Advances In Artificial Intelligence Research Articles

Designing experiential learning activities with generative artificial intelligence tools for authentic assessment

Purpose This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning. Design/methodology/approach The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use. Findings The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes. Originality/value This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

Uncovering and Expressing our Purpose.

Generative artificial intelligence (AI) is currently a source of angst, because of its ability to give us content that sounds uncannily like a real person, and because of concern that people will not stop at using it as a tool to generate and synthesize ideas, but instead will cede control over our words, and then our thoughts. This editorial details each article in Creative Nursing Vol. 30 Issue 2, highlighting the ways in which social media, different kinds of AI, and other tools for connectivity can be used for good: finding our purpose, uniting people over long distances, expediting knowledge implementation, managing large volumes of literature, advancing health equity, and enriching nursing education.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

Predicting Airbnb pricing: a comparative analysis of artificial intelligence and traditional approaches

Airbnb's distinctive model accommodates a broad spectrum of hosts, ranging from non-professionals to traditional establishments, resulting in a nuanced pricing system that poses challenges for prediction. This paper delves into Airbnb pricing and, to address this exercise, introduces computational approaches that combine traditional linear methods and advanced artificial intelligence techniques. Utilizing real data from the Netherlands, the study specifically focuses on all active Airbnb listings until September 2019. Our contribution stands out by incorporating the financial history of diverse rental offerings, a novel aspect compared to existing literature. Unlike other studies, our investigation spans various cities in the Netherlands, providing a comprehensive view beyond individual regions. Our results highlight the robust predictive capabilities of Artificial Intelligence techniques, that lead to lower susceptibility to overfitting and to superior overall performances. However, traditional methods also prove to be valuable, aiding in determining the significance of predictors in the predicting exercise. Collectively, these approaches offer valuable insights to enhance pricing strategies and overall performances within the Airbnb market.

The consequences of generative AI for online knowledge communities

Generative artificial intelligence technologies, especially large language models (LLMs) like ChatGPT, are revolutionizing information acquisition and content production across a variety of domains. These technologies have a significant potential to impact participation and content production in online knowledge communities. We provide initial evidence of this, analyzing data from Stack Overflow and Reddit developer communities between October 2021 and March 2023, documenting ChatGPT’s influence on user activity in the former. We observe significant declines in both website visits and question volumes at Stack Overflow, particularly around topics where ChatGPT excels. By contrast, activity in Reddit communities shows no evidence of decline, suggesting the importance of social fabric as a buffer against the community-degrading effects of LLMs. Finally, the decline in participation on Stack Overflow is found to be concentrated among newer users, indicating that more junior, less socially embedded users are particularly likely to exit.

Employment of Generative Artificial Intelligence in Classroom Environments to Improve Financial Education in Secondary School Students

Financial education is considered an essential skill that enables students to effectively manage their economic resources. However, it is still at an embryonic stage in several countries, and the ability of young people to apply financial education in life contexts has not substantially improved. To address these challenges, this study proposes and evaluates a novel approach to improve financial education among high school students by using generative artificial intelligence tools. Following a quasi-experimental design, we randomly assigned a total of 110 high school students to two conditions: an experimental group that participated in learning experiences under the financial education approach using artificial intelligence tools such as ChatGPT and a control group that engaged in the same learning activities following the traditional teaching approach. The results of the Mann-Whitney U test indicate that there are significant differences between the scores of the experimental group and the control group (p=1.64E-19<0.05,  group experimental=82.92>  group control=28.08), demonstrating the effectiveness of the generative artificial intelligence approach in enhancing financial education compared to the traditional approach. Furthermore, the Kruskal-Wallis test revealed a significance p-value of less than 0.05 (p=0.000935<0.05), indicating that the use of AI significantly improves the five indicators of financial education according to the post-test evaluation phase of the experimental group. On the other hand, Dunn's test for multiple comparisons reveals a significantly greater influence of the innovative approach using artificial intelligence in the following dimensions: Financial Planning Actions, Financial Analysis Actions, Financial Behavior, and Strategic Expense Management; however, the Investment Initiative dimension shows a significantly lesser influence ( =103.46). The implementation of artificial intelligence in the classroom to promote student learning is favored by innovative approaches to pedagogical action. In this way, from the classroom, we can address the lack of skills and financial education in our students.     Received: 11 December 2023 / Accepted: 19 March 2024 / Published: 5 May 2024

