Potential Misuse Research Articles

NLP and oral health information.

NLP and oral health information.

Mental health and substance use disorder comorbidities among Medicaid beneficiaries: Associations with opioid use disorder and prescription opioid misuse.

Medicaid presently insures about one-fourth of the US population and disproportionately insures about 38 % of non-elderly adults with an opioid use disorder (OUD). Owing to Medicaid's prominent role insuring persons with an OUD and that Medicaid coverage includes pharmaceutical benefits, there has been considerable interest in studying potential prescription opioid misuse among Medicaid beneficiaries and identifying subpopulations at higher risk for misuse and possible progression to an OUD. The study goals were to explore the associations among prescription opioid misuse, OUD, and co-occurring mental health and other substance use disorders (SUD). We analyzed Illinois Medicaid 2018 claims data for 1102479 adult beneficiaries 18 to 64 years of age. Using algorithms based on previous studies, we first determined either the presence or absence of nine SUDS (including OUD), nine mental health disorders and likely prescription opioid misuse. Then, we subdivided the beneficiary sample into five groups: those who were prescribed opioids and evidenced either no, possible, or probable misuse; those evidencing an OUD; and those evidencing no opioid use or misuse. Bivariate analyses, upset plots, and multinomial logistic regressions were used to compare the five subgroups on the prevalence of co-occurring SUDS and mental health disorders. Those with an OUD or with probable prescription opioid misuse had the highest prevalence of most co-occurring conditions with beneficiaries with an OUD the most likely to evidence co-occurring SUDS, particularly tobacco use disorder, whereas those with probable misuse had elevated prevalence rates of co-occurring mental health disorders comparable to those with an OUD. The medical complexity of persons with an OUD or misusing prescription opioids are considered in light of recent attempts to expand buprenorphine provision as a medication for OUD among Medicaid beneficiaries. Additionally, we consider the possibility of gender, co-occurring mental health disorders, and tobacco use disorder as important risk factors for progressing to prescription opioid misuse and an OUD.

Automatic Detection of Generated Texts and Energy: Exploring the Relationship

The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithms for text detection.

Bioética e medicina personalizada no envelhecimento: explorando complexidades

Abstract Concierge medicine emerged as a promising approach to offer exclusive and personalized treatments using genomic tools. In aging studies, concierge medicine has the potential to transform the treatment and prevention of age-associated and related diseases through pharmacogenetics and nutrigenomics; however, its use of genomic data raises important bioethical concerns, including privacy, consent, equity issues and potential misuse of these data for discriminatory purposes. Hence, careful consideration should be given to the biomedical, social, and ethical aspects of concierge medicine in aging contexts. Our review explores the main aspects of age-related pharmacogenetics and nutrigenomics data in concierge medicine, discussing the bioethical concerns involved in its use.

Unveiling the Environmental Implications of Automatic Text Generation and the Role of Detection Systems

The emergence of artificial intelligence (AI) and natural language processing (NLP) technologies has led to the proliferation of automated systems capable of generating text. While these advancements have enhanced various fields, such as language translation and content generation, they have also given rise to concerns regarding the potential misuse of generated texts, particularly in the context of environmental preservation. This scientific article investigates the intricate relationship between automatic detection of generated texts and the environment. We examine the impact of generated texts on environmental awareness, misinformation propagation, and the role of automated detection systems in mitigating the risks associated with generated content. Our findings highlight the crucial need for robust detection mechanisms to preserve the integrity of environmental discourse and ensure sustainable decision-making.

Artificial intelligence for detecting fake news on social media - attitude survey

in the modern digital era, social media are playing an important role in in the dissemination of information. Nevertheless, phenomena such as fake news have the potential to considerably compromise user trust. Concurrently, artificial intelligence unveils remarkable opportunities while also introducing great challenges. Artificial intelligence is already employed in identifying fake news on social media platforms, rendering this a crucial area of exploration due to its profound implications on society and politics. This paper examines the application of artificial intelligence on social media, with a particular focus on detecting fake news, through an analysis of the attitudes of the student population. The findings suggest a limited awareness among students about these kinds of programs, highlighting a necessity for the inclusion of these topics in future university curricula. A majority of students believe that artificial intelligence algorithms for detecting fake news are good for society, compared to those who believe the contrary. The results indicate a prevailing concern regarding the potential misuse of these technologies and their impact on freedom of speech, thereby emphasizing the need for impartial oversight and regulation. The research has demonstrated that students are aware of the advantages and limitations of applying artificial intelligence on social media, with older students exhibiting greater receptiveness towards these programs.

