Convergence Of Artificial Intelligence Research Articles

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry.

Conversational Fashion Outfit Generator Powered by GenAI

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

AI-driven transformation: From economic forecasting to strategic management

The convergence of Artificial Intelligence (AI) technologies and various sectors within the global economy has brought forth transformative changes, shaping decision-making processes, financial practices, and economic forecasting. This comprehensive paper explores the multifaceted applications of AI in diverse domains, ranging from economic forecasting and financial markets to accounting, auditing, strategic management, and crisis management. Through empirical analysis, case studies, and scholarly insights, we uncover how AI technologies are driving efficiencies, improving predictions, enhancing decision-making, and fostering innovation across these sectors. This study offers valuable academic contributions by delving into the specific methodologies, real-world examples, and impact assessments of AI adoption, shedding light on its implications for practitioners, policymakers, and researchers.

The emergence of AI in mental health: A transformative journey

This paper traces the evolution of Artificial Intelligence (AI) in psychiatry, from early attempts like the ELIZA program to the integration of neuroimaging studies and the emergence of digital interventions. Utilizing deep learning models, AI now demonstrates high accuracy in classifying psychiatric disorders, such as schizophrenia based on functional connectivity patterns. The rise of AI chat/therapy bots, exemplified by Woebot and Tess, addresses mental health challenges, providing accessible and effective cognitive behavioral therapy tools. Innovative interventions like avatar therapy and intelligent animal-like robots contribute to broadening therapeutic perspectives. While AI-based interventions offer advantages in reducing stigma and increasing accessibility, challenges including diagnostic accuracy, legal responsibilities, and privacy concerns must be carefully considered. Despite challenges, the significant potential of AI in psychiatry calls for large, controlled trials, national standards, and responsible integration into clinical practice. The convergence of AI and psychiatry represents a technological revolution poised to transform mental health care delivery globally.

Impact of artificial intelligence on health information literacy: guidance for healthcare professionals

Purpose This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all. Design/methodology/approach This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL. Findings Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices. Practical implications To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research. Originality/value This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

Synergizing Intelligence: Revolutionizing Supply Chains with Blockchain and AI

In the evolving landscape of supply chain management, the integration of blockchain technology and artificial intelligence (AI) stands as a beacon of innovation, promising to address the perennial challenges of efficiency, transparency, and reliability. This paper presents a comprehensive exploration of how AI can revolutionize blockchain supply chains, offering a synthesis of current research, methodologies, and case studies that highlight the transformative potential of this synergy. The supply chain, a complex network that underpins global trade, is often beleaguered by inefficiencies and vulnerabilities. Blockchain technology, with its decentralized and immutable ledger, has emerged as a solution to enhance traceability and trust. However, it is the infusion of AI that has the potential to catalyze a paradigm shift in supply chain management. AI’s capabilities in data analytics, machine learning, and autonomous decision-making can optimize logistics, predict trends, and automate tasks, thereby elevating the blockchain beyond its current utility. This research adopts a mixed-methods approach, drawing on qualitative insights from industry experts and quantitative data from performance metrics to assess the impact of AI on blockchain supply chains. Through a series of case studies, the paper illustrates the practical applications and challenges of this integration, providing a nuanced understanding of its implications. The findings reveal that AI significantly enhances the efficiency and accuracy of blockchain supply chains, leading to improvements in transaction times, data verification processes, and overall supply chain performance. The discussion delves into the strategic advantages of this integration, such as improved compliance and ethical supply chain practices, while also acknowledging the limitations and challenges that organizations must navigate. In conclusion, the paper posits that the convergence of AI and blockchain holds great promise for the future of supply chains. It offers a roadmap for practitioners looking to harness these technologies and sets forth directions for future research, particularly in the development of sophisticated AI algorithms tailored for blockchain applications and the long-term economic impacts on supply chain management. The study contributes to the broader field by providing empirical evidence and a new perspective on the potential of AI to create more resilient, efficient, and transparent supply networks..