A new framework for ethical artificial intelligence: keeping HRD in the loop

ABSTRACT As a result of the rapid development of technologies, artificial intelligence (AI) is changing human experiences and workplaces in ways that we are not prepared for. Recognized as a double-edged tool, AI has brought both positive and negative outcomes to organisations and employees. With its increasing popularity in the workplace and our daily practice, how to use AI intelligently and responsibly has become a top concern raised by both academics and practitioners. In this article, we discuss the desired and unintended consequences of AI as documented in current literature, examine the ethical concerns, and identify current solutions. We propose ‘keeping HRD in the loop’ as a means to addressing ethical challenges associated with AI and offer a new conceptual framework as a tool for reference. Integrating general AI principles and ethical guidelines developed by the Academy of HRD, we map out an ethical boundary consisting of nine human-centred principles and suggest a sample of six HRD interventions that can facilitate responsible AI practices and productive human-machine interfaces. By bringing HRD perspectives into the domain of AI practice, we hope to bring ethical considerations to the forefront of AI decision-making and stimulate more interest in interdisciplinary research and academic-industry collaboration.

The Prospects of Generative AI in Higher Education

Artificial intelligence (AI) has brought tremendous prospects and breakthroughs to a number of areas, including education. With an emphasis on the use of chatbots, analytics, generative AI, and personalized learning experiences, this research study offers a thorough analysis of the effects of AI on education. In order to shed light on the ethical implications, cultural considerations, language competence issues, and privacy concerns related to the use of AI in education, it explores the related limitations, obstacles, and concerns. Artificial Intelligence (AI) has the potential to completely transform higher education by promoting efficiency, creativity, customization, and engagement. Higher education could undergo a significant transition with the use of Generative Artificial Intelligence (GAI) tools as ChatGPT, Google BARD, and Bing Chat. But this integration also presents problems for avoiding plagiarism and upholding academic integrity. Within this In this work, we explore and evaluate useful strategies for effectively utilizing GAI's potential while also guaranteeing assignment integrity. We present the PAIGE (Promoting Assignment Integrity using Generative AI in Education) conceptual framework as a viable means of addressing these issues. This concept places a focus on the moral. The inclusion of GAI, encourages student engagement, and fosters chances for collaborative learning. Institutions of higher learning can efficiently use the promise of GAI while maintaining assignment integrity by utilizing the PAIGE framework. A responsible and prosperous future in education powered by generative AI is made possible by this strategy. The research report also explores the roles that parents, legislators, and educators play in minimizing the risks and optimizing the advantages of implementing AI in the classroom. Challenges with linguistic ability, privacy, and other factors related to using AI in education. Key Words: Generative AI, analytics, learning experiences, cognitive achievement, AI in the classroom, customized feedback

The synergy of skin and science – A comprehensive review of artificial intelligence’s impact on dermatology

Artificial intelligence (AI) has become an omnipresent area in modern culture. Every industry in the world has been greatly impacted by the development of technology, which has improved people’s quality of life. With the advent of AI, even 10 years old can now use smartphones to conquer the world by simplifying complex jobs. AI has made a substantial contribution to the health-care industry as well, sparking debates about whether robots may or may not eventually replace doctors in the medical field. Interestingly, AI additionally has made important advances in the field of dermatology. Through its discovery of applications that can predict a person’s skin type and the products they should use to achieve “perfect skin,” AI has greatly targeted its audience in the esthetics space, where people are most concerned with the health of their bodies and hair. AI has also developed strong relationships with these people and provided excellent advice for skin-related concerns. However, the question of whether individuals are mistreating their skin or relying too much on AI to address their skin troubles remains. Certain applications use the beauty calculator based on face symmetry, which can have a drastic impact on one’s self-confidence. These commercials may also instill false hope, and they may even be an advertising strategy used by the gods of the metaverse. Applications that give predictions regarding skin health can also create a state of anxiety in people who use them. This article examines whether AI has had a discernible effect on skin health, how it may influence cosmetic dermatology in the future, how accurate AI is in diagnosing conditions and recommending treatments, and whether we should rely on AI in the future for dermatological issues.

