Administrative Divisions Al-Mustaqbal Center for Artificial Intelligence Applications
How Artificial Intelligence Is Reshaping Production Lines The factory is no longer merely a collection of machines operating according to fixed instructions. It has evolved into a dynamic system capable of sensing, learning, and making decisions. With the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing, production lines are transitioning from traditional automation to intelligent operations capable of adapting instantly to changing demand and operating conditions. 1. Predictive Maintenance Machine learning models analyze vibration, temperature, and electrical current data to predict failures in motors and pumps days or even weeks before they occur. This helps reduce unexpected downtime and significantly lowers maintenance costs. 2. Quality Inspection Using Computer Vision Cameras powered by deep neural networks can inspect thousands of products per minute, detecting cracks and surface defects with accuracy that can exceed human visual inspection. These systems can also classify defects and trace their root causes. 3. Intelligent and Collaborative Robots Robots can learn assembly, welding, and packaging tasks through reinforcement learning, while collaborative robots (Cobots) work safely alongside human workers and adapt to changes in products without requiring lengthy reprogramming. 4. Digital Twins and Simulation A digital twin creates a virtual representation of a factory in which different scenarios can be tested and operational settings optimized before being implemented in the physical environment. This reduces risks and accelerates the transition from design to production. 5. Planning and Supply Chains AI algorithms forecast demand, schedule production, and dynamically manage inventory and procurement. They can also recommend the most cost-effective transportation routes, increasing resilience against supply-chain disruptions. Figure (2). Key Applications of Artificial Intelligence in Modern Factories. 6. Generative Design and Product Development Generative AI can propose hundreds of designs that satisfy constraints related to weight, strength, and cost. This shortens the product development cycle and supports additive manufacturing, including 3D printing. 7. Energy Efficiency and Sustainability Intelligent models can optimize energy consumption for motors, cooling systems, and lighting in real time. They can also facilitate the more efficient integration of renewable energy sources, such as photovoltaic systems, thereby reducing emissions and waste. 8. Occupational Safety and Skills Development Smart cameras can monitor workplace hazards and violations related to the use of personal protective equipment. Augmented reality systems and intelligent assistants can also support workers in training and enable faster, more informed decision-making. Challenges Several challenges remain in the transition toward smart factories, including data quality, industrial information security, high initial implementation costs, and the need for new skills that enable humans to work as partners with machines rather than being replaced by them. In conclusion, the factories of the future will succeed through their ability to learn continuously. Organizations that begin today with carefully planned pilot projects and gradually scale their implementation will be better positioned to respond to the demands of tomorrow's industrial environment. Al-Mustaqbal University – The No. 1 Private University in Iraq
Internet of Things and Its Applications in Smart Cities Introduction Cities around the world are undergoing a fundamental transformation in the way they are managed and operated, driven by the Internet of Things (IoT), which has become a cornerstone of what are known as “Smart Cities.” As the number of people living in urban areas continues to grow, placing increasing pressure on infrastructure and resources, the need for technological solutions that make urban management more efficient and sustainable has become increasingly urgent. The concept of a smart city is based on connecting a wide network of sensors and interconnected devices to collect and analyze data in real time, with the aim of improving residents’ quality of life, optimizing resource consumption, and supporting data-driven decision-making rather than relying on traditional estimates. According to numerous reports in this field, the number of IoT-connected devices worldwide continues to grow, with smart-city applications accounting for a significant share of this expansion. What Does the Internet of Things Mean in an Urban Context? The Internet of Things is a system of devices equipped with sensors and network connectivity that enables them to collect, transmit, and process data with minimal direct human intervention. This system typically consists of four main layers: 1. Sensing Layer: Includes sensors and end devices that collect raw data from the surrounding environment, such as temperature, motion, light, and humidity sensors. 