Relevant bibliographies by topics / Healthcare Analytics and AI Integration

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Healthcare Analytics and AI Integration'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Healthcare Analytics and AI Integration"

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Pendyala, Santhosh Kumar. "Advanced Healthcare Data Analytics: A Cloud- AI Integrated Framework for Enhanced Clinical Outcomes." International Journal of Advanced Robotics and Automation 7, no. 1 (2024): 1–8. https://doi.org/10.15226/2473-3032/7/1/00144.

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The integration of cloud computing and artificial intelligence (AI) in healthcare data analytics has revolutionized patient care by enabling realtime, data-driven decision-making. Recent studies demonstrate significant improvements in predictive diagnostics and treatment personalization through such integrations. For instance, the implementation of AI-driven analytics within cloud infrastructures has led to a 72.3% enhancement in data processing efficiency, facilitating timely clinical interventions. Moreover, the adoption of machine learning algorithms in cloud-based systems has achieved pred
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Researcher. "TRANSFORMING HEALTHCARE THROUGH DATA ENGINEERING, PREDICTIVE ANALYTICS, AND AI MODELS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1710–18. https://doi.org/10.5281/zenodo.14265470.

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This article explores how artificial intelligence, predictive analytics, and data engineering have revolutionized contemporary healthcare systems. It investigates how these integrated technologies transform clinical decision-making procedures, operational effectiveness, and patient care delivery. The article examines several topics, such as the fundamentals of data engineering, predictive analytics, AI model architectures, the integration of real-time analytics, and clinical applications. With an emphasis on technical difficulties, data quality control, system integration, and regulatory compl
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Tamar Lekiashvili, Tamar Lekiashvili. "THE INTEGRATION OF ARTIFICIAL INTELLIGENCE WITH THE PROCESSES IN HEALTHCARE." Economics 106, no. 1-2 (2024): 39–44. http://dx.doi.org/10.36962/ecs106/1-2/2024-39.

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This article explores the integration of Artificial Intelligence (AI) with Business Process Management (BPM) in healthcare. It examines how AI technologies, such as diagnostic decision support, process automation, and predictive analytics, enhance BPM strategies to streamline workflows, improve decision-making, and facilitate continuous process improvement. The discussion highlights opportunities for optimizing healthcare processes, enhancing patient care, and driving operational efficiency through the convergence of AI and BPM. However, the challenges related to data privacy, regulatory compl
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Fardin Sabahat Khan, Abdullah Al Masum, Jamaldeen Adam, Md Rashidul Karim, and Sadia Afrin. "AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics." Journal of Computer Science and Technology Studies 6, no. 5 (2024): 85–93. http://dx.doi.org/10.32996/jcsts.2024.6.5.8.

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This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findin
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Allahham, Mahmoud, Abdel-Aziz Ahmad Sharabati, Heba Hatamlah, Ahmad Yahiya Bani Ahmad, Samar Sabra, and Mohammad Khalaf Daoud. "Big Data Analytics and AI for Green Supply Chain Integration and Sustainability in Hospitals." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 19 (December 15, 2023): 1218–30. http://dx.doi.org/10.37394/232015.2023.19.111.

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This paper examines how big data analytics and AI improve hospital supply chain sustainability. Hospitals are recognizing the need for eco-friendly operations due to environmental issues and rising healthcare needs. It analyzes data from 68 UK hospitals using a conceptual model and partial least squares regression-based structural equation modeling. The research begins by examining hospital supply networks' environmental impact. Energy use, trash, and transportation emissions are major issues. It then explains how big data analytics and AI can transform these implications. This study prioritiz
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Jalil, Muhammad Saqib, Esrat Zahan Snigdha, Mohammad Tonmoy Jubaear Mehedy, et al. "AI-Powered Predictive Analytics in Healthcare Business: Enhancing Operational Efficiency and Patient Outcomes." American Journal of Medical Sciences and Pharmaceutical Research 07, no. 03 (2025): 93–114. https://doi.org/10.37547/tajmspr/volume07issue03-13.

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The implementation of AI-powered predictive analytics within healthcare business operations is transforming medical practices through improved operational performance and better clinical results. The research examines how algorithms from machine learning combined with deep learning methods and real-time data processing systems enable better decisions in clinical settings and resource management along with advanced patient care methods. The research employs both practical applications and scientific study of empirical evidence to evaluate the ability of predictive AI models in healthcare to dec
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Collins Nwannebuike Nwokedi, Olakunle Saheed Soyege, Obe Destiny Balogu, et al. "Big Data Analytics and Artificial Intelligence in Healthcare: Transforming Diagnostics, Treatment, and Disease Prevention." International Journal of Scientific Research in Science and Technology 11, no. 6 (2024): 1035–60. https://doi.org/10.32628/ijsrst25121245.