Synergy and collaboration with young educators and students: Insights from an open forum on generative artificial intelligence in medical education.

Synergy and collaboration with young educators and students: Insights from an open forum on generative artificial intelligence in medical education.

Public Health Students and Instructors Weigh in on Generative Artificial Intelligence: Are They on The Same Page?

Generative artificial intelligence (genAI) technology is used among students, yet it remains unclear how public health students and instructors perceive it to be effective in a learning environment. We described how and why public health students and instructors are using genAI technology along with their perceived benefits and limitations of using genAI, noting where perceptions overlap. We surveyed public health students and instructors at a higher education institution in the United States. Student survey questions covered which genAI technologies they used, which activities they used genAI for, and perceived benefits and limitations of using genAI. Questions for instructors covered which genAI technology they used, course activities genAI was integrated, and perceived benefits and limitations of using genAI. Student respondents ( n = 300) indicated using genAI technology for writing or clarifying concepts. Students and instructors ( n = 62) agreed genAI technology could save time on tedious tasks and will be part of our future workforce. They agreed that appropriate use in the classroom will better prepare future professionals. Alternatively, students and instructors indicated genAI may impede learning, produce inaccurate information, and pose opportunities for unethical behavior. While students and instructors agree on many aspects of genAI technology, instructors should be explicit about their expectations and rationale for use of genAI technology in classrooms.

Generative Artificial Intelligence in the EFL Writing Context: Students' Literacy in Perspective

The birth of generative artificial intelligence (AI) has the potential of developing language learners' English as a foreign language (EFL) writing skills; however, there is a substantial need to ensure that these learners are literate of generative AI-based tools and their potential to enhance learning writing skills. Therefore, the study aims to detect language learners' generative AI literacy in learning EFL writing skills. To achieve this purpose, the descriptive-survey method was used; (278) respondents completed a 30-closed item questionnaire and answered three questions in a semi-structured interview. The results showed that language learners’ generative AI tools literacy in the EFL writing context is at a medium level. Also, the study sample’s responses were not affected by their specialization, but by their GPA and mastery level of using generative AI tools in learning EFL writing skills did impact their responses. The analysis of the interviews revolved around three major themes: Challenges, affordance, and suggestions. In light of the findings, recommendations and suggestions are presented.

Applying Generative Artificial Intelligence to cognitive models of decision making

IntroductionGenerative Artificial Intelligence has made significant impacts in many fields, including computational cognitive modeling of decision making, although these applications have not yet been theoretically related to each other. This work introduces a categorization of applications of Generative Artificial Intelligence to cognitive models of decision making.MethodsThis categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate our proposed model in three experimental paradigms. These experiments used for model comparison involve modeling human learning and decision making based on both visual information and natural language, in tasks that vary in realism and complexity. This comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications.ResultsThe best performing model from the ablation we performed used a generative model to both create memory representations as well as predict participant actions. The results of this comparison demonstrates the importance of generative models in both forming memories and predicting actions in decision-modeling research.DiscussionIn this work, we present a model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods. These results can provide guidelines for cognitive modelers and decision making researchers interested in integrating Generative AI into their methods.

Conversational Fashion Outfit Generator Powered by GenAI

The convergence of artificial intelligence and fashion has given rise to innovative solutions that cater to the ever-evolving needs and preferences of fashion enthusiasts. This report delves into the methodology behind the development of a "Conversational Fashion Outfit Generator powered by GenAI," an advanced application that leverages the capabilities of Generative Artificial Intelligence (GenAI) to create personalized fashion outfits through natural language interactions. The model outlines the essential elements of the methodology, including data collection, natural language understanding, computer vision integration, and deep learning algorithms. Data collection forms the bedrock, as access to a diverse dataset of fashion-related information is critical for training and fine-tuning AI models. Natural Language Understanding (NLU) is instrumental in comprehending user input and generating context-aware responses, ensuring meaningful and engaging conversations. Computer vision technology is integrated to analyze fashion images, recognizing clothing items, styles, and colors, thus aiding in outfit recommendations. Deep learning algorithms, particularly recurrent and transformer-based models, form the backbone of the system, generating personalized and contextually relevant fashion suggestions. This methodology not only underpins the "Conversational Fashion Outfit Generator" but also reflects the evolving landscape of AI in the fashion industry, where personalized, interactive experiences are becoming increasingly paramount in the realm of fashion and e-commerce.

