Artificial Intelligence Interventions Research Articles

Designing an Inclusive Artificial Intelligence (AI) Curriculum for Elementary Students to Address Gender Differences With Collaborative and Tangible Approaches

This study presents an inclusive K-12 AI curriculum for elementary schools, focusing on six design principles to address gender disparities. The curriculum, designed by the researchers and an elementary teacher, uses tangible tools, and emphasizes collaboration in solving daily problems. The MANOVA results revealed initial gender differences in AI knowledge. Following MANCOVA analysis showed significant improvements in AI knowledge, as well as perceptions and behavioral intentions toward AI, effectively bridging the observed knowledge gaps without any significant differences attributed to gender or initial knowledge levels. This evidence underscores the success of tangible and collaborative AI interventions in uniformly enhancing AI knowledge and positively changing perceptions and behavioral intentions among all participants, regardless of gender. Both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning. This study contributes to the design of inclusive AI interventions, highlighting gender differences and including non-binary students as an essential part of the analysis.

Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact

AI has the potential to revolutionize mental health services by providing personalized support and improving accessibility. However, it is crucial to address ethical concerns to ensure responsible and beneficial outcomes for individuals. This systematic review examines the ethical considerations surrounding the implementation and impact of artificial intelligence (AI) interventions in the field of mental health and well-being. To ensure a comprehensive analysis, we employed a structured search strategy across top academic databases, including PubMed, PsycINFO, Web of Science, and Scopus. The search scope encompassed articles published from 2014 to 2024, resulting in a review of 51 relevant articles. The review identifies 18 key ethical considerations, including 6 ethical considerations associated with using AI interventions in mental health and wellbeing (privacy and confidentiality, informed consent, bias and fairness, transparency and accountability, autonomy and human agency, and safety and efficacy); 5 ethical principles associated with the development and implementation of AI technologies in mental health settings to ensure responsible practice and positive outcomes (ethical framework, stakeholder engagement, ethical review, bias mitigation, and continuous evaluation and improvement); and 7 practices, guidelines, and recommendations for promoting the ethical use of AI in mental health interventions (adhere to ethical guidelines, ensure transparency, prioritize data privacy and security, mitigate bias and ensure fairness, involve stakeholders, conduct regular ethical reviews, and monitor and evaluate outcomes). This systematic review highlights the importance of ethical considerations in the responsible implementation and impact of AI interventions for mental health and well-being. By addressing privacy, bias, consent, transparency, human oversight, and continuous evaluation, we can ensure that AI interventions like chatbots and AI-enabled medical devices are developed and deployed in an ethically sound manner, respecting individual rights, promoting fairness, and maximizing benefits while minimizing potential harm.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review.

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

AI-deation: When the Teacher is a Transformer in Role-Playing to create Privacy Decision Serious Games

Understanding the synergistic potential of human collaboration with artificial intelligence (AI) in creative endeavours, such as ideating Serious Game (SG) concepts, is of vital interest in our era of digital transformation. This study probes two pivotal questions: First, how does the incorporation of a GPT-4 transformer AI, assuming the role of a teacher, influence support for student teams during the ideation and balancing of SG concepts? Second, what are the students' perceptions of AI integration when co-designing these concepts with an AI in the educator role? In a between-group research design, two distinct groups engaged in a collaborative role-playing activity with digital role-specific cards and a visualised board to ideate and balance Serious Games addressing privacy decision-making. The first group, engaging in a local setting, collaborated with an AI that played the teacher role. In contrast, the second group played the co-ideation activity in a remote setting, with a human playing the role of the teacher. The findings indicate that generative AI can successfully be sourced to play the teacher role in a collaborative role-playing activity. Crucially, the timing of AI intervention thereby emerged as an essential factor that can impair creative support. Scheduled AI interventions can offer fresh insights but may not align with immediate team needs. The insights underscore the requirement to determine the most effective timing for AI intervention in human-AI co-playing ideation sessions to foster the full potential of an AI filling a role in a collaborative design process. Implications synthesised from the analytical findings and practical insights on AI-suggested design propositions/conflicts are discussed conclusively.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.