Medical students’ perception of the role of artificial intelligence in healthcare

Introduction. Artificial intelligence is defined as a part of computer science capable of manipulating extensive data through machine learning. The aim of this study is to investigate medical students? perceptions regarding the use of artificial intelligence in the field of healthcare. Material and Methods. This research was conducted as a cross-sectional study using the Computer Assisted Web Interviewing technique for data collection by surveying students through social networks. The sample consists of 160 students who were surveyed in November 2023. The aim was to provide answers to the question of how students perceive the use of new technology - artificial intelligence in the field that represents their future profession. Results. The results have shown a well-developed awareness among students regarding the potential application of artificial intelligence in the medical field, emphasizing a positive perception of the benefits that artificial intelligence can bring. They have also recognized the importance of incorporating artificial intelligence training into medical education. Students have expressed concerns, primarily about potential misuse of artificial intelligence and ethical issues related to its use in medicine. Conclusion. Medical students are aware not only of the benefits but also the risks associated with the implementation of artificial intelligence in medicine.

This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy

We explore the recently released ChatGPT model, one of the most powerful conversational AI models that has ever been developed. This opinion provides a perspective on its strengths and weaknesses and a call to action for the AI community (including academic researchers and industry) to work together on preventing potential misuse of such powerful AI models in our everyday lives.

Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora's box

The purpose of this research was to investigate attitudes of both students and teachers concerning Artificial Intelligence (AI) tools in the L2 classroom. The study was a descriptive, qualitative, mixedmethods case study whose data were taken from a purposive, convenient sample at a private, English-speaking university during the Summer Semester 2023 in Beirut, Lebanon. Data collection primarily involved an online survey on Google forms which was given to a sample of 49 students taking a research-based English 202 course of which 46 were completed. Afterwards, six English teachers and six students were chosen based on their voluntary will to participate in individual interviews for the former and semi-structured focus group interviews for the latter. The findings revealed that approximately 85% of students did indeed use AI unethically to get ideas for their assignments, assist them in their projects' “blue-prints” or do their assignments/projects altogether. The findings also revealed that a “love/hate” relationship seemed to dictate students' relationships with AI, where students did indeed make use of AI but were distrusting of it for privacy and equity concerns. Finally, findings also revealed that most of the interviewed instructors' readiness to undergo training for AI was more to monitor students' potential misuse of it. The article purposes a suggestive revamping of course learning objectives due to students' inclinations to misuse AI to do their coursework with 89.4% of students willing to use AI to complete their coursework should university punitive measures be removed; furthermore, the article equally proposes future research investigating the impact and use of AI in the higher educational classroom on student performance and that it be used with a “grain of salt” as it may unleash a Pandora's box of future generations graduating without the necessary know-how in delicate professions of medicine, nursing, engineering, architecture among others.

2175. Diagnostic Utility of Pneumococcal Urinary Antigen Assay in Clinical Practice: a Retrospective Study in a Community Hospital in Evanston, Illinois

Abstract Background While the pneumococcal urine antigen test (PUAT) is a validated tool for diagnosing pneumonia, recent evidence suggested lower sensitivity than expected (approximately 60-65%) and limited impact on clinical outcomes. A lack of clinical indicators for a positive test result and the absence of clear recommendations based on high-quality evidence create grounds for potential test misuse in real practice. Methods We performed a Workbench report to retrieve individual orders of Streptococcus pneumoniae urine antigen test (BinaxNOWTM) from our community hospital in Evanston, Illinois, between January 1, 2011, and December 31, 2021. We then identified patients with or without clinical pneumonia based on McGeer criteria: new infiltrates on chest x-ray plus cough, shortness of breath, oxygen saturation < 94%, or respiratory rate ≥ 25 plus fever, leukocytosis, or altered mental status. Results Among 8,110 individuals tested for PUAT, only 273 positive results were identified (3.36%); of those, 83 (30.4%) did not meet criteria for clinical pneumonia (Figure 1). The median age of patients in a group with clinical pneumonia and without was 67 and 69 years, respectively. The prevalence of diabetes mellitus, COPD/asthma, and immunosuppression was not statistically significant between groups (Table 1). The signs and symptoms are presented in Table 2.S. pneumoniae was cultured in only 13.9% of cases in patients with clinical pneumonia, which correlates with the known sensitivities of this test (Table 3). More ICU admissions and NIPPV use were noted in patients with clinical pneumonia (p = 0.04 each). However, there was no difference in the use of antibacterial therapy among the two groups (p = 0.83), mechanical ventilation rates (p = 0.2), and length of hospital stay (p = 0.2) (Table 4). The positive predictive value of PUAT was 69% in our study (Table 5). Conclusion Our study demonstrated a low positivity rate of PUAT despite a large sample size and a significant proportion of positive test results in patients without clinical pneumonia. The widespread use of PUAT should be carefully reconsidered and applied only to a selected category of patients. Such a strategy will reduce the discrepancy between current guidelines and real-world care patterns and provide an additional benefit of cost-effectiveness. Disclosures All Authors: No reported disclosures.