Abstract PO4-26-09: The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer

Abstract Background: Trastuzumab deruxtecan has substantially changed the treatment of HER2-low breast cancer, emphasizing the need for accurate differentiation between HER2 immunohistochemistry (IHC) scores of 0 and 1+. However, the current accuracy of HER2 IHC 0 and 1+ scoring in real-world is inadequate. Artificial intelligence (AI) has emerged as a potential solution to improve interpretation accuracy. We developed an AI algorithm based on whole slide images (WSI) to quantitatively assess HER2 expression and its role in interpreting HER2 IHC scores of 0 and 1+. Methods: Our three-phase AI analysis framework involved segmenting tumor areas (excluding ductal carcinoma in situ), detecting and classifying tumor cells based on membrane staining patterns, and grading HER2 IHC scores according to the 2018 ASCO/CAP HER2 guideline. The AI tool was trained using 6012 patches annotated by experienced pathologists. Performance evaluation was conducted using a test dataset comprising 265 slides. A total of 141 HER2 IHC slides (27 IHC 0 and 114 IHC 1+) from patients diagnosed with invasive breast cancer at Fudan University Shanghai Cancer Center in 2021 were included. Two pretrained expert pathologists independently rescored the HER2 slides, followed by analysis using the AI tool. All inconsistent cases were also reviewed by a third senior pathologist. Interobserver agreement between the pathologists and concordance between the pathologists and the AI interpretation results were assessed. We also explored potential ranges where HER2 interpretation by pathologists exhibited inconsistency or inaccuracy. Results: The HER2 AI algorithm showed high performance in interpreting all levels of HER2 expression, with an overall kappa value of 0.85. For HER2 IHC 0 and 1+ cases, the AI model demonstrated high sensitivities (0.839 and 0.914) and specificities (0.983 and 0.890) (Table 1). After the rescoring process, 51 cases were reclassified as IHC 0 and 90 cases as IHC 1+. The overall agreement rate between the historical and re-scoring results was 73.05% (103/141). The agreement rate between the AI and re-scoring results was 91.49% (129/141) (Table 2), indicating comparable interpretation capabilities between the AI model and well-trained pathologists. The interobserver agreement between the two pathologists was 83.69% (118/141), with agreement rates of 88.23% (45/51) for IHC 0 and 81.11% (73/90) for IHC 1+. Analyzing the inconsistent cases (n=12) between IHC 0 and IHC 1+, we examined the percentage of weak and incomplete expression provided by the AI. In 9 out of 12 cases, the AI provided the same results as the third senior pathologist, suggesting that AI could assist in interpretation within this range. We identified a range of 2.83% to 24.98% as a "grey-zone," where even well-trained pathologists provided inconsistent results. Conclusion: Our study demonstrates the excellent performance of an AI-based tool for scoring HER2 IHC 0 and 1+. The AI model exhibits comparable capabilities to well-trained pathologists in interpreting HER2 expression. Additionally, we identified a "grey-zone" among pathologists, highlighting the limitations of manual subjective interpretation. AI assistance within this zone may help mitigate subjective variations. Table 1. Correlation between pathologist read and AI read of HER2 immunohistochemistry. Table 2. Correlation between pathologist rescoring and AI read of HER2 immunohistochemistry. Citation Format: Ming Li, Hong Lv, Yizhi Zhao, Chenglu Zhu, Hansheng Li, Mingzhen Lin, Wen-Tao Yang. The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-26-09.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL DECISION-MAKING