2. Network Layer: Responsible for transmitting data from end devices to processing centers. It relies on various technologies, including fifth-generation (5G) networks and Low-Power Wide-Area Networks (LPWAN), such as LoRaWAN and NB-IoT. 3. Processing Layer: Where data is analyzed either in the cloud through Cloud Computing or at the network edge through Edge Computing to reduce response time. 4. Application Layer: Represents the interface through which end users or city operators interact with the system, whether through a city-monitoring dashboard or a smartphone application for citizens. When this concept is applied across an entire city, streets, buildings, power stations, and public utilities become part of an interconnected system capable of “sensing” what is happening around it and responding intelligently. In many cases, such systems can even predict potential problems before they occur. Key Areas of Application 1. Traffic Management Traffic management is one of the most pressing challenges facing major cities. Sensors embedded in roads and traffic signals, along with smart cameras equipped with computer vision algorithms, are used to collect real-time data on vehicle density, speed, and direction. This data can be used to automatically adjust traffic-light timing according to actual traffic volumes rather than relying on fixed schedules, thereby reducing congestion and waiting times at intersections. These systems also provide real-time data to navigation applications and can identify available parking spaces through sensors installed in parking areas. This reduces the time drivers spend searching for parking and consequently helps reduce congestion and fuel consumption. 2. Smart Lighting Smart streetlights use motion and light sensors to adjust lighting intensity according to actual demand. They can increase brightness when pedestrians or vehicles pass by and dim during periods of low activity, rather than operating at full capacity throughout the night regardless of actual need. This approach not only significantly reduces electricity consumption but also lowers maintenance costs, as smart lighting systems can immediately report faults or the need to replace lamps. This eliminates the need for costly and inefficient routine inspection rounds. Smart lighting poles can also be integrated with additional technologies, such as wireless Internet access points or air-quality monitoring sensors, maximizing the value of existing infrastructure. 3. Waste Management Waste containers can be equipped with fill-level sensors that send alerts when they are approaching capacity to a control center or directly to collection teams. This enables waste-collection routes to be optimized according to actual demand rather than fixed schedules, avoiding unnecessary trips to partially empty containers. As a result, operating costs and fuel consumption can be reduced, emissions from collection vehicles can be minimized, and overall cleanliness in residential areas can be improved. 4. Air Quality and Environmental Monitoring Networks of environmental sensors can be distributed across different areas of a city to measure pollution levels, such as nitrogen oxides and fine particulate matter (PM2.5), as well as humidity, temperature, and noise levels. These networks provide real-time and geographically precise data that help relevant authorities take rapid action when pollutant levels exceed permitted limits, such as issuing health warnings to residents or restricting vehicle movement in specific areas during periods of high pollution. In the long term, these data can also support urban planning by identifying areas most affected by pollution and guiding environmental policies based on quantitative evidence rather than general estimates. 5. Water and Energy Consumption Management Connected Smart Meters enable real-time monitoring of water and electricity consumption at the level of individual households or facilities, replacing traditional periodic meter readings. This not only provides more accurate billing but also enables early detection of leaks or faults in distribution networks, a problem that can traditionally result in significant water losses before being identified. At the energy level, these systems help balance electrical loads across different areas and facilitate the integration of renewable energy sources, such as solar power, into the city’s electricity grid more efficiently. This concept is known as the Smart Grid. 6. Public Safety and Emergency Response Smart surveillance cameras equipped with video-analysis algorithms, along with acoustic sensors capable of identifying unusual sounds such as explosions or screams, can help detect accidents and emergencies as they occur. This information can be transmitted as immediate alerts to police, ambulance, or civil-defense teams, significantly reducing response times compared with relying solely on reports from citizens. Such systems can enhance safety in public spaces, including squares, transportation stations, and schools. 