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The integration of Big Data Analytics and Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, treatment, and disease prevention. This paper explores how these advanced technologies enhance clinical decision-making, improve patient outcomes, and optimize healthcare processes. By leveraging vast datasets, AI-driven algorithms facilitate early disease detection, predictive analytics, and personalized medicine, significantly reducing diagnostic errors and enabling timely interventions. Furthermore, machine learning models assist in tailoring treatment plans based on patient-
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Talati, Dhruvitkumar. "AI in healthcare domain." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (2023): 256–62. http://dx.doi.org/10.60087/jklst.vol2.n3.p253.

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Artificial Intelligence (AI) has emerged as a transformative force in the healthcare domain, revolutionizing various aspects of medical research, diagnostics, treatment, and patient care. This paper provides an overview of recent developments and applications of AI in healthcare, highlighting its potential to enhance efficiency, accuracy, and accessibility in medical practices. The integration of machine learning algorithms, natural language processing, and computer vision techniques has enabled AI systems to analyze vast amounts of medical data, support clinical decision-making, and personali
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Talati, Dhruvitkumar. "AI in healthcare domain." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (2023): 256–62. http://dx.doi.org/10.60087/jklst.vol2.n3.p262.

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Artificial Intelligence (AI) has emerged as a transformative force in the healthcare domain, revolutionizing various aspects of medical research, diagnostics, treatment, and patient care. This paper provides an overview of recent developments and applications of AI in healthcare, highlighting its potential to enhance efficiency, accuracy, and accessibility in medical practices. The integration of machine learning algorithms, natural language processing, and computer vision techniques has enabled AI systems to analyze vast amounts of medical data, support clinical decision-making, and personali
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Tolulope, Olagoke Kolawole, Yetunde Mustapha Ashiata, Obianuju Mbata Akachukwu, Olamide Tomoh Busayo, and Yeboah Forkuo Adelaide. "A Systematic Review of Predictive Analytics Applications in Early Disease Detection and Diagnosis." Engineering and Technology Journal 10, no. 03 (2025): 4265–83. https://doi.org/10.5281/zenodo.15100306.

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The integration of predictive analytics and artificial intelligence (AI) in healthcare has revolutionized early disease detection and diagnosis, significantly improving patient outcomes and reducing healthcare costs. This systematic review examines the applications of predictive analytics in early-stage disease identification, focusing on AI-driven methodologies, machine learning (ML) algorithms, and big data analytics. By leveraging real-time patient data, electronic health records (EHRs), and genomic information, predictive models enhance diagnostic accuracy, facilitate timely interventions,
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Dissertations / Theses on the topic "Healthcare Analytics and AI Integration"

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Reda, Roberto. "A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14645/.

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With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective
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Books on the topic "Healthcare Analytics and AI Integration"

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Lacasa, Pilar, Alicia Hernando, and Alba García-Vega. Empowering Social Network Analytics Through Software Integration and AI. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036221546.

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Gupta, Shashi Kant, Dimitrios A. Karras, and Rajesh Natarajan, eds. Revolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65022-2.

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Mala, D. Jeya. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. IGI Global, 2022.

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Jeya Mala, D., ed. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9132-1.

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Mala, D. Jeya. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. IGI Global, 2022.

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Mala, D. Jeya. Integrating AI in Iot Analytics on the Cloud for Healthcare Applications. IGI Global, 2021.

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Mala, D. Djeya. Handbook of Research on Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. IGI Global, 2022.

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Dodd, John C. Healthcare IT Transformation: Bridging Innovation, Integration, Interoperability, and Analytics. Taylor & Francis Group, 2016.

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Dodd, John C. Healthcare IT Transformation: Bridging Innovation, Integration, Interoperability, and Analytics. Productivity Press, 2016.

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Healthcare IT Transformation: Bridging Innovation, Integration, Interoperability, and Analytics. Productivity Press, 2016.

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Book chapters on the topic "Healthcare Analytics and AI Integration"

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Kannan, A., B. Justus Rabi, and M. Anand. "Integration of AI in Insurance and Healthcare: What Does It Mean?" In Machine Learning for Business Analytics. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003206316-6.