Systematic Evaluation of Two Classical Receptor Models in Source Apportionment of Soil Heavy Metal(loid) Pollution Using Synthetic and Real-World Datasets.

Due to the lack of a priori knowledge on true source makeup and contributions, whether the source apportionment results of Unmix and positive matrix factorization (PMF) are accurate cannot be easily assessed, despite the availability of built-in indicators for their goodness of fit and robustness. This study systematically evaluated, for the first time, the applicability and reliability of these models in source apportionment of soil heavy metal(loid)s with synthetic datasets generated using known source profiles and contributions and a real-world dataset as well. For eight synthetic datasets with different pollution source characteristics, feasible Unmix solutions were close to the true source component compositions (R2 > 0.936; total mean squared errors (MSEs) < 0.04), while those of PMF had significant deviations (R2 of 0.484-0.998; total MSEs of 0.04-0.16). Nonetheless, both models failed to accurately apportion the sources with collinearity or non-normal distribution. Unmix generally outperformed PMF, and its solutions showed much less dependence on sample size than those of PMF. While the built-in indicators provided little hint on the reliability of both models for the real-world dataset, their sample-size dependence indicated that Unmix probably yielded more accurate solutions. These insights could help avoid the potential misuse of Unmix and PMF in source apportionment of soil heavy metal(loid) pollution.

The Rebranding of Newcastle as a Smart City. Cui Bono?

The city of Newcastle, regional capital of North East England and home to “the UK’s smartest street” located a short distance from its brand new Helix district, is described as having successfully transitioned from a traditionally industrial city to a “smart” city, able to use digital tools to address issues such as congestion, pollution, waste reduction and parking, with the dual objective of “helping people to live better lives” and of making the city more attractive to businesses, investors, international students, and tourists. While ubiquitous in political and academic discourse, the idea of “smart city” is strikingly absent from general public discussion, with very low awareness among those whose lives are supposed to be most improved. Criticisms of smart cities tend to focus on the fact that the services associated with smart cities constitute a very lucrative market, on the lack of residents’ involvement in changes that will directly affect their everyday life, and on the potential for misuse of the data collected. Drawing primarily from a range of published material such as policy documents, newsletters, working papers, announcements, websites, and brochures produced by the key players of the smart city, as well as from conversations with ordinary residents, this article aims to contribute to the discussion on smart cities by confronting official discourses with “actually existing” practices, through the case of the mid-sized city of Newcastle. It explores the uses of smart city tools and discourse to rebrand the city of Newcastle and revitalise civic pride, setting the smart city promises against actual improvements to the lives of local residents as well as their perceptions of their city. The first section of the article sets out the elements of the smart city to show how the characteristics of the city of Newcastle made it an ideal terrain for experimentations; the second section analyses the deployment of the smart city agenda in the city, focusing on the discourses and perceptions of the range of players involved. The third and final section seeks to understand who is set to benefit the most from Newcastle’s transition to a “smart city”. The conclusion presents a nuanced assessment of the potentialities and limits of the smart city.

Patients with limited health literacy have worse preoperative function and pain control and experience prolonged hospitalizations following shoulder arthroplasty.

