Generative AI with RDS as a Vector Store

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

As organizations increasingly navigate the complexities of the digital age, the incorporation of artificial intelligence (AI) into knowledge management (KM) frameworks is becoming indispensable for the attainment of sustained competitive advantage. This chapter explores the transformative capabilities of AI within KM, underscoring the pivotal significance of human–AI collaboration. We will investigate how emerging technologies, including AI, machine learning (ML) and natural language processing (NLP), can enhance key KM dimensions: the creation, storage, retrieval, sharing and application of knowledge. Furthermore, the chapter considers the essential contributions of human expertise, contextual understanding and decision-making in KM practices. By elucidating the synergy between the advantages of AI, like scalability, data analysis, human competencies, creativity and empathy, the chapter further explores specific implementations of AI–human collaboration, encompassing automated knowledge repositories for knowledge generation, NLP for knowledge retrieval and tailored recommendations for knowledge application. We will also address the ethical challenges and implications that accompany AI integration, focusing on issues such as bias, the need for transparency, trust in AI systems, data privacy and the balance between human oversight and AI control. Finally, we outline practical implications for organizations aiming to empower talent, create AI-friendly infrastructures and streamline knowledge processes to foster successful human–AI partnerships. We conclude by discussing the future of AI–human collaboration in KM, identifying emerging trends and the long-term benefits of integrating AI into organizational learning and innovation.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

BACKGROUND In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields.However, there remains a gap between technological potential and practical application, necessitating the establishment of a scientific evaluation system.Despite some existing research beginning to conduct clinical application assessments of generative AI dialogue systems, these efforts are largely limited to testing individual models on single tasks, lacking horizontal comparative analysis across multiple dialogue models and validation of continuous decision chains in real clinical scenarios.As generative artificial intelligence (AI) systems play an increasingly extensive role in the field of Medicine and Pharmacy, we need more research to explore this area. OBJECTIVE To systematically evaluate and compare the performance of eight mainstream generative AI systems, both domestic and international, across four core clinical pharmacy practice scenarios: medication consultation, medication education, prescription review, and case analysis with pharmaceutical care. This study aims to quantitatively assess their capabilities in addressing common clinical pharmacy practice problems. METHODS Assessment questions were systematically extracted from medication consultation clinic records, real clinical cases, and clinical pharmacist standardized training examination databases. Three researchers tested eight different generative AI systems on the same day using standardized "inquiry prompts." A double-blind scoring design was employed, with six experienced clinical pharmacists with extensive clinical backgrounds evaluating the AI responses on a 0-10 scale across six dimensions: accuracy, rigor, applicability, logical coherence, conciseness, and universality. Statistical analysis used one-way analysis of variance (ANOVA) to compare score differences between systems, with multiple comparison tests for significant results, and intraclass correlation coefficient (ICC) calculations to assess inter-rater consistency.Systematic descriptive evaluations of the AI-generated responses were also conducted. RESULTS DeepSeek-R1 demonstrated the best overall performance across all four task categories. Qwen, GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro performed slightly inferior to DeepSeek-R1. Doubao and Kimi showed inconsistent performance, while ERNIE Bot performed the poorest. Comprehensive evaluation indicated that responses from existing generative AI systems still have certain limitations and should be used as clinical reference tools rather than independent clinical decision-making bases. Inter-rater consistency analysis showed good agreement (ICC>0.75) in evaluating medication consultation, medication education, and prescription review. However, the lowest consistency level (ICC=0.70) was observed in assessing the conciseness of case analysis and pharmaceutical care, reflecting significant cognitive differences among raters regarding evaluation standards for these complex issues. CONCLUSIONS The DeepSeek-R1 model demonstrates significant potential as a supportive decision-making tool in clinical pharmacy practice. However, overall, the current generative AI systems still require systematic improvement and refinement in their ability to handle multidimensional complex clinical pharmacy problems. CLINICALTRIAL none

Publications on the application of artificial intelligence (AI) to many situations, including those in clinical medicine, created in 2023-2024 are reviewed here. Because of the short time frame covered, here, it is not possible to conduct exhaustive analysis as would be the case in meta-analyses or systematic reviews. Consequently, this literature review presents an examination of narrative AI's application in relation to contemporary topics related to clinical medicine. The landscape of the findings reviewed here span 254 papers published in 2024 topically reporting on AI in medicine, of which 83 articles are considered in the present review because they contain evidence-based findings. In particular, the types of cases considered deal with AI accuracy in initial differential diagnoses, cancer treatment recommendations, board-style exams, and performance in various clinical tasks, including clinical imaging. Importantly, summaries of the validation techniques used to evaluate AI findings are presented. This review focuses on AIs that have a clinical relevancy evidenced by application and evaluation in clinical publications. This relevancy speaks to both what has been promised and what has been delivered by various AI systems. Readers will be able to understand when generative AI may be expressing views without having the necessary information (ultracrepidarianism) or is responding as if the generative AI had expert knowledge when it does not. A lack of awareness that AIs may deliver inadequate or confabulated information can result in incorrect medical decisions and inappropriate clinical applications (Dunning-Kruger effect). As a result, in certain cases, a generative AI system might underperform and provide results which greatly overestimate any medical or clinical validity.