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Owoyemi, Ayomide, Eugeniah Arthur, Tope Ladi-Akinyemi, Yemisi Babalola, and Damian Okaibedi Eke. "Trustworthy AI in Healthcare: Exploring Ethics in Digital Health Technologies in Nigeria." In Trustworthy AI. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75674-0_9.

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Abstract The rapid expansion of digital health solutions in Africa, encompassing telemedicine, AI, and other technologies, aligns with WHO’s Goals for Sustainable Development and Universal Health Coverage. Despite its benefits, this growth raises ethical concerns regarding deploying these technologies. A cross-sectional survey targeting executives of Nigerian digital health startups was conducted using Google Forms. The survey focused on startup characteristics, data management, ethical/legal governance, and user engagement. Data analysis employed descriptive statistics and cross-tabulation in
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Visan, Anita Ioana, and Irina Gabriela Negut. "AI Algorithms in Sustainable Healthcare." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9725-1.ch001.

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The integration of artificial intelligence (AI) into healthcare is vital for sustainability. This overview highlights how AI enhances sustainable practices and improves patient care amid rising costs and resource scarcity. Technologies like machine learning (ML), natural language processing (NLP), and predictive analytics streamline operations, boost diagnostic accuracy, and personalize treatment plans. AI's ability to analyze large datasets helps providers make data-driven decisions, improving care quality. It plays a key role in medical imaging, supply chain management, and patient engagemen
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Pesqueira, Antonio, Maria José Sousa José Sousa, Andreia de Bem Machado Bem Machado, Sama Bolog, Luiz Vieira, and Ioana Bolog. "ADHD Healthcare Intelligence." In Advances in Business Information Systems and Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1210-0.ch008.

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Technological advancements are revolutionizing the healthcare sector, notably in the treatment and management of attention deficit/hyperactivity disorder (ADHD). This shift from traditional therapeutic and pharmacological interventions towards a more data-driven approach marks a significant change in ADHD care. The integration of big data (BD) and artificial intelligence (AI) has introduced innovative and effective methods for ADHD management. These technologies not only bring analytical accuracy but also an empathetic understanding of patient needs, enhancing the quality of life through impro
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Goyal, Nikhil Kumar, Monika Dandotiya, Jameel Ahmad Qurashi, Monika Kumari, and Shikha Sharma. "AI-Driven Predictive Analytics for Disease Management." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0690-2.ch008.

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AI-driven predictive analytics with powers of artificial intelligence and highly advanced machine learning algorithms applied to the vast healthcare data sources that help in the early indication of health risks, watching for potential diseases, monitoring, and then following on with more innovative interventions-not reactive care, but proactively preventing diseases. The integration of predictive analytics into healthcare systems addresses universal challenges in the form of chronic disease and population aging, in addition to further increasing costs in healthcare, while improving efficiency
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Kavibharathi, S., and Souvik Sen. "AI-Driven Telehealth Platforms to Improving Accessibility and Patient Engagement." In Intelligent Healthcare System. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552210-11.

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The integration of AI in telehealth platforms was rapidly transforming healthcare delivery by improving accessibility, enhancing patient engagement, and optimizing clinical outcomes. AI driven systems, leveraging technologies such as machine learning, predictive analytics, and natural language processing, offer personalized care solutions and real-time interventions, especially in underserved regions. This book chapter explores the potential of AI to revolutionize telehealth, focusing on key advancements such as virtual assistants, predictive analytics for identifying at risk patients, and mul
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Babu, Tina, Rekha R. Nair, and Kishore S. "AI for Multimedia Healthcare Applications." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-2935-1.ch001.

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This book chapter explores the profound impact of Artificial Intelligence (AI) on multimedia healthcare applications, showcasing its transformative influence on patient care and clinical practices. The narrative unfolds across several key domains, beginning with the revolutionary role of AI in enhancing medical imaging through advanced algorithms and deep learning models. The integration of AI into wearable devices for remote patient monitoring is examined, emphasizing its potential for continuous health assessment and early anomaly detection. The chapter delves into the realm of predictive an
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Asrifan, Andi, Akbar Akbar, Muhammad Nur, Robby Waluyo Amu, and Hadi Pajarianto. "AI Integration in Public Administration." In Advances in Public Policy and Administration. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-2272-8.ch003.

