Artificial Intelligence Interventions Research Articles

Designing medical artificial intelligence for in- and out-groups

Medical artificial intelligence (AI) is expected to deliver worldwide access to healthcare. Through three experimental studies with Chinese and American participants, we tested how the design of medical AI varies between in- and out-groups. Participants adopted the role of a medical AI designer and decided how to develop medical AI for in- or out-groups based on their experimental condition. Studies 1 (pre-registered: N = 191) revealed that Chinese participants were less likely to adopt human doctors' assistance in medical AI system when targeting patients from US (i.e., out-groups) than for patients from China (i.e., in-groups). Study 2 (N = 190) revealed that US participants were less likely to adopt human doctors' assistance in medical AI system when targeting patients from China (i.e., out-groups) than for patients from US (i.e., in-groups). Study 3 revealed that Chinese medical students (N = 160) selected a smaller training database for AI when diagnosing diabetic retinopathy among US patients (i.e., out-groups) than for Chinese patients (i.e., in-groups), and this effect was stronger among medical students from higher (vs. lower) socioeconomic backgrounds. This AI design inequity was mediated by individuals’ underestimation of out-group heterogeneity. Overall, our evidence suggests that out-group stereotype shapes the design of medical AI, unwittingly undermining healthcare quality. The current findings underline the need for more robust data on medical AI development and intervention research addressing healthcare inequity.

Reporting guidelines for artificial intelligence in healthcare research.

Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when reporting a study. The use of artificial intelligence (AI) reporting guidelines that address potential sources of bias specific to studies involving AI interventions has the potential to improve the quality of AI studies, through improvements in their design and delivery, and the completeness and transparency of their reporting. With a number of guidance documents relating to AI studies emerging from different specialist societies, this Review article provides researchers with some key principles for selecting the most appropriate reporting guidelines for a study involving an AI intervention. As the main determinants of a high-quality study are contained within the methodology of the study design rather than the intervention, researchers are recommended to use reporting guidelines that are specific to the study design, and then supplement them with AI-specific guidance contained within available AI reporting guidelines.

The intervention of artificial intelligence in the recruitment function in UAE’s hospitality industry

Artificial Intelligence (AI) is not a new concept for Hospitality Industry and Recruitment functions. AI has displaced the human intervention in routine tasks. In few years, AI will take over several jobs (Kubler, 2018). Recently AI technologies support application screening, data analysis, and preliminary interviews, saving time of recruiters. Chatbots are now designated recruitment officers supporting candidates. Researchers have studied the influence of AI on Recruitment, but only a few focused on the AI displacing human in the recruitment function performed in UAE’s hospitality industry.
 This research aims to understand the transformation in the recruitment function of UAE’s hospitality industry due to AI intervention. Using concurrent mixed-methods, data was collected by interviewing 10 UAE HR leaders and surveying 135 HR professionals. The inductive-deductive thematic analysis was conducted for subjective measures and descriptive analysis was performed for scaled measures.
 This study found that UAE’s hospitality sector deployed AI technologies in recruitment areas such as job advertisements, collecting applications, maintaining profiles, and storing the applications. The routine, repetitive, and heavy-volume tasks in the recruitment are delegated to AI while strategic roles are retained for human professionals including development of strategies, and creation of job descriptions and specifications. While the literature review suggested a wider application of AI in recruitment function, UAE’s hospitality sector seems to be lagging. The recommendations will benefit industry leaders, HR professionals, recruitment consultants, and AI developers to rethink on the recruitment strategies, operations, and administration and to embrace the intervention of AI in recruiting the best talent proficiently.

Application of Artificial Intelligence powered digital writing assistant in higher education: randomized controlled trial

A major challenge in educational technology integration is to engage students with different affective characteristics. Also, how technology shapes attitude and learning behavior is still lacking. Findings from educational psychology and learning sciences have gained less traction in research. The present study was conducted to examine the efficacy of a group format of an Artificial Intelligence (AI) powered writing tool for English second postgraduate students in the English academic writing context. In the present study, (N = 120) students were randomly allocated to either the equipped AI (n = 60) or non-equipped AI (NEAI). The results of the parametric test of analyzing of covariance revealed that at post-intervention, students who participated in the AI intervention group demonstrated statistically significant improvement in the scores, of the behavioral engagement (Cohen's d = .75, 95% CI [0.38, 1.12]), of the emotional engagement Cohen's d = .82, 95% CI [0.45, 1.25], of the cognitive engagement, Cohen's d = .39,95% CI [0.04, .76], of the self-efficacy for writing, Cohen's d = .54, 95% CI [0.18, 0.91], of the positive emotions Cohen's d = . 44, 95% CI [0.08, 0.80], and of the negative emotions, Cohen's d = −.98, 95% CI [−1.36, −0.60], compared with NEAI. The results suggest that AI-powered writing tools could be an efficient tool to promote learning behavior and attitudinal technology acceptance through formative feedback and assessment for non-native postgraduate students in English academic writing.