Patients with limited health literacy (LHL) may have difficulty understanding and acting on medical information, placing them at risk for potential misuse of health services and adverse outcomes. The purposes of our study were to determine (1) the prevalence of LHL in patients undergoing inpatient shoulder arthroplasty, (2) the association of LHL with the degree of preoperative symptom intensity and magnitude of limitations, (3) and the effects of LHL on perioperative outcomes including postoperative length of stay (LOS), total inpatient costs, and inpatient opioid consumption. We retrospectively identified 230 patients who underwent elective inpatient reverse or anatomic shoulder arthroplasty between January 2018 and May 2021 from a prospectively maintained single-surgeon registry. The health literacy of each patient was assessed preoperatively using the validated 4-item Brief Health Literacy Screening Tool. Patients with a Brief Health Literacy Screening Tool score ≤ 17 were categorized as having LHL. The outcomes of interest were preoperative patient-reported outcome scores and range of motion, LOS, total postoperative inpatient opioid consumption, and total inpatient costs as calculated using time-driven activity-based costing methodology. Univariate analysis was performed to determine associations between LHL and patient characteristics, as well as the outcomes of interest. Multivariable linear regression modeling was used to determine the association between LHL and LOS while controlling for potentially confounding variables. Overall, 58 patients (25.2%) were classified as having LHL. Prior to surgery, these patients had significantly higher rates of opioid use (P=.002), more self-reported allergies (P=.007), and worse American Shoulder and Elbow Surgeons scores (P=.001), visual analog scale pain scores (P=.020), forward elevation (P<.001), and external rotation (P=.022) but did not significantly differ in terms of any additional demographic or clinical characteristics (P>.05). Patients with LHL had a significantly longer LOS (1.84±0.92 days vs. 1.57±0.58 days, P=.012) but did not differ in terms of total hospitalization costs (P=.65) or total inpatient opioid consumption (P=.721). On multivariable analysis, LHL was independently predictive of a significantly longer LOS (β, 0.14; 95% confidence interval, 0.02-0.42; P=.035). LHL is commonplace among patients undergoing elective shoulder arthroplasty and is associated with greater preoperative symptom severity and activity intolerance. Its association with longer hospitalizations suggests that health literacy is an important factor to consider for postoperative disposition planning.

Interday Stability of Taxi Travel Flow in Urban Areas

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.

Analysis of Factor in User Intention to Use the Covid-19 Tracking Application

Science and technology can be collaborated to create an application that can help to track the contacts of COVID-19 patients. Smartphone-based contact tracing applications have been adopted by more than 50 countries. One of which is in Indonesia. In March 2020, Indonesia launched a mobile application to track the contact of COVID-19 patients namely PeduliLindungi. During its usage, users find some issues about PeduliLindungi, such as potential data leaks, data misuse, and data inaccuracies. This research is aimed to develop a conceptual model to analyze factors that affect user intentions in using the PeduliLindungi application. The proposed conceptual model is the integration of EUCS, DeLone and McLean that is equipped by the system security variables. There were 288 respondents. The data is processed using SmartPLS 3.0. According to the results of the analysis, the proposed conceptual model has 83.1 percent for its accuracy. User satisfaction and system security give a positive and significant impact on user intentions. The variables of content, accuracy, format, ease of use, and timeliness give a positive and significant impact on user satisfaction. On the other hand, the system security has no positive and significant impact on user satisfaction. Meanwhile, user satisfaction and system security itself affects the user's intentions in using the PeduliLindungi application.

The threat, hype, and promise of artificial intelligence in education

The idea of building intelligent machines has been around for centuries, with a new wave of promising artificial intelligence (AI) in the twenty-first century. Artificial Intelligence in Education (AIED) is a younger phenomenon that has created hype and promises, but also been seen as a threat by critical voices. There have been rich discussions on over-optimism and hype in contemporary AI research. Less has been written about the hyped expectations on AIED and its potential to transform current education. There is huge potential for efficiency and cost reduction, but there is also aspects of quality education and the teacher role. The aim of the study is to identify potential aspects of threat, hype and promise in artificial intelligence for education. A scoping literature review was conducted to gather relevant state-of-the art research in the field of AIED. Main keywords used in the literature search were: artificial intelligence, artificial intelligence in education, AI, AIED, teacher perspective, education, and teacher. Data were analysed with the SWOT-framework as theoretical lens for a thematic analysis. The study identifies a wide variety of strengths, weaknesses, opportunities, and threats for artificial intelligence in education. Findings suggest that there are several important questions to discuss and address in future research, such as: What should the role of the teacher be in education with AI? How does AI align with pedagogical goals and beliefs? And how to handle the potential leak and misuse of user data when AIED systems are developed by for-profit organisations?

Ethical Tensions in Applications of AI for Addressing Human Trafficking: A Human Rights Perspective

In the last two decades, human trafficking (where individuals are forcibly exploited for the profits of another) has seen increased attention from the artificial intelligence (AI) community. Clear focus on the ethical risks of this research is critical given that those risks are disproportionately born by already vulnerable populations. To understand and subsequently address these risks, we conducted a systematic literature review of computing research leveraging AI to combat human trafficking and apply a framework using principles from international human rights law to categorize ethical risks. This paper uncovers a number of ethical tensions including bias endemic in datasets, privacy risks stemming from data collection and reporting, and issues concerning potential misuse. We conclude by highlighting four suggestions for future research: broader use of participatory design; engaging with other forms of trafficking; developing best practices for harm prevention; and including transparent ethics disclosures in research. We find that there are significant gaps in what aspects of human trafficking researchers have focused on. Most research to date focuses on aiding criminal investigations in cases of sex trafficking, but more work is needed to support other anti-trafficking activities like supporting survivors, adequately address labor trafficking, and support more diverse survivor populations including transgender and nonbinary individuals.

