwidth throttling, and data transfer throttling. The findings presented in this paper provide insights into the importance of load balancing and QoS in LANs and offer guidance for addressing related challenges.

Development of a Health Information System for Primary Care Dental Practice

The health information systems (HIS) have become increasingly important in dental practice as they offer benefits such as improved quality of care, increased efficiency, and reduced medical errors. The development of an HIS for primary care dental practice involves several steps, including requirements gathering, system design, implementation, and testing. Dental professionals and staff members must be consulted to identify their needs and preferences for HIS implementation. The system must be designed to be user-friendly and intuitive to ensure ease of use and efficiency. Successful implementation requires the proper consideration of data privacy and security measures. This literature review highlights the benefits of HIS in dental practice and provides real references to studies that have investigated the effectiveness of HIS implementation. The development of an HIS for primary care dental practice can improve patient care and outcomes and enhance the overall efficiency of dental practices.

Use of Artificial Intelligence in the Advancement of Breast Surgery and Implications for Breast Reconstruction: A Narrative Review.

Breast reconstruction is a pivotal part of the recuperation process following a mastectomy and aims to restore both the physical aesthetic and emotional well-being of breast cancer survivors. In recent years, artificial intelligence (AI) has emerged as a revolutionary technology across numerous medical disciplines. This narrative review of the current literature and evidence analysis explores the role of AI in the domain of breast reconstruction, outlining its potential to refine surgical procedures, enhance outcomes, and streamline decision making. A systematic search on Medline (via PubMed), Cochrane Library, Web of Science, Google Scholar, Clinical Trials, and Embase databases from January 1901 to June 2023 was conducted. By meticulously evaluating a selection of recent studies and engaging with inherent challenges and prospective trajectories, this review spotlights the promising role AI plays in advancing the techniques of breast reconstruction. However, issues concerning data quality, privacy, and ethical considerations pose hurdles to the seamless integration of AI in the medical field. The future research agenda comprises dataset standardization, AI algorithm refinement, and the implementation of prospective clinical trials and fosters cross-disciplinary partnerships. The fusion of AI with other emergent technologies like augmented reality and 3D printing could further propel progress in breast surgery.

Practical and ethical challenges of large language models in education: A systematic scoping review

AbstractEducational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks. Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks. Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring. What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation. A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks. Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts. Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks. The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved. Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education.

Incorporating privacy considerations into EU data-driven merger review

ABSTRACT In recent years, large digital companies have been gobbling up hundreds of smaller highly innovative firms involved in the collection and the processing of data. Some regulators, academics and practitioners have expressed concerns that Big Tech might use the increased market power and the greater concentration of consumers’ personal data stemming from data-driven mergers to harm consumers in the form of lower privacy protection. They wonder whether and to what extent the EU Commission and/or national competition authorities should take into account data protection considerations when reviewing transactions under the Merger Regulation and/or national merger control rules. The Commission’s decisional practice points towards three possible routes for integrating privacy concerns into competition analysis. Given their respective shortcomings, we explore the possibility of using the German Federal Cartel Office’s line of reasoning in its Facebook decision as a model for the incorporation of privacy considerations into EU data-driven merger analysis.

Real Time, Remote, and Recorded: Medical Student Experiences with Smart Glasses in Obstetrical Simulation.

The coronavirus disease 2019 pandemic generated the need for a teaching tool for enhancing remote education and evaluation of medical trainees. Smart glasses are being explored as a hands-free teaching tool for teleconferencing with hands-on demonstrations in addition to livestreaming capability. We wanted to understand the efficacy of such virtual teaching techniques in teaching procedural and surgical skills. Medical students in their Obstetrics and Gynecology clerkship at our medical school were recruited to participate in a virtual demonstration of normal vaginal delivery. A birthing simulator and smart glasses were used to livestream the simulated delivery, and a previously published checklist was used to show the steps for conducting routine vaginal delivery. A follow-up electronic survey assessed the clarity of the video and audio feed, level of satisfaction, positive and constructive feedback, and an error-identification exercise with a smart glass-recorded video. A total of 62 students participated; 98% of them reported that the audio and video feeds were clear and 95% of the students reported being extremely satisfied or satisfied with the teaching tool. Students could identify on average three out of four errors. Students believed the streaming to be "interactive" and a "most effective remote learning" tool, but expressed that it did not "take the place of clinical skills learning." The combination of smart glass technology and simulation can be a useful new tool for clinical faculty who simultaneously deliver care to patients and teach. Continued research is needed to explore the use of smart glass technology into livestreaming or surgeries and examinations, with consideration for patient privacy concerns and remote assessment of students.

Passively generated big data for micro-mobility: State-of-the-art and future research directions

The sharp rise in popularity of micro-mobility poses significant challenges in terms of ensuring its safety, addressing its social impacts, mitigating its environmental effects, and designing its systems. Meanwhile, micro-mobility is characterised by its richness in passively generated big data that has considerable potential to address the challenges. Despite an increase in recent literature utilising passively generated micro-mobility data, knowledge and findings are fragmented, limiting the value of the data collected. To fill this gap, this article provides a timely review of how micro-mobility research and practice have exploited passively generated big data and its applications to address major challenges of micro-mobility. Despite its clear advantages in coverage, resolution, and the removal of human errors, passively generated big data needs to be handled with consideration of bias, inaccuracies, and privacy concerns. The paper also highlights areas requiring further research and provides new insights for safe, efficient, sustainable, and equitable micro-mobility.