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This chapter examines how AI might alter public administration by improving efficiency, accessibility, and decision-making. AI automates ordinary jobs, freeing up public servants for high-impact work. Predictive analytics improve government operations by offering data-driven insights for resource allocation and preemptive actions in healthcare, urban planning, and public safety. Chatbots and virtual assistants make government services more accessible to different populations, especially those with linguistic or physical obstacles. The chapter also highlights the ethical and operational issues
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C, Kotteeswari, and Praveen Kumar Patidar. "Conversational AI for Healthcare Personalizing Patient Care and Support through NLP-Driven Systems." In Applications of Natural Language Processing in Intelligent Systems and Conversational AI for Industry. RADemics Research Institute, 2025. https://doi.org/10.71443/9788197933691-12.

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The integration of Conversational AI in healthcare has transformed patient care delivery by enhancing accessibility, efficiency, and personalization. This chapter explores the pivotal role of Conversational AI in revolutionizing healthcare services, particularly for elderly and disabled patients, by providing real-time support, remote monitoring, and personalized guidance. Through the use of advanced Natural Language Processing (NLP) and machine learning techniques, AI systems can anticipate patient needs, optimize treatment plans, and streamline communication between patients and healthcare p
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Joshi, Herat. "AI and Chronic Diseases From Data Integration to Clinical Implementation." In Advances in Healthcare Information Systems and Administration. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6577-9.ch002.

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The integration of artificial intelligence (AI) into chronic disease management offers transformative potential for enhancing patient care, predictive analytics, and personalized treatment. As chronic illnesses like diabetes, cardiovascular diseases, and cancer rise globally, AI technologies—machine learning, deep learning, natural language processing—provide innovative solutions. This chapter explores AI's role in predicting health events, personalizing treatment, and optimizing outcomes through case studies and practical implementations. Ethical, privacy, and regulatory considerations are ad
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Conference papers on the topic "Healthcare Analytics and AI Integration"

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Tarafder, Md Tanvir Rahman, Nisher Ahmed, Zakir Hossain, Md Masudur Rahman, Tahmeed-Ur Rahman, and Asif Ahamed. "Integrating Transformative AI for Next-Level Predictive Analytics in Healthcare." In 2024 IEEE Conference on Engineering Informatics (ICEI). IEEE, 2024. https://doi.org/10.1109/icei64305.2024.10912195.

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Bamfo, Kenneth Asamoah, Daniel Zeidan, and Luis Terán. "Integrating Machine Learning with Explainable AI in Healthcare Analytics for Diabetes Prediction." In 2025 Eleventh International Conference on eDemocracy & eGovernment (ICEDEG). IEEE, 2025. https://doi.org/10.1109/icedeg65568.2025.11081581.

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Mondal, Somnath, Sujan Das, Shib Shankar Golder, Rajesh Bose, Sharabani Sutradhar, and Haraprasad Mondal. "AI-Driven Big Data Analytics for Personalized Medicine in Healthcare: Integrating Federated Learning, Blockchain, and Quantum Computing." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882330.

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Sreenivasu, S. V. N., Siva Koteswararao Katta, Jerlin Priya Lovelin Auguskani, Dumpa Vamsi Priya, V. Jagadish, and Vedaprada Raghunath. "Integrating AI-Driven IoT Solutions for Enhanced Predictive Analytics in Healthcare a Comprehensive Study on Chronic Disease Management." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837020.

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Janjua, Jamshaid Iqbal, Taher M. Ghazal, Walid Abushiba, and Sagheer Abbas. "Optimizing Patient Outcomes with AI and Predictive Analytics in Healthcare." In 2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2024. https://doi.org/10.1109/rtucon62997.2024.10830874.

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Prakash, Saurabh, Uddhav T. Kumbhar, Satish V. Kakade, G. Anitha, and Arjun Mittal. "Integration of Data Mining and Big Data Analytics in Healthcare." In 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies (HISET). IEEE, 2024. http://dx.doi.org/10.1109/hiset61796.2024.00075.

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Abdulkadir, Ahmed. "Integration of AI in Clinical Practice: Trustworthy Automation in Disease Marker Quantification." In Microscopy Histopathology and Analytics. Optica Publishing Group, 2024. https://doi.org/10.1364/microscopy.2024.mm3a.1.

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This talk explores the challenges of integrating artificial intelligence into clinical settings and presents an example of embedding the quantification of disease markers into clinical processes, ensuring the automated process is verifiable, without disrupting existing workflows. Full-text article not available; see video presentation
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Patil, Sachin C., Prashamsh Takkalapally, Balaram Yadav Kasula, and J. Logeshwaran. "An improved AI-driven Data Analytics model for Modern Healthcare Environment." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724303.

