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

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Adeyoyin, Adebisi. "Telemedicine and AI in Remote Patient Monitoring." Researchgate 3513, no. 19 (2024): 34. https://doi.org/10.5281/zenodo.11183975.

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The integration of Telemedicine and Artificial Intelligence (AI) in Remote Patient Monitoring (RPM) represents a transformative shift in healthcare delivery. Telemedicine enables remote access to healthcare services, while AI enhances data analytics and decision-making capabilities. This abstract explores the significance of Telemedicine and AI in RPM, highlighting their roles, advantages, challenges, and future implications. Telemedicine facilitates remote monitoring of patients' vital signs and symptoms, improving accessibility and enabling timely interventions. AI in RPM provides real-time

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Ivshin, A. A., A. V. Gusev, and R. E. Novitskiy. "Artificial intelligence: predictive analytics of perinatal risks." Voprosy ginekologii, akušerstva i perinatologii 19, no. 6 (2020): 133–44. http://dx.doi.org/10.20953/1726-1678-2020-6-133-144.

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Artificial intelligence (AI) has recently become an object of interest for specialists from various fields of science and technology, including healthcare professionals. Significantly increased funding for the development of AI models confirms this fact. Advances in machine learning (ML), availability of large data sets, and increasing processing power of computers promote the implementation of AI in many areas of human activity. Being a type of AI, machine learning allows automatic development of mathematical models using large data sets. These models can be used to address multiple problems,

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Jeshwanth Reddy Machireddy. "Harnessing AI and data analytics for smarter healthcare solutions." International Journal of Science and Research Archive 8, no. 2 (2023): 785–98. https://doi.org/10.30574/ijsra.2023.8.2.0105.

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The integration of Artificial Intelligence (AI) and Data Analytics in healthcare has emerged as a transformative force in improving the efficiency, accuracy, and accessibility of medical services. This research paper examines how AI-driven models and data analytics techniques are being harnessed to provide smarter healthcare solutions. Through the application of machine learning, predictive analytics, and data mining, healthcare providers can now analyze vast amounts of patient data, offering more accurate diagnostics, personalized treatment plans, and enhanced clinical decision-making. In par

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Researcher. "AI and Quantum Computing: The Future of Data Analytics at Scale." International Journal of Computer Science and Information Technology Research (IJCSITR) 6, no. 2 (2025): 35–53. https://doi.org/10.5281/zenodo.15068399.

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<em>The rapid growth of data driven applications has revealed the computational and scalability limitations of traditional computer systems in the delivery of AI and ML solutions. However, with artificial intelligence enhancing various sectors such as banking, healthcare and logistics, the need for improved and more efficient computing has led to the exploration of quantum computing as a possible solution. Quantum Computing (QC), that uses concepts such as superposition and entanglement of quantum bits or qubits is expected to improve AI based data analytics by reducing the time for training m

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Chibuzor Njoku, Ikechukwu Onwe, Chidinma I Onyeibor, Chinyere E Ekanem, and Obinna R Diala. "Integrating artificial intelligence and data analytics in imaging for early cancer detection: Optimizing workforce efficiency and healthcare resource allocation." International Journal of Scientific Research Updates 9, no. 1 (2025): 017–21. https://doi.org/10.53430/ijsru.2025.9.1.0026.

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Advancements in artificial intelligence (AI) have revolutionized healthcare, particularly in early cancer detection and workforce optimization. This paper explores the integration of AI-driven imaging technologies and predictive approaches to improve diagnostic accuracy, streamline radiology workflows, and enhance healthcare resource allocation. By leveraging machine learning algorithms, tele-radiology, and automation, AI can significantly reduce diagnostic delays, optimize radiology workforce distribution, and improve healthcare delivery in underserved areas. The paper examines the role of AI

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Researcher. "ADVANCING CLINICAL DECISION SUPPORT: A TECHNICAL ANALYSIS OF IBM WATSON HEALTH'S AI-DRIVEN HEALTHCARE ANALYTICS PLATFORM." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1265–75. https://doi.org/10.5281/zenodo.14170059.