In the realm of finance, decision-making processes have always been pivotal in determining the success or failure of individuals, businesses, and economies at large. With the advent of Artificial Intelligence (AI), this landscape is undergoing a profound transformation. The integration of AI technologies in financial decision-making is not merely an augmentation of existing processes but a fundamental shift in how decisions are conceived, analysed, and executed. This introduction seeks to delve into the evolving role of AI in financial decision-making, particularly within the context of the Indian financial sector. The rapid pace of technological advancement has propelled AI into the forefront of various industries, and finance is no exception. AI encompasses a spectrum of technologies, including machine learning, natural language processing, deep learning, and predictive analytics, among others. These tools offer the ability to process vast amounts of data, identify patterns, and generate insights at speeds and scales that were previously unimaginable. In the context of financial decision-making, this translates into enhanced capabilities for risk assessment, portfolio optimization, fraud detection, and customer service, among others. India, with its burgeoning economy and vibrant financial markets, stands at the cusp of this AI-driven revolution. The country's financial ecosystem, characterized by a diverse array of institutions ranging from traditional banks to fintech startups, is ripe for AI-led innovation. The adoption of AI in Indian finance is driven by several factors, including the proliferation of digital technologies, the increasing availability of data, and the imperative for efficiency and competitiveness in a globalized economy. Furthermore, initiatives such as the government's Digital India campaign and regulatory efforts to promote fintech innovation have created a conducive environment for AI adoption in the financial sector.

Abstract PO5-05-09: Testing AI-Predicted Protein Motifs that Direct Constitutive Genomic AR Activity in Endocrine Resistant Breast Cancer

Abstract Testing AI-Predicted Protein Motifs that Direct Constitutive Genomic AR Activity in Endocrine-Resistant Breast Cancer Ashfia F. Khan 1,2, Anthony S. Peidl 1,2, Shaymaa Bahnassy 3, Henry Vo2, Micah B. Castillo 2, Sarah K Herzog4,5, Suzanne AW Fuqua4,6, Preethi Gunaratne 2, Xiaolian Gao 2, Subash C. Pakhrin7, Tasneem Bawa-Khalfe1,2 1 Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 2 Department of Biology & Biochemistry, University of Houston, Houston, TX 3 Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 4 Lester & Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 5 Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, TX 6 Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 7 Department of Computer Science & Engineering Technology, University of Houston-Downtown, Houston, TX Background: Endocrine therapy (ET) remains the first-line treatment for hormone-receptor positive (HR+) breast cancer (BCa). Approximately 15–20% of HR+ BCa are intrinsically resistant to ET, and 30–40% of patients acquire resistance. Resistance to ET (ET-R) supports cancer progression with reduced disease-free survival and greater incidence of metastatic disease. Hence, therapeutic strategies for ET-R HR+ BCa remain an overarching challenge. The androgen receptor (AR) is emerging as an attractive alternative target for BCa subtypes, and elevated AR levels drive HR+ BCa progression. Targeting AR in HR+ BCa is proving difficult with preclinical studies showing conflicting results for AR antagonists. Yet clinical trials with several AR-targeting drugs are ongoing. Our recent report highlights a unique constitutively active modified AR population that drives HR+ BCa metastatic properties and is insensitive to AR inhibitors. Our current objectives are to 1) use a novel machine-learning model to predict AR modifications and 2) establish a strategy to identify patients with high modified AR levels. Methods: An advanced artificial intelligence (AI) tool and mid-throughput microfluidic peptide array technology were used to map modification domains on AR. SUMO post-translational modification of AR (SUMO-AR) was eliminated in HR+ BCa using CRISPR-Cas9 technology. RNA-seq was employed to identify a unique gene signature for SUMO-AR, and comparative bioinformatic analysis stratified patients with high versus low SUMO-AR. Results: A novel deep-learning AI platform SumoPred-PLM is trained to identify consensus, non-consensus, and SUMO2/3-specific motifs on AR. We verified SUMO2/3-specific sites on AR with a mid-throughput microfluidic peptide array. The identified SUMO2/3-acceptor site of AR is important for HR+ BCa cell pathophysiology; loss of this SUMO2/3-acceptor site impacts endogenous AR SUMOylation, cell morphology, and proliferation/apoptosis. Using both high and low SUMO-AR BCa lines, a unique SUMO-AR gene profile was established. Our SUMO-AR gene signature identifies HR+ BCa patients with greater susceptibility to metastatic progression. Conclusion: Our studies present a unique pipeline that incorporates deep-learning AI technology to identify vulnerable motifs in AR for future drug discovery. Drug screens are currently ongoing. In addition, we establish a SUMO-AR gene signature that stratifies HR+ BCa patients with high/low SUMO-AR and predicts disease progression. We expect the results could be utilized to identify responders to AR inhibitors in ongoing clinical trials. Citation Format: Ashfia Khan, Anthony Peidl, Shaymaa Bahnassy, Henry Vo, Micah Castillo, Sarah Herzog, Suzanne Fuqua, Preethi Gunaratne, Xiaolian Gao, Subash Pakhrin, Tasneem Bawa-Khalfe. Testing AI-Predicted Protein Motifs that Direct Constitutive Genomic AR Activity in Endocrine Resistant Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-05-09.