Raising the Bar for Randomized Trials Involving Artificial Intelligence: The SPIRIT-Artificial Intelligence and CONSORT-Artificial Intelligence Guidelines

Artificial intelligence (AI)-based applications have the potential to improve the quality and efficiency of patient care in dermatology. Unique challenges in the development and validation of these technologies may limit their generalizability and real-world applicability. Before the widespread adoption of AI interventions, randomized trials should be conducted to evaluate their efficacy, safety, and cost effectiveness in clinical settings. The recent Standard Protocol Items: Recommendations for Interventional Trials-AI extension and Consolidated Standards of Reporting Trials-AI extension guidelines provide recommendations for reporting the methods and results of trials involving AI interventions. High-quality trials will provide gold standard evidence to support the adoption of AI for the benefit of patient care.

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations.

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

Psychiatric Advance Directives and Artificial Intelligence: A Conceptual Framework for Theoretical and Ethical Principles.

The patient's decision-making abilities are often altered in psychiatric disorders. The legal framework of psychiatric advance directives (PADs) has been made to provide care to patients in these situations while respecting their free and informed consent. The implementation of artificial intelligence (AI) within Clinical Decision Support Systems (CDSS) may result in improvements for complex decisions that are often made in situations covered by PADs. Still, it raises theoretical and ethical issues this paper aims to address. First, it goes through every level of possible intervention of AI in the PAD drafting process, beginning with what data sources it could access and if its data processing competencies should be limited, then treating of the opportune moments it should be used and its place in the contractual relationship between each party (patient, caregivers, and trusted person). Second, it focuses on ethical principles and how these principles, whether they are medical principles (autonomy, beneficence, non-maleficence, justice) applied to AI or AI principles (loyalty and vigilance) applied to medicine, should be taken into account in the future of the PAD drafting process. Some general guidelines are proposed in conclusion: AI must remain a decision support system as a partner of each party of the PAD contract; patients should be able to choose a personalized type of AI intervention or no AI intervention at all; they should stay informed, i.e., understand the functioning and relevance of AI thanks to educational programs; finally, a committee should be created for ensuring the principle of vigilance by auditing these new tools in terms of successes, failures, security, and relevance.

Employability implications of artificial intelligence in healthcare ecosystem: responding with readiness

PurposeIntervention of artificial intelligence (AI) has brought up the issue of future job prospects in terms of the employability of the professionals and their readiness to harness the benefits of the AI. The purpose of this study is to recognize the implications of AI on employability by analyzing the issues in the health-care sector that if not addressed, can dampen the possibilities offered by AI intervention and its pervasiveness (Cornell University, INSEAD, and WIPO, 2019).Design/methodology/approachTo get an insight on these concerns, an approach of total interpretive structural modelling, cross impact matrix multiplication applied to classification and path analysis have been used to understand the role of the critical factors influencing employability in the health-care sector.FindingsThis study primarily explores the driving-dependence power of the critical factors of the employability and displays hierarchical relationships. It also discusses measures which, if adopted, can enhance employability in the health-care sector with the intervention of AI.Research limitations/implicationsEmployability also has an impact on the productivity of the health-care service delivery which may provide a holistic opportunity to the management in health-care organizations to forecast the allocation and training of human resources and technological resources.Originality/valueThe paper attempts to analyze AI intervention and other driving factors (operational changes, customized training intervention, openness to learning, attitude toward technology, job-related skills and AI knowledge) to analyze their impact on employability with the changing needs. It establishes the hierarchical relationship among the critical factors influencing employability in the health-care sector because of the intervention of AI.

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic.

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two ways—firstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Artificial intelligence in emergency medicine: A scoping review.

IntroductionDespite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine.MethodsThe scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines for scoping reviews using Medline‐OVID, EMBASE, CINAHL, and IEEE, with a double screening and extraction process. The search included articles published until February 28, 2020. Articles were excluded if they did not self‐classify as studying an AI intervention, were not relevant to the emergency department (ED), or did not report outcomes or evaluation.ResultsOf the 1483 original database citations, 395 were eligible for full‐text evaluation. Of these articles, a total of 150 were included in the scoping review. The majority of included studies were retrospective in nature (n = 124, 82.7%), with only 3 (2.0%) prospective controlled trials. We found 37 (24.7%) interventions aimed at improving diagnosis within the ED. Among the 150 studies, 19 (12.7%) focused on diagnostic imaging within the ED. A total of 16 (10.7%) studies were conducted in the out‐of‐hospital environment (eg, emergency medical services, paramedics) with the remainder occurring either in the ED or the trauma bay. Of the 24 (16%) studies that had human comparators, there were 12 (8%) studies in which AI interventions outperformed clinicians in at least 1 measured outcome.ConclusionAI‐related research is rapidly increasing in emergency medicine. There are several promising AI interventions that can improve emergency care, particularly for acute radiographic imaging and prediction‐based diagnoses. Higher quality evidence is needed to further assess both short‐ and long‐term clinical outcomes.