7. Smart Buildings At the individual-building level, IoT systems can automatically control air conditioning, ventilation, and lighting based on actual occupancy, significantly reducing energy consumption in large commercial and residential buildings. Smart security systems, such as connected door locks and intelligent fire alarms, can also be integrated into the same ecosystem to provide a safer and more efficient environment. The Role of Artificial Intelligence and Communications in Enhancing These Applications The success of IoT applications cannot be separated from advances in wireless communication technologies and Artificial Intelligence (AI). With the transition to fifth-generation networks, and the future potential of sixth-generation networks, it is becoming possible to connect millions of devices within the same city without significant degradation in performance, thanks to features such as ultra-low latency and high connection density. At the same time, the vast amounts of data generated by these networks have little practical value without AI algorithms capable of analyzing them and extracting useful patterns. These technologies can be used to predict traffic congestion, detect faults in water and electricity networks at an early stage, and optimize building energy consumption based on residents’ behavioral patterns. Existing Challenges Despite their significant benefits, IoT applications in smart cities face several challenges, including: High infrastructure costs: Deploying millions of sensors and upgrading communication networks requires substantial investment, which can be a major obstacle, particularly in developing countries. Privacy concerns: The collection of vast amounts of residents’ data raises serious questions regarding how such data are stored and used and who has access to them. Cybersecurity: Every connected device represents a potential vulnerability that could be exploited in cyberattacks. A breach of a single system could potentially disrupt critical services across an entire city. Interoperability between systems: The diversity of manufacturers and communication protocols creates challenges in ensuring that different devices can communicate seamlessly within a unified ecosystem. Dependence on reliable communication infrastructure: Efficient operation of these systems requires stable and fast communication networks. This makes advanced technologies such as 5G and future 6G networks increasingly important for the expansion of smart-city applications, particularly in areas with lower population density where network coverage may be weaker. Conclusion The Internet of Things represents a fundamental pillar in the transformation of traditional cities into more efficient, sustainable, and secure urban environments. As sensor costs continue to decline, wireless communication networks advance toward sixth-generation technologies, and Artificial Intelligence algorithms become increasingly capable of analyzing massive datasets, these applications are expected to expand into deeper aspects of citizens’ daily lives. However, achieving this transformation in a balanced manner requires serious attention to challenges related to privacy, cybersecurity, and cost. This is essential for transforming the “Smart City” from an ambitious theoretical concept into a tangible reality that serves people before serving technology itself. Al-Mustaqbal University — The No. 1 Private University in Iraq
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) into the agricultural sector represents a pivotal pillar for achieving global food security. This study provides a comprehensive analysis of smart agricultural applications, ranging from the early detection of pests and diseases and precision monitoring of soil and water resources to robotic field automation, supply chain management, and climate adaptation analytics. 1. General Framework and the Transition toward Digital Agriculture The global agricultural sector is undergoing a historic transformation known as the Fourth Agricultural Revolution (Agriculture 4.0). This transformation is driven by increasing pressures resulting from continuous population growth, which requires a substantial increase in food production by 2050, alongside environmental challenges including freshwater scarcity, soil degradation, and severe climate variability. Historically, conventional agriculture has relied heavily on field experience and uniform decision-making across entire fields, often resulting in excessive consumption of water, fertilizers, and pesticides. In contrast, smart agriculture relies on collecting big data from multiple sources and analyzing it through Artificial Intelligence algorithms to transform agricultural management from generalized practices into highly precise, data-driven decision-making systems. 