Continual learning in medical devices: FDA's action plan and beyond

Evaluating generative AI responses to real-world drug-related questions

The International Conference on Information Systems and Artificial Intelligence [ISAI] is an academic conference on the advancement of artificial intelligence and information systems. The conference includes the fields of machine vision, natural language processing, speech recognition and the industry application of AI, and is of great significance to the development of intelligent machines, intelligent human-computer interaction, and effective use of information, human health and social security.The primary goal of the conference is to promote research and developmental activities in information system and artificial intelligence and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in information system and artificial intelligence and related areas.The 2020 5th Annual International Conference on Artificial Intelligence and Information Systems received 608 full-paper and abstract submissions from China and many other countries or regions over the world. Through a rigorous peer-review process, all submissions were refereed based on their quality of content, level of innovation, significance, originality and legibility. The proceedings are published by IOP Conference Series: Journal of Physics: Conference Series (JPCS).We would like to thank all the authors, speaker and participants for their great contributions to the success of ISAI2020 and to express our sincere gratitude to all the members of Technical Program Committee and organizers for their enthusiasm, time, and expertise.ISAI 2020 Organizing Committee

The International Conference on Information Systems and Artificial Intelligence is an academic conference on the advancement of artificial intelligence and information systems. The conference includes the fields of machine vision, natural language processing, speech recognition and the industry application of AI, and is of great significance to the development of intelligent machines, intelligent human-computer interaction, and effective use of information, human health and social security.The primary goal of the conference is to promote research and developmental activities in information system and artificial intelligence and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in information system and artificial intelligence and related areas. In order to further promote the development of artificial intelligence and information systems disciplines and strengthen domestic and international academic exchanges and cooperation.

Aim/Purpose In a post-pandemic learning environment, artificial intelligence (AI) may become the new standard. More and more college students are utilizing AI tools like ChatGPT and Bard to enhance their learning. This study compares the use of web-search and AI-based systems to assess how much students' knowledge has improved in an Internet Marketing course. Background While authorship and academic integrity have received a lot of attention in higher education research thus far, using AI effectively provides several benefits for both teaching and learning. In other words, by utilizing these capabilities, AI-based systems may boost students' motivation, interest, and level of knowledge. Methodology The course will teach the students how to use the application of AI (i.e. ChatGPT). To evaluate the efficacy of AI technology, this study will conduct empirical research to compare the knowledge improvement of students who would obtain knowledge about buying sustainable production products in Taiwan between using the web-search systems and the AI-based systems. The assessments of students' knowledge growth are evaluated by using a dependent samples t-test. Contribution This finding expands on prior research examining the possibility of using AI-powered learning aids to inspire students (Al Shamsi et al., 2022; Lund et al., 2023). The ability of ChatGPT to deliver cutting-edge content, personalized feedback, and interactive learning sessions may all contribute to generating curiosity, a sense of accomplishment, and ultimately a greater desire to learn more about subjects. Findings It is anticipated that the AI chatbots created for information delivery might have something to do with students' increased interest in, motivation for, and understanding of their studies. This study examines the potential benefits to students' understanding of sustainable development goals with an AI-specific method of information provision relevant to purchasing items produced sustainably. Recommendations for Practitioners In the near run, using AI systems has been proven to boost university students' sentiments. University students may feel more supported by AI when they use it more regularly. Long-term AI use may cause reliance, especially in the absence of human companionship. Recommendations for Researchers Not all students and instructors have a lot of experiences in teaching and learning with generative AI, despite the fact that it is a relatively new technology. After generative AI is completely incorporated into classrooms, the roles of AI in teaching and learning could reconsider. Impact on Society In order to prepare students for work in a world driven by generative AI, higher education in the future needs change. With an emphasis on in-class and hands-on activities for assessment, new learning outcomes—skills in learning and teaching with AI, AI literacy—and the importance of interdisciplinarity should be highlighted. Future Research Future studies should concentrate on the following topics: interdisciplinary education, creative pedagogies and their evaluation, new assessment and its acceptability.

This study presents a comprehensive framework designed to enhance data security in artificial intelligence (AI) systems by integrating robust cybersecurity measures with information governance principles. Employing a convergent parallel mixed-methods design, the research combines a quantitative meta-analysis of 11,245 AI deployments, a qualitative synthesis of 89 governance studies, and technical validations across three open-source AI models (BERT, YOLOv7, and federated learning). Critical threats, including data poisoning (42% success rate), adversarial examples (23% higher vulnerability in vision models), and model inversion (31% more frequent in generative AI), were identified. The proposed framework demonstrated a Composite Security Score (CSS) of 0.87, outperforming existing models (CSS range: 0.78–0.82), and achieved 84–89% attack mitigation and 90–95% regulatory compliance under standards like GDPR and NIST RMF. The framework’s practical contribution lies in offering sector-specific, scalable solutions for strengthening AI system security, thereby enabling healthcare, finance, and public sector organizations to align innovation with ethical and legal obligations. Theoretically, the study advances knowledge by bridging cybersecurity engineering with governance structures, addressing a critical gap in AI risk management. Preliminary evaluations into quantum-ready encryption strategies were explored, though more extensive testing is suggested for future research. Key limitations include reliance on secondary datasets, potential model generalizability constraints, and emerging threats like quantum attacks that require ongoing adaptation. Recommendations advocate sector-specific adoption, quantum-ready encryption, and research into adaptive security and third-party risks to ensure scalable, ethical AI security.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.

As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.