Assessment of the role of false-positive alerts in computer-aided polyp detection for assistance capabilities.

False positives (FPs) pose a significant challenge in the application of artificial intelligence (AI) for polyp detection during colonoscopy. The study aimed to quantitatively evaluate the impact of computer-aided polyp detection (CADe) systems' FPs on endoscopists. The model's FPs were categorized into four gradients: 0-5, 5-10, 10-15, and 15-20 FPs per minute (FPPM). Fifty-six colonoscopy videos were collected for a crossover study involving 10 endoscopists. Polyp missed rate (PMR) was set as primary outcome. Subsequently, to further verify the impact of FPPM on the assistance capability of AI in clinical environments, a secondary analysis was conducted on a prospective randomized controlled trial (RCT) from Renmin Hospital of Wuhan University in China from July 1 to October 15, 2020, with the adenoma detection rate (ADR) as primary outcome. Compared with routine group, CADe reduced PMR when FPPM was less than 5. However, with the continuous increase of FPPM, the beneficial effect of CADe gradually weakens. For secondary analysis of RCT, a total of 956 patients were enrolled. In AI-assisted group, ADR is higher when FPPM≤5 compared with FPPM>5 (CADe group: 27.78% vs 11.90%; P=0.014; odds ratio [OR], 0.351; 95% confidence interval [CI], 0.152-0.812; COMBO group: 38.40% vs 23.46%, P=0.029; OR, 0.427; 95% CI, 0.199-0.916). After AI intervention, ADR increased when FPPM≤5 (27.78% vs 14.76%; P=0.001; OR, 0.399; 95% CI, 0.231-0.690), but no statistically significant difference was found when FPPM>5 (11.90% vs 14.76%, P=0.788; OR, 1.111; 95% CI, 0.514-2.403). The level of FPs of CADe does affect its effectiveness as an aid to endoscopists, with its best effect when FPPM is less than 5.

Exploring the Role of Artificial Intelligence in Enhancing Equity and Inclusion in Education

The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. Artificial Intelligence (AI) has emerged as a transformative force in education, promising to revolutionize traditional teaching and learning methods. One critical aspect of this transformation is AI's potential to enhance equity and inclusion in educational settings. This paper explores the current state, challenges, and opportunities regarding AI's role in promoting equity and inclusion in education. The historical evolution of AI in education is examined, tracing its roots from early intelligent tutoring systems to contemporary adaptive learning platforms and virtual tutoring systems. Advances in machine learning, natural language processing, and data analytics have expanded AI's capabilities, enabling personalized learning experiences tailored to individual student needs. However, the widespread implementation of AI in education faces several challenges, including concerns about data privacy, algorithmic bias, and the digital divide. It is crucial to address these challenges through responsible and ethical AI deployment, ensuring that AI interventions prioritize equity, inclusivity, and transparency. Further research is needed to explore the effectiveness of AI interventions in different educational contexts and to develop strategies for mitigating potential risks and maximizing benefits. By leveraging AI technologies thoughtfully and ethically, educators and policymakers can work towards building a more equitable and inclusive education system that empowers all learners to reach their full potential.

Artificial intelligence in surgery.

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

Enhancing Digital Learning Outcomes Through the Application of Artificial Intelligence: A Comprehensive Review

This research investigates the application of artificial intelligence (AI) in digital learning environments and its impact on learning outcomes. A comprehensive review of literature was conducted, encompassing studies from various educational levels and settings. The analysis reveals promising findings regarding the effectiveness of AI interventions, including intelligent tutoring systems, adaptive learning platforms, virtual assistants, and content recommendation systems, in enhancing learning outcomes. Learners exhibit high levels of engagement and satisfaction with AI-enhanced learning environments, appreciating the interactive features and personalized support provided by AI technologies. However, challenges and limitations associated with AI implementation, such as technical issues, privacy concerns, and ethical considerations, were identified. The research contributes valuable insights into the potential benefits and risks of AI in education, with implications for both research and practice. Future research directions include optimizing AI algorithms, exploring ethical and social implications, and addressing educator training needs to ensure the successful integration of AI technologies into teaching and learning processes.

Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis.

Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.

The educational value of artificial intelligence in higher education: a 10-year systematic literature review

Purpose This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia. Design/methodology/approach A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education. Findings The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated. Research limitations/implications The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education. Practical implications This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders. Originality/value This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.

The Study of Artificial Marketing tools used in Indian Cosmetic Industry and its impact on Consumer Behaviour

This research paper investigates the profound influence of Artificial Intelligence (AI) marketing tools on consumer buying behaviours within the burgeoning landscape of the cosmetic industry. With technology becoming increasingly intertwined with beauty and personal care, this study aims to dissect the multifaceted relationship between AI interventions and consumer choices.&#x0D; Through a comprehensive review of existing literature, the research seeks to identify patterns and trends in how AI-driven marketing strategies shape the decision-making processes of cosmetic consumers. The study focuses on a spectrum of AI tools employed in the industry, including virtual try-on experiences, personalized product recommendations, and AI-powered chatbots, exploring their impact on consumer perceptions, preferences, and purchasing decisions.&#x0D; The descriptive analysis will unveil the nuances of how AI algorithms interpret and respond to individual consumer data, tailoring marketing strategies to align with diverse beauty preferences. Furthermore, the research will explore the role of augmented reality in transforming the cosmetic shopping experience, offering consumers a virtual lens into product usage before making informed choices.&#x0D; By shedding light on the intricate interplay between AI marketing tools and consumer behaviours in the cosmetic industry, this paper aims to provide valuable insights for cosmetic brands, marketers, and policymakers. Understanding the dynamics of this relationship is crucial for the industry's stakeholders as they navigate the evolving landscape of beauty and technology, ensuring that AI interventions not only meet consumer expectations but also contribute positively to the overall cosmetic purchasing experience.

Performative Ethics From Within the Ivory Tower: How CS Practitioners Uphold Systems of Oppression

This paper analyzes where Artificial Intelligence (AI) ethics research fails and breaks down the dangers of well-intentioned but ultimately performative ethics research. A large majority of AI ethics research is criticized for not providing a comprehensive analysis of how AI is interconnected with sociological systems of oppression and power. Our work contributes to the handful of research that presents intersectional, Western systems of oppression and power as a framework for examining AI ethics work and the complexities of building less harmful technology; directly connecting technology to named systems such as capitalism and classism, colonialism, racism and white supremacy, patriarchy, and ableism. We then explore current AI ethics rhetoric’s effect on the AI ethics domain. We conclude by providing an applied example to contextualize intersectional systems of oppression and AI interventions in the US justice system and present actionable steps for AI practitioners to participate in a less performative, critical analysis of AI. This article appears in the AI &amp; Society track.

Examining Ghana's National Health Insurance Act, 2003 (Act 650) to Improve Accessibility of Artificial Intelligence Therapies and Address Compensation Issues in Cases of Medical Negligence

Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding automated healthcare systems. &#x0D; Methods: Legal and regulatory analysis of Act 650 were conducted, review of academic literature on global uptake of AI interventions and medical negligence principles were elucidated, examination of case studies implementing pilot AI therapy programs under insurance schemes were considered.&#x0D; Results &amp; Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues. Proposed amendments to reimburse certain AI therapies through the National Health Insurance Scheme, expand certified provider eligibility, and institute transparent negligence compensation formulas.&#x0D; Recommendations: Reform Act 650 to support increased appropriate use of AI healthcare services, protect patients undergoing automated diagnosis/treatment, and clarify liability rules for medical negligence incidents relating to AI.&#x0D; Novelty &amp; Significance: First extensive analysis focused on opportunities for Ghana’s health insurance framework to catalyze equitable diffusion of advanced AI therapeutics and address emerging legal challenges and safety risks as automated medicine advances.

Artificial Intelligence in nursing: trustworthy or reliable?