The Workplace Playbook VR: Exploring the Design Space of Virtual Reality to Foster Understanding of and Support for Autistic People

A growing number of organizations are hiring autistic individuals as they start to recognize the value of a neurodiverse workforce. Despite this trend, the lack of support for autistic employees in workplaces complicates their employment. However, little is known about how people around autistic individuals can support them to create pleasant employment experiences. In this work, we develop the concept of the Workplace Playbook VR to investigate how virtual reality (VR) can help autistic people develop their work-related social communication skills in partnership with people in their support network. Using a video prototype to present the concept, we interviewed 28 participants, including 10 autistic people and 18 members of their support networks, which included family members and professionals. Our interviews revealed that the Workplace Playbook VR program can provide common ground for autistic people and members of their support network to participate in more empathetic communication regarding workplace challenges. Despite the benefits, we identified the potential misuse of social communication skills training features of the VR program to correct the personal characteristics of autistic individuals. Furthermore, to cultivate inclusive workplace environments, we found the needs of VR development not only for autistic people but also for neurotypical employees to promote their understanding of autism and empathy toward autistic employees. We suggest VR designs that promote a sense of agency and self-advocacy for autistic employees, and autism awareness and acceptance training for neurotypical employees.

8977 The Use and (Potential) Misuse of the AAGL Endometriosis Classification System

<h3>Study Objective</h3> To demonstrate use and (potential) misuse of the AAGL 2021 Endometriosis Classification Scoring System, an anatomy-based surgical complexity scoring system - with the goal of optimizing future endometriosis staging. <h3>Design</h3> A case report applying the new AAGL 2021 Endometriosis Classification and use of the App. <h3>Setting</h3> A university hospital based in Detroit, Michigan; Wayne State University/ Detroit Medical Center. <h3>Patients or Participants</h3> A case report of a Stage 2 Endometriosis based on the Scoring System with a patient presenting for infertility. <h3>Interventions</h3> Application and utilization of the AAGL 2021 Endometriosis Classification for a Stage 2 Endometriosis Classification. <h3>Measurements and Main Results</h3> Scoring system demonstrated feasibility, applied utility to the correlation that was discussed regarding the Scoring System, but also limitations and potential misuses of the scoring system. <h3>Conclusion</h3> The AAGL Endometriosis Scoring System is a feasible intraoperative scoring system correlated with pain. It is critical to be cognizant of staging parameters while scoring the endometriotic lesions and remaining consistent.

Examining the Awareness, Acceptability, and Adoption of Conventional and non-conventional Forms of Pre-Exposure Prophylaxis (PrEP) for HIV Prevention Among jail-involved Black Sexual Minority men (BSMM) and Black Transgender Women (BTW) in Two Diverse US Cities.

Black sexual minority men (BSMM) and Black transgender women (BTW) are disproportionately impacted by HIV and incarceration in the United States. In-depth interviews (N=34) and ongoing thematic analysis guided by the Exploration Phase of the Exploration, Preparation, Implementation, Sustainment framework were conducted to uncover key themes focused on the awareness, acceptability, and early adoption of conventional (i.e., daily oral pill) and non-conventional forms of PrEP (i.e., long-acting injectable, e-prescription for pick up post release) among jail-involved BSMM and BTW in Chicago, Illinois and Baton Rouge, Louisiana. The majority of participants were cisgender BSMM (88%) and were enrolled in Chicago (65%). There was greater awareness, availability, and adoption of conventional PrEP and non-conventional PrEP e-prescription for pick up post release among Chicago participants compared with Baton Rouge participants. Participants were largely receptive to all three forms of PrEP and stated a high need for HIV prevention in jails and immediately following release. PrEP stigma emerged as a major barrier to conventional daily PrEP adoption while incarcerated; potential misuse (e.g., pill selling) as a potential barrier to PrEP eprescription; and needle aversion and added clinic time as potential barriers to longacting injectable (LAI)-PrEP. Participants indicated that PrEP e-prescription could help support continuity of care post release and highlighted reduced stigma, convenience, and longer-term HIV protection as benefits for LAI-PrEP. Study findings provided context-specific information to inform the implementation of future PrEP interventions for jail-involved BSMM and BTW in two highly HIV-impacted jurisdictions.