REVIEWING THE ROLE OF AI IN DRONE TECHNOLOGY AND APPLICATIONS

This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications. As drones continue to evolve from recreational gadgets to indispensable tools across industries, the integration of AI enhances their capabilities, enabling advanced functionalities and expanding their potential use cases. The convergence of AI and drone technology has given rise to a myriad of applications, transforming industries ranging from agriculture to surveillance. Machine learning algorithms empower drones with autonomous navigation capabilities, allowing them to navigate complex environments and adapt to dynamic scenarios. Computer vision technologies enable drones to perceive and analyze visual information, facilitating tasks such as object recognition, tracking, and environmental monitoring. These advancements significantly contribute to enhanced aerial surveying, precision agriculture, and disaster response efforts. In the realm of precision agriculture, AI-equipped drones aid in crop monitoring, disease detection, and yield estimation, optimizing resource allocation and boosting agricultural productivity. Drones with AI-driven capabilities are increasingly employed in environmental monitoring, wildlife conservation, and disaster response, providing real-time data for efficient decision-making. Recent trends in AI-infused drone technology highlight its dynamic evolution. Edge computing solutions empower drones to process data locally, reducing latency and enhancing real-time responsiveness. Reinforcement learning algorithms enable drones to learn from their experiences, adapting and optimizing their performance over time. Swarm intelligence, an emerging field in drone technology, leverages AI to enable coordinated and synchronized actions among multiple drones, expanding their capabilities for collaborative tasks. In conclusion, this review sheds light on the pivotal role of AI in transforming drone technology and expanding its applications. The synergy between AI and drones has unlocked new possibilities across various industries, ranging from agriculture to disaster response. As technology continues to advance, the collaborative integration of AI and drones promises to redefine the future of aerial technology, introducing unprecedented efficiencies and capabilities across diverse sectors.
 Keywords: Role, AI, Drone, Applications, Technology.

Retinal Vascular Imaging Application in Women’s Reproductive Health: Clinical Implications and Future Directions

Abstract Over the past two decades, population-based studies employing semiautomatic computer-assisted programs have uncovered associations between retinal microvascular features and various systemic conditions. As the recognition of retinal imaging in cardiometabolic health grows, there is increasing evidence supporting its application in women’s health, particularly during the reproductive age. This review aims to summarize the indications of retinal imaging in women’s health and intergenerational health, where suboptimal retinal imaging has been found to mirror pathological systemic changes, such as suboptimal hemodynamic circulation, inflammation, endothelial dysfunction, oxidative stress, and hypoxia in vivo. Findings from Singapore Growing Up in Singapore Towards Healthy Outcomes and Singapore Preconception Study of Long-Term Maternal and Child Outcomes cohorts have reported serial changes in retinal conventional microvascular features (e.g., retinal arteriolar narrowing, retinal venular widening) and retinal geometric microvascular features (e.g., sparse fractal dimension, enlarged branching angle, and increased curvature tortuosity) during the preconception and antenatal phases. These morphological abnormalities were found to be related to female fertility, maternal antenatal health conditions, postnatal maternal cardiometabolic health, and intergenerational health in the fetus. Given the compelling evidence of the ability to detect microvascular changes through noninvasive methods at an early stage, retinal imaging holds the potential to facilitate timely interventions, mitigate the progression of complications, and prevent adverse pregnancy outcomes. Looking ahead, the convergence of artificial intelligence and advanced imaging techniques heralds a promising era in women’s health research and clinical practice.

NAVIGATING DENTISTRY’S AI REVOLUTION: OPPORTUNITIES IN PAKISTAN

The convergence of Artificial Intelligence (AI) and dentistry marks a profound shift in the way oral healthcare is delivered and managed. In Pakistan, where healthcare resources may be constrained, the integration of AI presents unique opportunities to enhance access, efficiency, and precision in dental practice. AI-powered tele-dentistry platforms offer remote consultations and diagnostics, bridging geographical gaps in access to specialized care. This empowers patients across Pakistan, ensuring timely interventions and expert opinions regardless of location.AI algorithms excel in radiographic interpretation, aiding in early detection of oral pathologies like caries and oral cancers. By augmenting diagnostic accuracy, AI mitigates the burden of oral diseases in Pakistan, optimizing resource allocation and improving patient outcomes. Additionally, AI-driven treatment planning streamlines workflows and enhances precision in dental procedures. From predictive modeling for orthodontic outcomes to computer-aided design of dental prosthetics, AI optimizes treatment efficacy and patient satisfaction. Embracing AI as a complementary tool, Pakistani dental professionals can unlock new horizons in care delivery. By integrating AI with traditional methodologies, dentists can navigate this transformative landscape, fostering innovation and excellence in oral healthcare nationwide.