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This technical article comprehensively examines IBM Watson Health's AI-driven healthcare analytics platform, focusing on its implementation framework and clinical applications. The article explores the platform's advanced analytics capabilities, diagnostic support systems, and specialized oncology applications while providing detailed insights into its technical architecture and deployment methodologies. The article demonstrates how machine learning algorithms, real-time analytics processing, and sophisticated data integration mechanisms work together to create an effective healthcare decision

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Mittapelly, Sai Sharan Reddy. "AI-Powered Interface Monitoring: Revolutionizing Healthcare Data Integration." European Journal of Computer Science and Information Technology 13, no. 40 (2025): 128–38. https://doi.org/10.37745/ejcsit.2013/vol13n40128138.

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The integration of artificial intelligence in healthcare interface monitoring has transformed the landscape of clinical data management and system reliability. AI-powered systems have revolutionized traditional monitoring paradigms by introducing predictive capabilities, enhanced alert intelligence, and autonomous interface management. Through advanced pattern recognition and correlation algorithms, these systems enable healthcare organizations to detect and prevent potential failures before they impact clinical operations. The implementation of AI-driven analytics has significantly improved p

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AKINBODE Azeez Kunle. "AI-enhanced healthcare analytics and predictive modeling for value-based care: A comprehensive analysis of implementation and outcomes in the United States healthcare system." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 1433–45. https://doi.org/10.30574/wjarr.2025.26.3.2290.

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The transition from fee-for-service to value-based care (VBC) models represents a fundamental shift in the US healthcare system, emphasizing patient outcomes and cost-effectiveness over volume of services. This comprehensive analysis examines the role of Artificial Intelligence (AI) and predictive modeling in enhancing healthcare analytics within VBC frameworks. Through systematic evaluation of current implementations, technological capabilities, and outcome metrics, this study demonstrates that AI-enhanced healthcare analytics significantly improve care quality, reduce costs, and optimize res

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Venkat Mounish Gundla. "Advances in Scalable Data Architectures for AI-Driven Healthcare Analytics." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 547–53. https://doi.org/10.32996/jcsts.2025.7.3.62.

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Scalable data architectures tailored for AI-driven healthcare analytics are transforming the healthcare landscape by enabling advanced diagnostic capabilities, predictive modeling, and operational optimizations. These architectures address the unique challenges presented by healthcare data, its volume, heterogeneity, quality concerns, and regulatory requirements through innovative combinations of cloud computing, distributed processing frameworks, specialized storage solutions, and sophisticated data pipelines. The progression from traditional monolithic systems to distributed cloud-native arc

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Das, Rupali, B. Suneela, Dheeraj Akula, Swapnil Saurav, Shruti Tyagi, and D. Esther Rani. "Integrating AI-Driven Data Analytics into Healthcare Business Models: A Multi-Disciplinary Approach." Journal of Neonatal Surgery 14, no. 5S (2025): 761–73. https://doi.org/10.52783/jns.v14.2126.

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The integration of AI-driven data analytics into healthcare business models has emerged as a transformative approach to improving patient outcomes, optimizing operational efficiency, and enhancing financial sustainability. This study investigates the impact of AI-powered predictive analytics, automation, and patient engagement strategies on key healthcare performance metrics. Our findings indicate that AI models significantly reduce patient readmission rates, with Artificial Neural Networks (ANN) achieving an accuracy of 88.1% in predicting hospital readmissions. This led to an estimated €900

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Jharbade,, Dr Ruchika. "Digital Therapeutics: Transforming Healthcare Through AI & Emerging Technology." IOSR Journal of Dental and Medical Sciences 24, no. 1 (2025): 40–49. https://doi.org/10.9790/0853-2401014049.

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The integration of Artificial Intelligence (AI) into Digital Therapeutics (DTx) has emerged as a transformative trend in healthcare, offering innovative solutions for the management, treatment, and prevention of various medical conditions. DTx, defined as clinically validated software-based therapeutic interventions, are increasingly leveraging AI technologies, including machine learning, deep learning, and advanced data analytics, to enhance their efficacy, personalization, and scalability. This literature review explores the state-ofthe-art developments in AI-driven DTx, focusing on key appl

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Onyekachukwu Victor Unanah and Olu James Mbanugo. "Integration of AI into CRM for Effective U.S. healthcare and pharmaceutical marketing." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 609–30. https://doi.org/10.30574/wjarr.2025.25.2.0396.