2. Early Diagnosis of Plant Diseases and Agricultural Pests Plant pests and fungal, bacterial, and viral diseases pose a direct threat to crops, causing significant annual agricultural losses worldwide. Early diagnosis using computer vision and deep learning represents a critical line of defense for protecting agricultural productivity. First: Digital Detection and Image Processing Smart diagnostic systems rely on training Convolutional Neural Networks (CNNs) using advanced models such as ResNet and YOLO. The diagnostic process generally consists of four main stages: Data Acquisition: Capturing high-resolution images using field cameras, unmanned aerial vehicles (drones), or farmers’ mobile phones. Preprocessing: Adjusting lighting and contrast and removing visual noise to identify the characteristics of infected leaves or fruits. Feature Extraction: Identifying subtle patterns such as color changes, fungal spots, leaf deterioration, and geometric deformations. Predictive Classification: Identifying the type of disease or pest, estimating its prevalence, and, where appropriately validated, recommending suitable treatment measures. Second: Multispectral Imaging and Pre-Visual Analysis Multispectral and hyperspectral cameras, when integrated with Artificial Intelligence models, can detect plant stress before visible symptoms appear to the human eye. This is achieved by measuring changes in light reflectance across non-visible spectral bands, such as the near-infrared range, which can provide indicators associated with changes in photosynthetic activity and chlorophyll levels within plant tissues. 3. Water Resource Management, Smart Irrigation, and Predictive Adaptation Agriculture accounts for a major share of global freshwater withdrawals. AI-based irrigation systems aim to maximize water-use efficiency and achieve the principle of “more crop per drop.” AIoT-Based Adaptive Irrigation Systems Wireless sensor networks can be deployed at different soil depths to measure moisture levels, temperature, and salinity. These sensors transmit real-time data to a centralized platform that uses machine-learning algorithms to estimate evapotranspiration and determine crop water requirements in real time. Integration with Weather Forecasts Smart irrigation systems do not rely solely on current soil conditions. They can also integrate data from weather stations and climate models. When rainfall is forecast in the coming hours, the system can automatically adjust or cancel irrigation schedules, helping prevent excessive irrigation, root damage, and unnecessary water consumption. 4. Automated Soil, Fertilization, and Weed Management Balanced soil nutrition and targeted weed control are essential components of maintaining agricultural soil fertility and crop health. Interactive Soil Maps and Variable-Rate Fertilization By analyzing soil samples and satellite imagery using machine-learning techniques, digital maps can be generated to represent the distribution of major nutrients, including nitrogen, phosphorus, and potassium (NPK), as well as soil acidity (pH). These maps can be integrated into automated variable-rate fertilization systems to ensure that precise quantities of nutrients are applied to specific areas of the field according to their requirements. Smart Precision Spraying for Weed Control Smart sprayers use high-speed cameras and computer-vision models to distinguish between crops and weeds in real time as agricultural machinery moves through the field. Spray nozzles are activated only when weeds are detected, potentially reducing chemical pesticide use compared with conventional blanket spraying, depending on crop type, equipment, and field conditions. 5. Agricultural Robots and Autonomous Equipment Agricultural robots equipped with intelligent navigation and perception systems can help address seasonal labor shortages while increasing precision in labor-intensive agricultural operations. High-Precision GPS (RTK) and LiDAR Autonomous tractors and agricultural machinery use Real-Time Kinematic (RTK) GPS, LiDAR, and optical cameras to navigate automatically through agricultural fields and perform operations such as plowing and land leveling with high positional accuracy, reducing the need for continuous manual driving. Robots for Harvesting Delicate Fruits Fruit-picking robots employ articulated robotic arms equipped with tactile and visual sensors to assess the maturity of crops such as tomatoes, strawberries, and apples. These systems are designed to select and harvest fruits carefully while minimizing physical damage. Automated Greenhouse Management Smart greenhouse systems use machine-learning techniques to dynamically regulate temperature, humidity, carbon dioxide concentration, and artificial LED lighting to maintain favorable growing conditions around the clock. 6. Supply Chains, Yield Forecasting, and Climate Adaptation The impact of Artificial Intelligence extends beyond the farm to the market and consumer, providing integrated solutions that can contribute to food security. Accurate Crop Yield Forecasting and Economic Management Predictive algorithms can process historical weather data, vegetation density, growth rates, and historical agricultural records to estimate total crop yields weeks before harvesting. This enables farmers, traders, and logistics providers to plan transportation and storage operations in advance and potentially reduce market disruptions. Reducing Losses and Tracking Supply Chains The integration of Artificial Intelligence with Blockchain technology and tracking sensors can facilitate continuous monitoring of transportation and storage conditions, including temperature and humidity. The system can generate real-time alerts when abnormal conditions or refrigeration failures are detected, helping reduce crop losses during transportation. Genetic Engineering and the Development of Resilient Varieties Deep-learning algorithms can be used to analyze plant genomic sequences and identify genes associated with water tolerance, salinity resistance, and tolerance to high temperatures. This can accelerate plant breeding and hybridization programs aimed at developing crop varieties better adapted to climate change. 