Artificial Intelligence Interventions Research Articles

Artificial Intelligence and Robotics Led Technological Tremors: A Seismic Shift towards Digitizing the Legal Ecosystem

The legal ecosystem is continuously confronted with new challenges and disruptions as a result of the technological invasion initiated by cutting-edge technologies, such as Artificial Intelligence (AI) and Robotics, which have taken over the world. The amalgamation of AI-enabled mechanisms and robotics into human life has elevated significant issues. This digital juggernaut cannot stay constant by the legal landscape, and some degree of assimilation is permitted to pave the way for the efficient administration of justice. The current study is significant since there is a substantial absence of legal research into the implications of AI and robotics on legal rights, which undoubtedly impacts the legal ecosystem. In this study, we have examined the significance, progress, and challenges of integrating Robotics and AI into the legal ecosystem, as they pave way for resilient legal infrastructure. Issues such as privacy, ethical grievances, data protection, confidentiality, and integrity issues are evaluated in this study. The study reviewed existing research into AI and robotics intervention in the legal ecosystem to propose a framework for addressing the increased concerns about the implications of technological apparatus in the legal ecosystem. Finally, the study concludes with recommendations that can be adopted for future work.

Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis.

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate external testing and confirm the benefits of AI-assisted care compared with conventional care through appropriately designed and conducted studies, for which prospective studies are desirable. This article explains some of the fundamental methodological points that should be considered when designing and appraising the clinical evaluation of AI algorithms for medical diagnosis. The specific topics addressed include the following: (a) the importance of external testing of AI algorithms and strategies for conducting the external testing effectively, (b) the various metrics and graphical methods for evaluating the AI performance as well as essential methodological points to note in using and interpreting them, (c) paired study designs primarily for comparative performance evaluation of conventional and AI-assisted diagnoses, (d) parallel study designs primarily for evaluating the effect of AI intervention with an emphasis on randomized clinical trials, and (e) up-to-date guidelines for reporting clinical studies on AI, with an emphasis on guidelines registered in the EQUATOR Network library. Sound methodological knowledge of these topics will aid the design, execution, reporting, and appraisal of clinical evaluation of AI.

The Legitimacy of Artificial Intelligence in Judicial Decision Making

Since the birth of artificial intelligence, the discussion of the legitimacy of its application to judicial scenarios has never stopped. The domestic academic circles question the legality of artificial intelligence decision-making mainly embodies four aspects: the judge's subjectivity crisis, the power legitimacy crisis, the imputation difficulty crisis, and the damage to the justice realization crisis. Therefore, it is urgent to clarify the legal logic of artificial intelligence in judicial decision-making and clarify its decision-making limits. Therefore, this paper aims to prove the legality of intelligent judicial operation simultaneously from the four-dimensional perspectives of artificial intelligence's intervention in judicial decision-making, such as the judge's subjectivity, the legitimacy of power, the attribution of fault, and the realization of justice, with a view to the subject, power, responsibility, justice, four aspects of the governance of China's intelligent judiciary to make recommendations.

Whose interests will AI serve? Autonomous agents in infrastructure use

This article examines new challenges for sustainability presented by artificial intelligence (AI) in infrastructure megaprojects. We differentiate between the deliberative processes of infrastructure megaproject construction and the everyday uses of such infrastructure, focusing on the latter as both having major sustainability impacts and presenting distinct opportunities for AI intervention. While AI applications are often imagined supporting human decision-making processes, we argue that cases in which AI agents act autonomously without direct human intervention present novel questions about how to achieve sustainable outcomes. Specifically, we differentiate between the setting of AI objective functions in the development stages and the control of unanticipated behaviours once an AI system can act independently. After illustrating applications of AI in energy grid management and automated driving, we examine how such systems could operate either in support of shared sustainability interests or to advance their private interests.

Application and progress of artificial intelligence technology in gastric cancer diagnosis and treatment

Artificial intelligence (AI) is one of the most rapidly evolving fields in biomedicine during the past decade. Represented by radiomics, machine learning and deep neural network, AI has been increasingly favored by researchers due to its ability to obtain feature information and discover the potential relationship between data and medical outcomes from high-throughput medical data. The incidence and mortality of gastric cancer (GC) has remained high in China. Through combining AI technology with medical examination such as endoscopy, imaging, pathological examination and sequencing, clinical researchers have made great progress in the auxiliary diagnosis, disease staging, prognosis and curative effect prediction of patients with GC. Although the intervention of AI in the medical industry has greatly improved the effective utilization of high-throughput data and accelerated the intelligent process of disease diagnosis and treatment, a number of problems has been raised in medical ethics, patient privacy and the legal status of medical AI at the same time. In the future, rational planning and management of AI technology will provide a strong impetus to promote the development of medicine and reshape the medical industry.