The convergence of artificial intelligence and blockchain in industrial robotics

In an era marked by rapid technological advances, understanding the intersection of artificial intelligence (AI), Blockchain, and industrial robotics is both timely and imperative. The research is motivated by the pressing need for a unified framework that stakeholders can refer to as they navigate the complexities of integrating AI and Blockchain into their robotic systems. Through this paper, the aim is to bridge the existing knowledge gap, highlight the synergistic benefits of combining these technologies, and discuss the challenges and opportunities that lie ahead, thereby providing a well-rounded viewpoint crucial for future research and practical applications. This paper serves as a synthesis of existing literature. As AI and Blockchain individually revolutionise various facets of industrial operations, their combined capabilities could bring about unprecedented levels of efficiency, security, and autonomy. The prospect of integration is full of enormous potential, but it also faces some challenges that require sufficient attention. Technological progress, favorable regulatory frameworks, and firm industry adoption are crucial for achieving the expected benefits of such integration.

THE INTEGRATION OF ARTIFICIAL INTELLIGENCE WITH THE PROCESSES IN HEALTHCARE

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 compliance, interoperability, and workforce readiness must be addressed. Looking ahead, the integration of AI with BPM holds immense potential to revolutionize healthcare delivery and improve patient outcomes. Keywords: Artificial Intelligence (AI), Healthcare, Applications, Benefits, Challenges.

AI Revolution: Safeguarding Tomorrow’s Frontiers - Transforming Cybersecurity Across Industries (A Short Approach)

The article explores the revolutionary effects of artificial Intelligence (AI) on cybersecurity in various industries, emphasizing how important it is to protect sensitive data and strengthen vital infrastructure. Using AI-driven cybersecurity solutions signifies a paradigm change away from reactive, defensive tactics and toward proactive, adaptive, and predictive defense systems. The article illustrates how artificial Intelligence (AI) improves threat detection, guards against fraudulent activity, secure patient data, and strengthens the digital thread in manufacturing and supply chain operations. It focuses on essential industries such as nuclear energy, finance, healthcare, and manufacturing. The convergence of AI and cybersecurity becomes a strategic necessity as sectors negotiate the challenges of a globalized world, providing light on the way to a more inventive, safe, and resilient future.

AI AND IMAGINATION: BRIDGING THE GAP BETWEEN CREATIVITY AND MACHINES

Abstract—The convergence of artificial intelligence and human imagination is reshaping the creative landscape. This exploration examines how AI has evolved from its traditional role to become a catalyst for innovation. Through advanced generative models and neural networks, AI goes beyond automation, allowing creativity to flourish without limitations. However, ethical considerations and societal implications arise as AI-infused imagination explores new frontiers in artistry and problem-solving. The evolving partnership between human ingenuity and machine intelligence opens up collaborative opportunities that unveil exciting possibilities in creativity and innovation. Keywords—Artificial Intelligence, Human imagination, neural networks, generative model, automation.

Use of Digital Twin Technology in the Teaching-Learning Process, in the field of University Education: A Bibliometric Review

At the convergence of artificial intelligence with pedagogy, digital twin technology is emerging as a tool that offers advanced simulations and interactive learning environments. This is because digital twins make it possible to replicate virtually any piece of equipment or test items in a laboratory. Based on the above, the objective of this article is to explore thematic gaps regarding the use of digital twin technology in the teaching-learning process. For this reason, a bibliometric review study is developed taking as data publications indexed in Scopus, during the years 2018 to 2023. Through the search equation, 92 publications were extracted. The study was developed under a quantitative approach at an exploratory-descriptive level. The results demonstrated that there is a growing and sustained scientific production on the topic under study, showing that, during and after the COVID-19 pandemic, there was greater production compared to other years. Furthermore, although it was identified that this technology is applicable to different specialties of higher education, it was determined that, to a greater extent, the topics discussed are engineering education assisted by digital twins, virtual reality and digital twins for engineering training and simulation-based training through digital twins. However, it is concluded that there is an unaddressed gap regarding the rules and regulations that should govern the use of digital twins in higher education as well as what the teacher's competencies should be so as to guarantee the appropriate use of digital twins as part of their teaching methodology, since the impact on student learning depends on this.

Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI

AbstractAs Artificial Intelligence (AI) rapidly advances and integrates into various domains, cybersecurity emerges as a critical field grappling with both the benefits and pitfalls of AI technologies. This paper explores the multifaceted dimensions of AI-driven cyberattacks, offering insights into their implications, mitigation strategies, underlying motivations, and profound societal impacts. The research centres on developing and presenting the AI Cybersecurity Dimensions (AICD) Framework, a comprehensive, multidimensional schema designed to guide academics, policymakers, and industry professionals in understanding and combating the evolving challenges posed by AI-driven cyber threats. The research unveils the complex dynamics of offensive AI, stressing the need for adaptive defences and ethical considerations. Concurrently, the study highlights adversarial AI threats, calling for proactive measures to address their potential ramifications. Through rigorous textual analyses and extensive literature reviews, the paper underscores the urgency for interdisciplinary approaches to bridge the technology-humanity chasm traditionally observed in cybersecurity discussions. By synthesising these diverse elements, the AICD Framework emerges as an instrumental tool for holistic understanding and practical interventions in the AI-infused cybersecurity landscape. The paper concludes with an urgent call for collaborative efforts in research and practice to navigate the intricate challenges and capitalise on the opportunities borne from the convergence of AI and cybersecurity.