Designing a clean and efficient air conditioner with AI intervention to optimize energy-exergy interplay

Conventional refrigerants in air conditioners (A/Cs) although deliver localized comfort within dwellings, their overall detrimental impact on the environment is an alarming issue. This study investigates eco-friendly alternatives to the refrigerants R410A and R22 (which contribute to global warming and ozone depletion) through a comprehensive analysis of salient parameters such as coefficient of performance (COP), volumetric cooling capacity, and exergetic efficiency (ηex) featuring a sustainably retrofitted vapor compression based A/C. Diverse thermophysical properties of alternate refrigerants yield multiple options for contriving a sustainable A/C. Performance enhancement of the retrofitted system is then realized through a multifaceted genetic algorithm (GA) coupled with an artificial neural network (ANN). Subsequently, the incongruence of optimality between maximum system COP and maximum ηex is dealt by a dual ANN powered non dominated sorting genetic algorithm-2 (NSGA-2) optimizer which provides balanced output in terms of COP (4.37 for L20a and 4.238 for ARM71a refrigerant respectively) and ηex (26.208% for L20a and 25.413% for ARM71a refrigerant respectively). The artificial intelligence (AI) based approach helps comprehend the trade-off between different system performance indices (having different units/ranges of variation) during optimum design selection. Furthermore, the data-driven surrogate model reveals the dominating effect of energy performance over exergy performance of the system, urging for the higher priority of resource allocation for COP upgrade than ηex upgrade. Finally, the multi-objective optimization yields a broader set of Pareto optimal points which offer flexibility to the stakeholders to thrust the sustainable system towards higher COP or higher ηex mode of operation.

Towards Designing Out Helplessness: AI Interventions for Videogame Learnability

I research how we can utilize artificial intelligence (AI) techniques to make videogames more effective teachers of their own internal complexities. I focus on exploring how we can design more graceful tutorials by finding inspiration from the learning sciences and inclusive design domains. Towards this end I study theories of failure in videogames, build tools that emphasize best learning practices and develop AI systems that improve videogame learnability.

Towards Equitable AI Interventions for People Who Use Drugs: Key Areas That Require Ethical Investment.

There has been growing investment in artificial intelligence (AI) interventions to combat the opioid-driven overdose epidemic plaguing North America. Although the evidence for the use of technology and AI in medicine is mounting, there are a number of ethical, social, and political implications that need to be considered when designing AI interventions. In this commentary, we describe 2 key areas that will require ethical deliberation in order to ensure that AI is being applied ethically with socially vulnerable populations such as people who use drugs: (1) perpetuation of biases in data and (2) consent. We offer ways forward to guide and provide opportunities for interventionists to develop substance use-related AI technologies that account for the inherent biases embedded within conventional data systems. This includes a discussion of how other data generation techniques (eg, qualitative and community-based approaches) can be integrated within AI intervention development efforts to mitigate the limitations of relying on electronic health record data. Finally, we emphasize the need to involve people who use drugs as stakeholders in all phases of AI intervention development.

P071: Artificial intelligence in emergency medicine: A scoping review

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

Artificial Intelligence and Community Well-being: A Proposal for an Emerging Area of Research

We are calling for a new area of research on the nexus of community well-being and artificial intelligence (AI). Three components of this research we propose are (1) the development and use of well-being metrics to measure the impacts of AI; (2) the use of community-based approaches in the development of AI; and (3) development of AI interventions to safeguard or improve community well-being. After providing definitions of community, well-being, and community well-being, we suggest a definition of AI for use by community well-being researchers, with brief explanations of types and uses of AI within this context. A brief summary of threats and opportunities facing community well-being for which AI could potentially present solutions or exacerbate problems is provided. The three components we propose are then discussed, followed by our call for cross-sector, interdisciplinary, transdisciplinary and systems-based approaches for the formation of this proposed area of research.

The impact of artificial intelligence on corporate control

This paper investigates the relationship between artificial intelligence (AI) and corporate control in the United Arab Emirates (UAE) emerging market. An exploratory study was conducted to derive the research questions. The nonprobability purposive sampling technique was implemented to select 10 highly experienced interviewees. In-depth primary data was collected through semi-structured interviews during 2019. Qualitative content analysis was used to answer the research questions. The results show a positive impact of AI on firm productivity and the auditing process, but uncertain influence on accounting information systems. More specifically, AI intervention increases firm productivity, creates new jobs and speeds up work processes. However, current AI technology is less likely to redefine auditing roles and still insufficient for developing accounting information systems. Human integration with AI systems will lead to more efficient results. This paper increases our understanding of how AI techniques can improve corporate control practices and the importance of selecting appropriate accounting professionals to decrease AI operation risks.

Role of Artificial Intelligence in Social Media Marketing

This presented review paper encompasses all the ongoing trends of the Artificial Intelligence. This review evaluates the possibilities of Artificial Intelligence (AI) in social media marketing. This review paper aims to study the potential of AI in the social media marketing. This review paper fulfils the objectives of simulation of AI in the business organisations to enhance marketing which will in return increase sales. The paper aims examines the possibilities and strengths of AI. This review paper will explore the intervention of AI into marketing arena. The review flows from the general to specific. It evaluates the effect of AI on both the society as a whole and also specifically on the business organisation. It assesses the effect of AI on both the Social media and the business.