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The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) systems is revolutionizing the landscape of U.S. healthcare and pharmaceutical marketing. As the healthcare sector becomes increasingly patient-centric, the need for personalized, data-driven marketing strategies is paramount. AI-powered CRM platforms offer the ability to analyze vast datasets, uncovering patterns and insights that facilitate more effective targeting of healthcare providers and patients. This integration enhances the ability of pharmaceutical companies to deliver tailored content, optim

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Taye Bekele and Haile Mekonnen. "Integrating Artificial Intelligence in Healthcare : Improving Patient Care and Diagnosis." Proceeding of The International Conference of Inovation, Science, Technology, Education, Children, and Health 2, no. 1 (2022): 257–61. https://doi.org/10.62951/icistech.v2i1.126.

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Artificial intelligence (AI) has shown great potential in revolutionizing the healthcare sector by enhancing diagnosis accuracy and optimizing treatment plans. This paper investigates the application of AI technologies in healthcare, focusing on machine learning algorithms, natural language processing, and predictive analytics. The study evaluates several case studies where AI has been implemented to assist in early disease detection, personalized treatment, and patient management. Results suggest that AI integration leads to improved healthcare outcomes and efficiency, transforming patient ca

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Chintala, Suman. "ARTIFICIAL INTELLIGENCE WITH MICROSTRATEGY: ENHANCING DATA INGESTION AND CUSTOMER BENEFITS WITH AI INTEGRATION." International Journal of Advanced Research 12, no. 05 (2024): 1106–12. http://dx.doi.org/10.21474/ijar01/18825.

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The integration of Artificial Intelligence (AI) with MicroStrategy is revolutionizing data management and analytics, significantly enhancing organizational productivity and efficiency. By incorporating advanced technologies such as large language models (LLMs) and generative AI, MicroStrategy offers features like Auto Answers and Auto Dashboard, which streamline data analysis and provide rapid, reliable insights. This integration is particularly impactful in fields such as healthcare, where AI-driven solutions like CerviCARE AI improve diagnostic accuracy in cervical cancer screening. The impl

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Satish Kumar Nadendla. "Innovating Global Healthcare Solutions with AWS: Utilizing AI, Data Analytics, and Cloud Services for Disease Control and Personalized Medicine." World Journal of Advanced Research and Reviews 14, no. 3 (2022): 801–14. https://doi.org/10.30574/wjarr.2022.14.3.0472.

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With the recent developments in AI, Data analytics, and Cloud computing, the global healthcare sector has seen a drastic transformation with the former offering solutions for disease control and personalized medicine. Amazon Web Services (AWS) is a strong, scalable, and secure platform to combine these technologies and improve healthcare delivery. In this paper, we will discuss how AI and data analytics powered by AWS allow for predictive modeling, early disease detection, and real-time monitoring of patients, leading to positive healthcare outcomes. Their data processing for precision medicin

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Panyaram, Sudheer. "Utilizing Quantum Computing to Enhance Artificial Intelligence in Healthcare for Predictive Analytics and Personalized Medicine." FMDB Transactions on Sustainable Computing Systems 2, no. 1 (2024): 22–31. http://dx.doi.org/10.69888/ftscs.2024.000194.

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Advancements in quantum computing hold the potential to revolutionize artificial intelligence (AI), particularly in the field of healthcare. This paper explores how quantum computing can be leveraged to improve predictive analytics and facilitate personalized medicine. Through enhanced computational capacity, quantum computing enables faster processing and analysis of large, complex datasets, essential for predictive models in healthcare. This integration can lead to more precise diagnostics, treatment options, and disease prevention strategies by refining AI’s capability to handle vast data.

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Reshma R, Ms. "Artificial Intelligence in Medical Diagnostics and Healthcare." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–6. https://doi.org/10.55041/isjem02354.