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare.

The rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable. A qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data. A 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination. We have made specific recommendations to increase conversational AI's equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots' fairness and their ability to reduce health inequalities.

FIWARE-Compatible Smart Data Models for Satellite Imagery and Flood Risk Assessment to Enhance Data Management

The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonization and smart modeling of the data to foster advanced AI analytical processes, which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the satellite imagery data and the flood risk assessment processes. The utilization of those models reinforces the fusion and homogenization of diverse information and data, facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework is developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customized and applicable easily to support the water data management processes effectively.

Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients

Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessary biopsies are a big issue in the management of BI-RADS 4 lesions with negative implications including increased medical costs, healthcare wastes, unnecessary psychological burdens to the patients, and potential complications and risks. Objectives 1. Optimize the precision breast cancer risk assessment tool, iBRISK, that utilizes artificial intelligence (AI) technologies, including natural language processing (NLP), image processing, and deep learning, with clinical risk factors and imaging features. 2. Develop a user-friendly web interface for iBRISK to facilitate clinicians or insurers in estimating cancer risk and making informed biopsy decisions for BI-RADS 4 lesions. Methods Our intelligent-augmented breast cancer risk calculator (iBRISK) model was trained on multimodal data collected from 10,778 patients, including demographic factors, historical and clinical characteristics, mammographic features, and pathologic signatures. We validated iBRISK using 4,200 patients from multiple leading hospitals, including Houston Methodist Neal Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Texas Health San Antonio MD Anderson Mays Cancer Center. The iBRISK was connected to a backend server which is linked to a frontend web user interface using technologies like Hypertext Preprocessor (PHP) for the backend application running on an APACHE HTTP server and React-JavaScript for the front-end web application that communicates with the backend using Representational State Transfer Application Programming Interface (RESTful API) and JavaScript Object Notation (JSON) format. The communication was secured using Secure Sockets Layer (SSL) encryption. Results The iBRISK model demonstrated high sensitivity in malignancy prediction and achieved an accuracy of 89.5%, area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92-0.95), sensitivity of 100%, and specificity of 81%. Only 0.16% of lesions determined to have low POM by the model were malignant. Our multi-center study shows that iBRISK achieves at least 50% reduction in unnecessary biopsies of BI-RADS 4 cases. Data elements for the required 20 features are entered into the interface using a variety of imputation methods including direct text, dropdown menus and radio button selections. The user-friendly web interface provides risk scores (0 – 1), risk levels (low, medium, and high), and associated biopsy recommendations. Conclusion The user-friendly iBRISK web interface is proposed as an adjunct to the BI-RADS system, enhancing the precision of BI-RADS 4 lesion cancer risk stratification. It is expected to reduce unnecessary biopsies, lower health costs, and enhance the quality of health care. This approach aims to tackle a critical issue in breast cancer diagnosis by leveraging advanced AI technologies and big data and providing clinicians with a tool to make more informed and precise biopsy decisions for BI-RADS 4 lesions. Citation Format: Chika Ezeana, Xiaohui Yu, Zhihao Wan, Tiancheng He, Tejal Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela Otto, Kenneth Kist, Lin Wang, Joe Ensor, Heather Speck, Ya-Chen Shih, Bumyang Kim, I-Wen Pan, David Spak, Wei Yang, Jenny Chang, Stephen Wong. An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-06.