AI, Privacy Breaches and Ethical Transgressions: An Integrative Theoretical Framework

Despite the surge of Artificial Intelligence (AI) in management, the role of rapidly advancing AI on privacy breaches and ethical transgressions is elusive in the extant literature. In the present paper, we propose a theoretical framework integrating attributes of privacy breaches and transgressions in various types of AI tools, applications, and interventions. The proposed model illuminates on transgressions in three types of artificial intelligence: mechanical AI, thinking AI and feeling AI, considering varying levels of severity of impact on privacy. Building from the lens of the psychological contract, the theoretical framework explains the adoption of AI: acceptance, equivocation, reluctance, or rejection in operational, justice, and governance transgressions within each type of AI. By highlighting the challenges embedded in the adoption of AI and a need to safeguard privacy in organizations, our theory provides research and managerial insights, the future scope of scientific scholarship, and a roadmap to the contemporary business ecosystem to seek an in-depth understanding of intertwined aspects of AI, privacy breaches and transgressions

You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly.

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial.

BackgroundThere has been an increase in personal health records with the increased use of wearable devices and smartphone apps to improve health. Traditional health promotion programs by human professionals have limitations in terms of cost and reach. Due to labor shortages and to save costs, there has been a growing emphasis in the medical field on building health guidance systems using artificial intelligence (AI). AI will replace advanced human tasks to some extent in the future. However, it is difficult to sustain behavioral change through technology alone at present.ObjectiveThis study investigates whether AI alone can effectively encourage healthy behaviors or whether human interventions are needed to achieve and sustain health-related behavioral change. We examined the effectiveness of AI and human interventions to encourage dietary management behaviors. In addition, we elucidated the conditions for maximizing the effect of AI on health improvement. We hypothesized that the combination of AI and human interventions will maximize their effectiveness.MethodsWe conducted a 3-month experiment by recruiting participants who were users of a smartphone diet management app. We recruited 102 participants and divided them into 3 groups. Treatment group I received text messages using the standard features of the app (AI-based text message intervention). Treatment group II received video messages from a companion, in addition to the text messages (combined text message and human video message intervention by AI). The control group used the app to keep a dietary record, but no feedback was provided (no intervention). We examine the participants’ continuity and the effects on physical indicators.ResultsCombined AI and video messaging (treatment group II) led to a lower dropout rate from the program compared to the control group, and the Cox proportional-hazards model estimate showed a hazard ratio (HR) of 0.078, which was statistically significant at the 5% level. Further, human intervention with AI and video messaging significantly reduced the body fat percentage (BFP) of participants after 3 months compared to the control group, and the rate of reduction was greater in the group with more individualized intervention. The AI-based text messages affected the BMI but had no significant effect on the BFP.ConclusionsThis experiment shows that it is challenging to sustain participants' healthy behavior with AI intervention alone. The results also suggest that even if the health information conveyed is the same, the information conveyed by humans and AI is more effective in improving health than the information sent by AI alone. The support received from the companion in the form of video messages may have promoted voluntary health behaviors. It is noteworthy that companions were competent, even though they were nonexperts. This means that person-to-person communication is crucial for health interventions.

Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers.

Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.

Embracing artificial intelligence: how can we make it inclusive and relevant for real-world dermatological practice?

Artificial intelligence (AI) developments are rapidly advancing with algorithms capable of out-performing dermatologists in specific contrived challenges, but where prospective evaluation in real-world settings remain limited. Professional bodies have a role to encourage AI approaches to address clinical unmet needs, improve quality of care, and enhance patient experience without compromising safety. In 2019, the American Academy of Dermatology advocated principles of human-centred design for augmented intelligence innovations (1) emphasising the importance of synergy between dermatologist and the computer. More recently, the British Association of Dermatologists highlighted the weak current evidence-base supporting effectiveness of AI interventions in routine clinical practice and encouraged clinicians to independently evaluate the evidence prior to adoption (2).

Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials

ObjectiveGiven the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI...

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions

To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021. We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion. After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods. There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.