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Artificial Intelligence (AI) is revolutionizing healthcare by transforming traditional diagnostic processes, enhancing treatment planning, and improving overall patient care. AI-driven technologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and fuzzy logic, are being increasingly integrated into clinical settings to assist healthcare professionals in making more accurate and timely decisions. AI has demonstrated its potential to surpass human expertise in medical imaging interpretation, predictive analytics, and personalized medicine by analyzing l

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Kalpinagarajarao, Gopi Krishna. "AI-enhanced oracle platforms: A new era of predictive healthcare analytics and cybersecurity." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 1 (2025): 1823–30. https://doi.org/10.54660/.ijmrge.2025.6.1-1823-1830.

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In the world of healthcare landscape, along with the fundamentally changing context of data in life science and healthcare around the globe, the combination of Artificial Intelligence (AI) with Oracle platforms is ushering in a new era in predictive analytics and cybersecurity. In this paper, we look at how AI-enhanced Oracle platforms are turning predictive healthcare analytics on its head by allowing real time data processing, precision diagnostics, and comprehensive insights into patient care and using Oracle databases that use AI-driven algorithms to combine huge datasets to find patterns

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Aravind Puppala. "AI-Augmented Business Intelligence in Healthcare Enterprise Systems: Case Studies of Integration for Performance, Outcomes, and Efficiency." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1345–52. https://doi.org/10.30574/wjaets.2025.15.3.1053.

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The integration of artificial intelligence into business intelligence systems is transforming healthcare delivery through enhanced predictive capabilities and decision support. This article presents case studies of successful AI-BI implementations at leading healthcare institutions, demonstrating significant improvements in operational efficiency, clinical outcomes, and financial performance. Mayo Clinic's patient flow optimization system and Cleveland Clinic's clinical risk stratification platform showcase the transformative potential of AI-augmented analytics in healthcare enterprise environ

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Triveni Kolla. "How AI is transforming fraud detection in healthcare." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3674–81. https://doi.org/10.30574/wjarr.2025.26.2.1922.

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Healthcare fraud presents a formidable challenge to modern healthcare systems worldwide, with substantial financial losses and erosion of patient trust. Traditional detection methodologies based on rule frameworks and manual review processes have proven inadequate, generating excessive false positives and missing complex fraud patterns. The healthcare sector's digital transformation has created unprecedented opportunities to leverage artificial intelligence for fraud prevention. This article examines how AI technologies—including machine learning algorithms, natural language processing, and ne

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Brahmanand Reddy Bhavanam. "The role of AI in transforming healthcare: A technical analysis." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 803–11. https://doi.org/10.30574/wjaets.2025.15.1.0288.

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Artificial Intelligence is fundamentally transforming healthcare delivery by enhancing diagnostic accuracy, enabling personalized treatment, and improving operational efficiency. This article examines the technical foundations of healthcare AI systems, current applications across diagnostic support, predictive analytics, robotics, and drug discovery, and the implementation challenges these technologies face. By analyzing the complex interplay between data acquisition, algorithm development, regulatory frameworks, and clinical integration, we identify both the transformative potential and pract

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Sopruchi, Ariowachukwu Divine, and Anguzu Rashid. "The Integration of AI-Driven Decision Support Systems in Healthcare: Enhancements, Challenges, and Future Directions." IDOSR JOURNAL OF COMPUTER AND APPLIED SCIENCES 9, no. 2 (2024): 17–25. http://dx.doi.org/10.59298/jcas/2024/92.1725.

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Artificial Intelligence (AI) is transforming the healthcare landscape by enhancing diagnostic accuracy, operational efficiency, and patient management through AI-driven Decision Support Systems (AI-DSS). These systems leverage vast datasets, including electronic health records and various biomolecular markers, to provide evidence-based recommendations for clinical practice. Despite their potential, challenges such as data quality, algorithmic bias, and ethical concerns persist. This paper explores the capabilities of AI-DSS in healthcare, the methodologies underpinning their operation, and the

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Swapnil Narlawar. "Explainable AI (XAI) in enterprise analytics systems." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4087–97. https://doi.org/10.30574/wjarr.2025.26.2.2072.