A New Era of Healthcare: The Convergence of Artificial Intelligence and Pharmaceuticals

A New Era of Healthcare: The Convergence of Artificial Intelligence and Pharmaceuticals

The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions

Chemical-enhanced oil recovery (EOR) is a field of study that can gain significantly from artificial intelligence (AI), addressing uncertainties such as mobility control, interfacial tension reduction, wettability alteration, and emulsifications. The primary objective of this paper is to introduce an integrated framework for AI and chemical EOR for energy harvest operations. Central emphasis is placed on the energy transition, with the aim of expediting the development of cleaner energy harvesting systems and attaining the goal of net-zero emission. To do so, we present how the energy transition is changing the manufacturing of the chemicals for EOR application. For this, the uncertainty associated with materials' design and critical role of the simulators for transferring the laboratory experiences into full-field implementations is discussed. The concept of digitalization and its impact on energy companies are highlighted. The role of digital twin in simulators integration is discussed, emphasizing how increased data access can help design more tolerant chemicals for harsh reservoir environments using real-time data. Also, we discuss how the chemical suppliers, research institutes, startups, and field operators can benefit from self-leaning and robotic laboratories for chemicals manufacturing. Moreover, this paper explores how including AI perspectives can improve our understanding of developing chemical formulations by blending hybrid capabilities. This approach contributes to making energy production more sustainable and aligning with the goal of zero emissions. A workflow is presented to demonstrate how the integration of AI and chemical EOR can be used for both hydrocarbon production and other energy transition operations, such as carbon capture, utilization and storage, hydrogen storage, and geothermal reservoirs. The outcome of this paper stands as a pioneering effort that uniquely addresses these challenges for both academia and the industry and can open many additional doors and identify topics requiring further investigations.

Adversarial Threats in Climate AI: Navigating Challenges and Crafting Resilience

The convergence of Artificial Intelligence (AI) with climate science is a double-edged sword. AI-enhanced modeling has transformative potential for the field, but it comes with new vulnerabilities, especially from adversarial machine learning. Such adversarial tactics can distort AI-driven climate models, producing misleading projections on phenomena like sea-level changes and temperature predictions. Beyond just mod-eling, AI-enhanced systems in resource management, conserva-tion, and agriculture are at risk. Tampering with climate da-tasets can undermine decades of global research and erode trust, while adversarial misinformation campaigns can skew public discourse. Ethically, distorted data risks magnifying socio-environmental disparities. Addressing these challenges necessitates robust modeling using advanced techniques, data defense with cryptographic solutions, AI-driven infrastructure safeguards, and AI algorithms to detect and counter misinfor-mation. Simply put, securing AI in climate science is not just a technical challenge, but a global imperative.

Enhancing patient information performance in internet of things-based smart healthcare system: Hybrid artificial intelligence and optimization approaches

Utilizing cutting-edge technology in the healthcare industry could provide advanced methods for managing medical records for patients’ treatment. Integrating essential technologies like artificial intelligence (AI) can enhance healthcare services. The intersection of the Internet of Things (IoT) and AI presents various prospects within the healthcare industry. This research paper presents a novel model for enhancing the standard of treatment in smart healthcare systems (SHS) based on the convergence of AI and IoT. We investigate optimizing patient data processing performance in smart healthcare centers. The upgraded particle swarm optimization-long short-term memory (PSO-LSTM) algorithm is introduced to optimize the IoT-based SHS model. In order to perform a more suitable classification of the patient medical data, PSO is determined to be compared with PSO-LSTM to tune several metrics and benchmarks to achieve the highest value on patient data process performance. The proposed outcomes discovered that in terms of the test sets, the predicted values of patient health risks, and just six of them are varied, with an accuracy of 92.5%. This accuracy indicates the PSO-LSTM algorithm has more satisfactory performance and higher efficiency and provides a more secure, reliable, and improved overall patient satisfaction experience.