Trustworthiness in Artificial Intelligence (AI) innovation is a priority for governments, researchers and clinicians; however, clinicians have highlighted trust and confidence as barriers to their acceptance of AI within a clinical application. While there is a call to design and develop AI that is considered trustworthy, AI still lacks the emotional capability to facilitate the reciprocal nature of trust. This paper aims to highlight and discuss the enigma of seeking or expecting trust attributes from a machine and, secondly, reframe the interpretation of trustworthiness for AI through evaluating its reliability and validity as consistent with the use of other clinical instruments. AI interventions should be described in terms of competence, reliability and validity as expected of other clinical tools where quality and safety are a priority. Nurses should be presented with treatment recommendations that describe the validity and confidence of prediction with the final decision for care made by nurses. Future research should be framed to better understand how AI is used to deliver care. Finally, there is a responsibility for developers and researchers to influence the conversation about AI and its power towards improving outcomes. The sole focus on demonstrating trust rather than the business-as-usual requirement for reliability and validity attributes during implementation phases may result in negative experiences for nurses and clinical users. This research will have significant implications for the way in which future nursing is practised. As AI-based systems become a part of routine practice, nurses will be faced with an increasing number of interventions that require complex trust systems to operate. For any AI researchers and developers, understanding the complexity of trust and creditability in the use of AI in nursing will be crucial for successful implementation. This research will contribute and assist in understanding nurses' role in this change.

AI for Sustainable Development: Addressing Environmental and Social Challenges

The integration of artificial intelligence (AI) technologies holds significant promise in addressing pressing environmental and social challenges, thus contributing to sustainable development efforts worldwide. This article provides a comprehensive overview of the role of AI in tackling various aspects of sustainability, including environmental conservation, resource management, climate change mitigation, and social equity. By leveraging AI techniques such as machine learning, optimization, and data analytics, innovative solutions are being developed to monitor ecosystems, optimize energy consumption, enhance agricultural practices, and promote social inclusion. However, alongside these opportunities, there are also ethical, regulatory, and socio-economic considerations that must be carefully addressed to ensure that AI interventions contribute positively to sustainable development goals. This paper highlights recent advancements, challenges, and future directions in utilizing AI for sustainable development, emphasizing the importance of interdisciplinary collaboration and stakeholder engagement in realizing the full potential of AI-enabled solutions.

Digital Technology: A Game Changer in Vegetable Cultivation

Vegetables are important constituents of Indian agriculture and nutritional security due to their short duration, high yielding capacity, nutritional richness, economic viability and ability to generate on farm and off farm employment. Increasing per capita income, health consciousness, urbanisation, increasing working women, shifting of farmers to high value vegetables are also important ingredients for fuelling vegetable growth in India. But we are still not getting the required /capita vegetables/day.so the answer to increasing the production of various vegetables employing various digital technologies is required. Growing high yielding varieties/hybrids and applying new production methods can significantly increase yield in most vegetables. The application of digital technologies to integrate agricultural output from the paddock to the customer is known as digital agriculture. These technologies can give the agricultural business the tools and data it needs to make better decisions and increase productivity). Information and communication technology (ICTs) and Artificial intelligence (AI) are the two key technologies used in digital agriculture. Information and communication technology (ICT) can be defined as a collection of technologies that aid or support the storage, processing, or distribution of data/information.Despite a big, well-educated, well-trained, and well-organized agricultural extension workforce, over 60% of farmers in the country remain unreached, with no extension organisation serving them. Radio and television are the primary sources of information. e- Extension, Agrisnet, Digital green, e- Sagu, Agmarknet, iKisan, Village Knowledge Centers, Kisan Call Centers, SMS Portal/mKisan portal are some of the e-Agriculture efforts in India. Furthermore, information and communication technology (ICT) aid in the timely completion of decisions, as well as the discovery of the best solutions and efficient systems for water management and irrigation in order to maximise production Artificial intelligence is assisting several agricultural sectors in increasing output and efficiency. In every industry, AI technologies are help in overcoming traditional hurdles. AI intervention in agriculture is assisting farmers in increasing their farming efficiency while reducing harmful environmental impacts. The agriculture business has embraced AI with open arms in order to improve overall results.AI is changing the way food is produced, resulting in a 20% reduction in agriculture sector emissions. Incorporating AI technology into agriculture, aids in the control and management of any unwelcome natural occurrence. It consists of robotic process automation, a machine learning platform, and sensors, among other things. Harvesting, weed management, pest and disease monitoring, irrigation control, and predictive agricultural analytics are all possible with these. Artificial intelligence and information and communication technologies (ICT) have the potential to be a technological revolution and a boom in agriculture, helping to feed the world's growing population.