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Explainable AI (XAI) represents a critical frontier in enterprise analytics as organizations increasingly rely on AI systems for consequential business decisions. The opacity of sophisticated machine learning models presents significant barriers to trust, compliance, and effective deployment, particularly in sensitive domains like finance and healthcare. This article explores the integration of XAI methods into enterprise analytics platforms, examining the architectural requirements, implementation challenges, and evaluation methodologies necessary for success. A structured framework emerges t

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Obiajuru Triumph Nwadiokwu. "Ethical AI and machine learning integration in health innovation information systems for clinical excellence." Computer Science & IT Research Journal 6, no. 3 (2025): 125–42. https://doi.org/10.51594/csitrj.v6i3.1872.

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The integration of Ethical Artificial Intelligence (AI) and Machine Learning (ML) in Health Innovation Information Systems is transforming clinical excellence by enhancing decision-making, optimizing healthcare workflows, and improving patient outcomes. AI-driven health technologies, such as predictive analytics, automated diagnostics, and personalized medicine, offer significant benefits in disease detection, treatment planning, and patient monitoring. However, their implementation raises ethical concerns related to data privacy, algorithmic bias, transparency, and accountability, necessitati

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Sawant, Devika. "AI in Medical." International Journal for Research in Applied Science and Engineering Technology, no. 6 (June 30, 2024): 168–75. http://dx.doi.org/10.22214/ijraset.2024.63010.

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Abstract: Artificial Intelligence (AI) stands as a transformative influence in the realm of medicine, poised to reshape healthcare delivery and elevate patient outcomes. This summary offers an overview of AI's impact on medical applications, with key technologies such as machine learning, natural language processing, and computer vision playing pivotal roles across various healthcare domains, from diagnostics to treatment planning and patientcare. A notable stride in this evolution is the integration of AI-driven telemedicine solutions, effectively expanding access to healthcare services, part

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Carrasco Ramírez, José Gabriel. "AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 1, no. 1 (2024): 31–37. http://dx.doi.org/10.60087/jaigs.v1i1.p37.

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This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imagi

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Cheepurubilli, Tharun. "AI in Healthcare: Medical Diagnostics & Drug Discovery." Journal of Research and Innovation in Technology, Commerce and Management Vol. 2, Issue 6 (2025): 2630–38. https://doi.org/10.5281/zenodo.15591237.

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This paper examines the transformative impact of artificial intelligence (AI) in healthcare, with specific focus on medical diagnostics and drug discovery. The integration of AI technologies has revolutionized medical imaging analysis, disease diagnosis, personalized medicine approaches, and predictive analytics in patient care. Through comprehensive analysis of current implementations, this research highlights how machine learning algorithms, deep neural networks, and natural language processing have enhanced diagnostic accuracy, accelerated drug development timelines, and improved patient ou

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Jaymin Harishkumar Sutarwala. "Artificial Intelligence in Healthcare CRM: A Systematic Review of Emerging Technologies and Patient-Centered Applications." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2717–27. https://doi.org/10.32628/cseit251112294.

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Integrating Artificial Intelligence (AI) in Healthcare Customer Relationship Management (CRM) systems represents a significant advancement in modern healthcare delivery. This comprehensive review examines emerging innovations at the intersection of AI and Healthcare CRM, focusing on machine learning algorithms, predictive analytics, and natural language processing applications. The research demonstrates how these technologies enhance patient care through improved risk prediction, personalized treatment planning, and automated communication systems. Further investigation explores the implementa

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Sarker, Mithun. "Assessing the Integration of AI Technologies in Enhancing Patient Care Delivery in U.S. Hospitals." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 338–51. http://dx.doi.org/10.60087/jklst.vol2.n2.p351.