7. Field Challenges and Future Perspectives Despite the significant potential of Artificial Intelligence technologies in agriculture, several major challenges remain in achieving widespread adoption: Initial Investment Costs The high capital costs associated with purchasing IoT devices, agricultural robots, and unmanned aerial vehicles can represent a significant barrier, particularly for small-scale farmers. Digital Infrastructure Limited access to high-speed communication networks, including 4G and 5G, in many rural and remote areas can restrict real-time data transmission and connectivity. Data Quality and Training Deep-learning models require large, accurately labeled datasets that adequately represent local crops, environmental conditions, and weather patterns in order to achieve reliable performance. Future Prospects The future of smart agriculture is expected to move increasingly toward Edge AI, which enables real-time data processing directly on agricultural machinery and robotic devices without requiring continuous Internet connectivity. At the same time, declining sensor costs and the growing availability of Software as a Service (SaaS) and cloud-based agricultural solutions may make these technologies increasingly accessible to small-scale farmers. Such developments could accelerate the global transition toward more efficient, resilient, and sustainable agricultural systems. Al-Mustaqbal University — The No. 1 Private University in Iraq
Artificial intelligence is no longer confined to applications such as image recognition or the voice assistants we use every day. Rather, it has begun to quietly penetrate one of the most important infrastructures in our lives: telecommunications networks. Every phone call we make, every message we send, and every video we watch online passes through a complex network of stations, towers, and servers operating around the clock to transmit data at the highest possible speed and with the lowest possible error rate. Today, artificial intelligence has become an essential partner in managing and developing these networks, and it is increasingly regarded as one of the fundamental pillars of the next generation of telecommunications, known as the Sixth Generation (6G). Why Do Networks Need Artificial Intelligence in the First Place? Modern telecommunications networks handle enormous amounts of data at every moment: millions of connected devices, continuous changes in signal quality caused by weather, physical obstacles, or congestion, and usage patterns that vary from one hour to another and from one geographical area to another. Managing all this complexity manually, or even through conventional fixed algorithms, has become an almost impossible task. This is where artificial intelligence comes into play. AI is distinguished by its ability to learn from data and dynamically adapt to changes, rather than relying on rigid, predefined rules. Where Does Artificial Intelligence Actually Appear in the World of Telecommunications? Automatic Network Performance Optimization: Machine learning algorithms can monitor network conditions moment by moment, predict potential congestion areas, and proactively redistribute resources, such as bandwidth, before users experience any slowdown or interruption. Predictive Maintenance: Instead of waiting for a transmission station to fail and then repairing it, artificial intelligence models can analyze performance data and detect early indicators of potential problems. This allows preventive intervention and reduces downtime. Improving Communication Quality over Wireless Channels: Wireless channels are affected by many factors, including noise, interference, and signal reflections, a phenomenon technically known as fading. Intelligent models can predict channel behavior and adapt to it more effectively than traditional methods, thereby improving the quality of transmitted voice, video, and data. Self-Organizing Networks: Research is moving toward networks capable of diagnosing their own problems and adjusting their configurations automatically without direct human intervention. This concept is sometimes referred to as self-driving networks or autonomous networks. Sixth Generation (6G): When Artificial Intelligence Becomes Part of the Network Architecture While previous generations of telecommunications networks, such as 4G and 5G, have used artificial intelligence as a supporting tool to improve performance, the vision proposed for 6G goes much further: integrating artificial intelligence into the core design of the network from the outset, rather than treating it as an additional technology introduced later. 