Data Subjects' Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media

Automatic emotion recognition (ER)-enabled wellbeing interventions use ER algorithms to infer the emotions of a data subject (i.e., a person about whom data is collected or processed to enable ER) based on data generated from their online interactions, such as social media activity, and intervene accordingly. The potential commercial applications of this technology are widely acknowledged, particularly in the context of social media. Yet, little is known about data subjects' conceptualizations of and attitudes toward automatic ER-enabled wellbeing interventions. To address this gap, we interviewed 13 US adult social media data subjects regarding social media-based automatic ER-enabled wellbeing interventions. We found that participants' attitudes toward automatic ER-enabled wellbeing interventions were predominantly negative. Negative attitudes were largely shaped by how participants compared their conceptualizations of Artificial Intelligence (AI) to the humans that traditionally deliver wellbeing support. Comparisons between AI and human wellbeing interventions were based upon human attributes participants doubted AI could hold: 1) helpfulness and authentic care; 2) personal and professional expertise; 3) morality; and 4) benevolence through shared humanity. In some cases, participants' attitudes toward automatic ER-enabled wellbeing interventions shifted when participants conceptualized automatic ER-enabled wellbeing interventions' impact on others, rather than themselves. Though with reluctance, a minority of participants held more positive attitudes toward their conceptualizations of automatic ER-enabled wellbeing interventions, citing their potential to benefit others: 1) by supporting academic research; 2) by increasing access to wellbeing support; and 3) through egregious harm prevention. However, most participants anticipated harms associated with their conceptualizations of automatic ER-enabled wellbeing interventions for others, such as re-traumatization, the spread of inaccurate health information, inappropriate surveillance, and interventions informed by inaccurate predictions. Lastly, while participants had qualms about automatic ER-enabled wellbeing interventions, we identified three development and delivery qualities of automatic ER-enabled wellbeing interventions upon which their attitudes toward them depended: 1) accuracy; 2) contextual sensitivity; and 3) positive outcome. Our study is not motivated to make normative statements about whether or how automatic ER-enabled wellbeing interventions should exist, but to center voices of the data subjects affected by this technology. We argue for the inclusion of data subjects in the development of requirements for ethical and trustworthy ER applications. To that end, we discuss ethical, social, and policy implications of our findings, suggesting that automatic ER-enabled wellbeing interventions imagined by participants are incompatible with aims to promote trustworthy, socially aware, and responsible AI technologies in the current practical and regulatory landscape in the US.

Translational Ethics: Justified Roles of Bioethicists Within and Beyond Lifecycles of Artificial Intelligence Systems in Health

"Background: Artificial Intelligence (AI) systems hold great promise for the future development within a variety of sectors. At the same time, there is also great concern about harms and potential misuse of AI. Upscaling and implementing existing AI systems do already have the potential of affecting severely, and potentially irreversibly, fundamental social conditions for social interaction, professional autonomy, and political governance. Therefore, guiding principles and frameworks to support developers and governing authorities are emerging around the world to foster justified trust in AI research and innovation. Ultimately, these safeguarding institutions and mechanisms rely on human knowledge and wisdom. Health is an area that is expected to benefit from AI based technologies aimed at promoting beneficial, accurate and effective preventive and curative interventions. Also, machine learning technologies might be used to improve the accuracy of the evidence base for cost-effective and beneficial decision-making. How can bioethicists contribute to promote beneficial AI interventions and avoid harms produced by AI technology? What would be justified roles of bioethicists in development and use of AI systems? Method: The paper is based on literature review and philosophical reflection. Discussion: In this presentation, we will base our analysis on an analytical decomposition of the life cycle of AI systems into the phases of development, deployment and use. Furthermore, we will use a framework of translational ethics proposed by Bærøe, and identify a variety of structural tasks, as well as limitations to such, for bioethicists to undertake within this emerging multifold area of experts and disciplines. "

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.

BackgroundResearch on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC.ObjectiveWe intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings.MethodsWe conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data.ResultsWe retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%).ConclusionsWe observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection.

BackgroundArtificial intelligence (AI) assistance has been considered as a promising way to improve colonoscopic polyp detection, but there are limited prospective studies on real‐time use of AI systems.MethodsWe conducted a prospective, multicenter, randomized controlled trial of patients undergoing colonoscopy at six centers. Eligible patients were randomly assigned to conventional colonoscopy (control group) or AI‐assisted colonoscopy (AI group). AI assistance was our newly developed AI system for real‐time colonoscopic polyp detection. Primary outcome is polyp detection rate (PDR). Secondary outcomes include polyps per positive patient (PPP), polyps per colonoscopy (PPC), and non‐first polyps per colonoscopy (PPC‐Plus).ResultsA total of 2352 patients were included in the final analysis. Compared with the control, AI group did not show significant increment in PDR (38.8% vs. 36.2%, p = 0.183), but its PPC‐Plus was significantly higher (0.5 vs. 0.4, p < 0.05). In addition, AI group detected more diminutive polyps (76.0% vs. 68.8%, p < 0.01) and flat polyps (5.9% vs. 3.3%, p < 0.05). The effects varied somewhat between centers. In further logistic regression analysis, AI assistance independently contributed to the increment of PDR, and the impact was more pronounced for male endoscopists, shorter insertion time but longer withdrawal time, and elderly patients with larger waist circumference.ConclusionThe intervention of AI plays a limited role in overall polyp detection, but increases detection of easily missed polyps; ChiCTR.org.cn number, ChiCTR1800015607.

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.&#x0D; 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.&#x0D; 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.