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The healthcare industry's integration of artificial intelligence (AI) has been significantly accelerated by the COVID-19 pandemic. The urgency for swift diagnosis and treatment, coupled with the rise in demand for remote care and monitoring, has prompted a concentrated effort towards AI-driven solutions aimed at enhancing healthcare delivery and patient outcomes. Various AI-powered technologies including predictive analytics, natural language processing, and computer vision have been harnessed to facilitate screening and diagnosis, expedite drug discovery, and advance vaccine development. Furt

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Nyiramana, Mukamurera P. "The Role of Artificial Intelligence in Clinical Decision Support Systems." RESEARCH INVENTION JOURNAL OF PUBLIC HEALTH AND PHARMACY 3, no. 2 (2024): 14–17. http://dx.doi.org/10.59298/rijpp/2024/321417.

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Clinical Decision Support Systems (CDSS) are integral tools in modern healthcare, designed to assist clinicians by providing patient-specific recommendations based on vast medical data and knowledge. The advent of Artificial Intelligence (AI) has significantly enhanced CDSS, enabling sophisticated predictive analytics, early detection of complications, and personalized interventions. AI techniques like machine learning, natural language processing, and deep learning play crucial roles in refining CDSS functionality. However, challenges such as data quality, AI transparency, and clinician trust

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Francisca Chibugo Udegbe, Ogochukwu Roseline Ebulue, Charles Chukwudalu Ebulue, and Chukwunonso Sylvester Ekesiobi. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A SYSTEMATIC REVIEW OF APPLICATIONS AND CHALLENGES." International Medical Science Research Journal 4, no. 4 (2024): 500–508. http://dx.doi.org/10.51594/imsrj.v4i4.1052.

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This paper presents a systematic review of the role of Artificial Intelligence (AI) in healthcare, highlighting its applications and challenges. AI technologies, including machine learning, natural language processing, and predictive analytics, are transforming healthcare through diagnostic assistance, treatment personalization, patient monitoring, optimization of healthcare operations, and public health. Despite the potential benefits, the integration of AI in healthcare faces significant challenges, such as data privacy and security concerns, ethical and legal issues, interoperability and in

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Kashyap, Gaurav. "AI in Blockchain-Enabled Healthcare Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem17947.

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The integration of Artificial Intelligence (AI) and Blockchain technology offers transformative potential for the healthcare industry. Blockchain provides a secure, immutable, and transparent infrastructure for managing healthcare data, while AI enhances decision-making, predictive analytics, and personalized treatment. This research paper explores the synergies between AI and Blockchain in healthcare systems, focusing on how these technologies can improve patient care, streamline operations, enhance data security, and address key challenges like interoperability and trust. Through case studie

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Arnold, Niwarinda. "Smart Health Records: Integrating AI for Efficient Data Management." NEWPORT INTERNATIONAL JOURNAL OF RESEARCH IN MEDICAL SCIENCES 6, no. 1 (2025): 14–18. https://doi.org/10.59298/nijrms/2025/6.1.141800.

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The rapid evolution of healthcare demands innovative solutions for managing the vast quantities of data generated daily. This paper examines the concept of Smart Health Records (SHRs) and their integration with Artificial Intelligence (AI) to improve healthcare delivery. SHRs represent an advanced iteration of electronic health records, equipped with AI-powered tools to ensure real-time data sharing, predictive analytics, and automated administrative processes. AI’s capabilities, including natural language processing, machine learning, and predictive modeling, streamline data management and en

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Mangharamani, Ruchi, and Dr Shruti Saxena. "AI-Powered Decision Intelligence: How Autonomous Analytics is Reshaping Business, Healthcare, and Public Policy." International Journal of Research in Humanities and Social Sciences 13, no. 3 (2025): 306–18. https://doi.org/10.63345/ijrhs.net.v13.i3.17.

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AI-powered decision intelligence represents a paradigm shift in how organizations analyze data and make informed decisions. By integrating autonomous analytics into business operations, healthcare diagnostics, and public policy formulation, this technology is enhancing the speed and accuracy of decision-making processes. In the business sector, AI-driven systems streamline operations by automating complex data analyses, identifying trends, and providing actionable insights that support strategic planning and risk management. In healthcare, the use of AI enables early detection of diseases, per

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Krishna Prasad N Rao. "Empowering Patient-Centric Healthcare with AI-Driven Predictive Analytics in Blockchain." Journal of Information Systems Engineering and Management 10, no. 11s (2025): 678–90. https://doi.org/10.52783/jisem.v10i11s.1667.