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Mienye, Ibomoiye Domor, Theo G. Swart, and George Obaido. "Fairness Metrics in AI Healthcare Applications: A Review." In 2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2024. http://dx.doi.org/10.1109/iri62200.2024.00065.

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Melhem, Abdullah, Ahmed Aleroud, Abdullah Al-mamun, George Karabatis, and Mohamed I. Ibrahem. "Adversary-Resilient Clustered Federated Learning for Secure AI-Driven Healthcare Data Analytics." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059533.

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Reports on the topic "Healthcare Analytics and AI Integration"

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Adams, Jonathan, Gaurav Agnihotri, David Pendlebury, and Ed White. Research impact in society and the economy: The digital health revolution in medical care. Clarivate, 2024. http://dx.doi.org/10.14322/isi.grr.research.impact.in.society.

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This Global Research Report from the Institute for Scientific Information (ISI) examines the transformative impact of digital health on society and the global economy. Leveraging insights from our collaboration with Times Higher Education at the Digital Health 2024 event at Stanford University, the report integrates research and patent data to highlight the societal benefits of digital health innovations. Key findings include a 70-fold increase in digital health publications since 2013, with AI and advanced analytics driving innovation. The report identifies leading contributors to digital hea
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Spirin, Oleg, and Mariia Shyshkina. Artificial Intelligence in Education and Educational Research: Challenges, Risks, and Prospects for Integration. Institute for Digitalisation of Education of the NAES of Ukraіne, 2025. https://doi.org/10.33407/lib.naes.id/eprint/745119.

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Artificial intelligence (AI) is playing an increasingly important role in education, contributing to the personalization of learning, the automation of administrative processes and educational research. The use of AI allows for increased learning efficiency, improved management of educational resources and improved analytics of educational data. However, the integration of AI into education is accompanied by challenges related, in particular, to technical limitations, pedagogical risks, ethical aspects and security issues. Future research should focus on the integration of AI and augmented and
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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add
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Pasupuleti, Murali Krishna. AI and Quantum-Nano Frontiers: Innovations in Health, Sustainability, Energy, and Security. National Education Services, 2025. https://doi.org/10.62311/nesx/rr525.

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Abstract: This research report explores transformative advancements at the intersection of Artificial Intelligence (AI), Quantum Computing, and Nanotechnology, focusing on breakthrough innovations in health, sustainability, energy, and global security. By integrating quantum algorithms, AI-driven analytics, and advanced nanomaterials, this report highlights revolutionary solutions in precision medicine, predictive diagnostics, sustainable energy storage, universal water purification, and cybersecurity. Real-world case studies and emerging technologies such as graphene-based nanomaterials, quan
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Faveri, Benjamin, Maureen Johnson-León, Prem Sylvester, et al. Towards A Global AI Auditing Framework: Assessment and Recommendations. Edited by Luis Adrián Castro-Quiroa, Eloísa Gacía-Canseco, Joan Hassan, et al. International Panel on the Information Environment (IPIE), 2025. https://doi.org/10.61452/zwed1485.

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A high-level précis of the Synthesis Report can be found in the Summary for Policymakers Recommendations for a Global AI Auditing Framework: Summary of Standards and Features. The growing integration of artificial intelligence (AI) into critical sectors of society, from healthcare to education, has the potential to support widespread social transformation and progress. However, AI systems also have the power to perpetuate biases, deepen inequalities, and cause environmental harm. Accurately evaluating the risks and benefits of an AI system requires a careful audit. Current approaches to auditi
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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
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McKinley, Catherine, Prem Sylvester, Benjamin Faveri, et al. Recommendations for a Global AI Auditing Framework: Summary of Standards and Features. Edited by Saiph Savage, Mona Sloam, Luis Adrián Castro-Quiroa, et al. International Panel on the Information Environment (IPIE), 2024. https://doi.org/10.61452/guyx7442.

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This Summary for Policymakers provides a high-level précis of the Synthesis Report Towards A Global AI Auditing Framework: Assessment and Recommendations. The growing integration of artificial intelligence (AI) into critical sectors of society, from healthcare to education, has the potential to support widespread social transformation and progress. However, AI systems also have the power to perpetuate biases, deepen inequalities, and cause environmental harm. Accurately evaluating the risks and benefits of an AI system requires a careful audit. Current approaches to auditing, however, rarely i
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