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology.

Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Assistant (DL-ODA), a fully automated, end-to-end clinical platform that enables AI interventions for any disease site featuring an automated model-training pipeline, auto-segmentations, and QA reporting. We developed, tested, and prospectively deployed the DL-ODA system at a large university affiliated hospital center. Medical professionals activate the DL-ODA via two pathways (1): On-Demand, used for immediate AI decision support for a patient-specific treatment plan, and (2) Ambient, in which QA is provided for all daily radiotherapy (RT) plans by comparing DL segmentations with manual delineations and calculating the dosimetric impact. To demonstrate the implementation of a new anatomy segmentation, we used the model-training pipeline to generate a breast segmentation model based on a large clinical dataset. Additionally, the contour QA functionality of existing models was assessed using a retrospective cohort of 3,399 lung and 885 spine RT cases. Ambient QA was performed for various disease sites including spine RT and heart for dosimetric sparing. Successful training of the breast model was completed in less than a day and resulted in clinically viable whole breast contours. For the retrospective analysis, we evaluated manual-versus-AI similarity for the ten most common structures. The DL-ODA detected high similarities in heart, lung, liver, and kidney delineations but lower for esophagus, trachea, stomach, and small bowel due largely to incomplete manual contouring. The deployed Ambient QAs for heart and spine sites have prospectively processed over 2,500 cases and 230 cases over 9 months and 5 months, respectively, automatically alerting the RT personnel. The DL-ODA capabilities in providing universal AI interventions were demonstrated for On-Demand contour QA, DL segmentations, and automated model training, and confirmed successful integration of the system into a large academic radiotherapy department. The novelty of deploying the DL-ODA as a multi-modal, fully automated end-to-end AI clinical implementation solution marks a significant step towards a generalizable framework that leverages AI to improve the efficiency and reliability of RT systems.

Engagement with mHealth Alcohol Interventions: User Perspectives on an App or Chatbot-Delivered Program to Reduce Drinking.

Research suggests participant engagement is a key mediator of mHealth alcohol interventions' effectiveness in reducing alcohol consumption among users. Understanding the features that promote engagement is critical to maximizing the effectiveness of mHealth-delivered alcohol interventions. The purpose of this study was to identify facilitators and barriers to mHealth alcohol intervention utilization among hazardous-drinking participants who were randomized to use either an app (Step Away) or Artificial Intelligence (AI) chatbot-based intervention for reducing drinking (the Step Away chatbot). We conducted semi-structured interviews from December 2019 to January 2020 with 20 participants who used the app or chatbot for three months, identifying common facilitators and barriers to use. Participants of both interventions reported that tracking their drinking, receiving feedback about their drinking, feeling held accountable, notifications about high-risk drinking times, and reminders to track their drinking promoted continued engagement. Positivity, personalization, gaining insight into their drinking, and daily tips were stronger facilitator themes among bot users, indicating these may be strengths of the AI chatbot-based intervention when compared to a user-directed app. While tracking drinking was a theme among both groups, it was more salient among app users, potentially due to the option to quickly track drinks in the app that was not present with the conversational chatbot. Notification glitches, technology glitches, and difficulty with tracking drinking data were usage barriers for both groups. Lengthy setup processes were a stronger barrier for app users. Repetitiveness of the bot conversation, receipt of non-tailored daily tips, and inability to self-navigate to desired content were reported as barriers by bot users. To maximize engagement with AI interventions, future developers should include tracking to reinforce behavior change self-monitoring and be mindful of repetitive conversations, lengthy setup, and pathways that limit self-directed navigation.