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This paper proposes a novel approach integrating Multilayer Perceptron (MLP) with Gated Recurrent Unit (GRU) for predictive analytics within blockchain-based health records systems. In contrast to conventional models, the suggested method combines the power of GRUs to identify temporal correlations in sequential data with the strengths of MLPs in extracting complex patterns from organized health data. More precise and dynamic predictions are made possible by this hybrid model, which is designed to comprehend complicated health trajectories. The proposed model combines the strengths of MLP in l

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Dakshaja Prakash Vaidya. "AI and resilient cloud infrastructure in healthcare." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4430–36. https://doi.org/10.30574/wjarr.2025.26.2.2069.

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The transformative convergence of artificial intelligence and resilient cloud infrastructure within healthcare environments represents a fundamental shift in medical service delivery, data management, and patient care administration. The digital evolution occurring across healthcare institutions has established new frameworks for handling clinical information at unprecedented scale and complexity. Cloud infrastructure provides the foundation through multi-tier architectures that balance security requirements with accessibility needs, while sophisticated storage frameworks accommodate the expon

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Abdullah Atallah Aqeel Al-Tarfawi. "The Future of AI in Managing Medical Records and Administrative Tasks." Power System Technology 48, no. 4 (2024): 3300–3311. https://doi.org/10.52783/pst.1193.

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Artificial intelligence (AI) has revolutionized numerous industries, including healthcare. This article explores the transformative potential of AI in managing medical records and streamlining administrative tasks within healthcare systems. By enhancing efficiency, reducing human error, and enabling predictive analytics, AI offers solutions to long- standing challenges in healthcare administration. This paper delves into the integration of AI-driven tools, the ethical and security considerations involved, and the future trajectory of AI in healthcare administration. Key examples and case studi

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Sagay, Irene, Sandra Oparah, Opeoluwa Oluwanifemi Akomolafe, Ajao Ebenezer Taiwo, and Tolulope Bolarinwa. "Using AI to Predict Patient Outcomes and Optimize Treatment Plans for Better Healthcare Delivery." International Journal of Future Engineering Innovations 1, no. 1 (2024): 146–52. https://doi.org/10.54660/ijfei.2024.1.1.146-152.

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Artificial Intelligence (AI) revolutionizes healthcare by enabling predictive analytics and personalized treatment plans, improving patient outcomes and optimizing care delivery. This paper explores the current landscape of AI in healthcare, detailing the various AI models and algorithms employed in outcome prediction, including machine learning, deep learning, and natural language processing. It highlights the integration of AI with clinical decision support systems (CDSS). It presents examples of successful AI-driven treatment optimizations in fields such as oncology, cardiology, and persona

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Yogeshappa, Vedamurthy Gejjegondanahalli. "Advances in AI-Driven Innovations Across Healthcare, Cloud Platforms, and Data Management." International Journal of Research in Engineering, Science and Management 8, no. 1 (2025): 98–102. https://doi.org/10.5281/zenodo.14759689.

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This research paper explores the integration of Artificial Intelligence (AI) in diverse domains, including healthcare, cloud platforms, data management, and strategic risk assessment. By leveraging cutting-edge AI methodologies and scalable cloud infrastructures, this study highlights advancements in predictive analytics, IoT frameworks, and machine learning applications. Moreover, it addresses ethical considerations, performance optimization, and the operational challenges of AI technologies.

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Naga, Santhosh Reddy Vootukuri. "AI-Driven Unified Framework for Mental Health Data." International Journal in Engineering Sciences 2, no. 4 (2025): 1–8. https://doi.org/10.5281/zenodo.15179470.

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Integrating Artificial Intelligence (AI) into the mental health field is revolutionizing the profession, with greater accuracy of diagnosis and room for customized intervention therapies. The traditional practice of mental healthcare through personal opinions and reactive treatments with resulting late diagnoses is replaced by data-driven, scalable solutions provided by AI-based platforms. This paper is a continuation of Chavali's (2024)[1] research on a "Unified Data Integration and Record Identification Framework" and suggests a revised framework adapted for mental health data. Building on A

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