6G networks are expected to rely on artificial intelligence models to make real-time decisions regarding data routing, resource allocation, and even the prediction of users’ needs before they arise. This could open the door to entirely new applications, such as high-quality extended reality, remote control of industrial robots with near-zero response times, and communication among millions of smart devices simultaneously. Challenges That Cannot Be Ignored Despite these promising capabilities, significant challenges remain. Training and operating artificial intelligence models require substantial computational resources and energy, which is particularly important in telecommunications environments that are expected to be energy efficient. Privacy and data security are also important concerns, as the highly accurate analysis of users’ usage patterns raises legitimate questions about how such information can be protected. There is also the challenge of trust: How can we ensure that decisions made automatically by the network and based on artificial intelligence are reliable and explainable, particularly in sensitive applications such as remote medical communications or emergency systems? Conclusion The convergence of artificial intelligence and telecommunications is not merely a passing technological trend; rather, it represents a fundamental transformation in the way networks are designed and managed. While networks were once systems governed by fixed rules, they are now evolving into intelligent systems capable of learning, adapting, and predicting. As the world approaches the era of sixth-generation telecommunications, it is becoming increasingly clear that the future of telecommunications will not be shaped independently of developments in artificial intelligence. Instead, the two will become deeply interconnected—two sides of the same technological coin.
Second: The Impact of Generative Artificial Intelligence on Learning Efficiency • Personalized Learning Unlike the traditional model based on a standardized curriculum for all students, Generative AI systems enable the development of adaptive learning pathways tailored to each learner’s level and pace of comprehension. This is achieved by analyzing the learner’s performance and providing explanations and exercises specifically designed to address individual knowledge gaps. • A 24/7 Educational Assistant Generative AI provides students with the ability to ask questions and receive immediate, detailed answers at any time. This reduces their complete dependence on lecturers or peers and provides them with greater opportunities for self-directed learning. • Enhancing Creative and Applied Thinking When used consciously and responsibly, the role of Generative AI is not limited to providing ready-made answers. It can instead be employed to propose hypothetical scenarios, generate project ideas, and simulate practical cases that develop learners’ problem-solving skills. Third: Challenges and Associated Risks Despite these advantages, several fundamental challenges emerge that require serious critical consideration: • Overreliance and the Decline of Critical Thinking Skills: Excessive dependence on Generative AI to complete tasks may weaken students’ or researchers’ ability to analyze independently and formulate their own arguments. • Issues of Scientific Originality and Academic Integrity: The use of these tools in research writing raises serious questions concerning intellectual property and the need to establish clear regulations governing responsible and transparent use. • Knowledge Hallucinations: AI models may generate inaccurate information or references that are incorrect or do not actually exist. This necessitates careful human verification of all outputs generated by these systems before relying on them. • The Digital Divide Between Institutions: Universities differ in their ability to provide the infrastructure and training required to make optimal use of these technologies. This may widen the gap between resource-rich institutions and those with limited capabilities. Fourth: Toward Conscious Integration Rather Than Blind Replacement The optimal approach to Generative AI in the academic environment does not lie in rejecting it or surrendering completely to it. Rather, it lies in developing a balanced framework that employs AI as a supportive partner to human intellect rather than a substitute for it. This requires: Developing clear institutional policies regulating the ethical use of these tools in teaching and research. Training faculty members and researchers in the critical and effective use of these technologies. Promoting a culture of verification and reviewing AI-generated outputs rather than accepting them blindly. Redesigning assessment methods to reflect a reality in which these tools have become part of the learning environment. The academic realm today stands before a profound and irreversible transformation, driven by Generative AI technologies as a force that amplifies scientific efficiency and productivity. Yet the essence of this revolution does not lie in the technology itself, but in the awareness of academics regarding how to employ it: a critical mind directing the tool, rather than a tool that silences the mind. The promising academic future is therefore one that combines the computational power of artificial intelligence with the originality of critical human thought, serving the mission of science in expanding the horizons of knowledge rather than reducing its depth and richness. Dr. Eng. Ferial Ibrahim Jabbar
Using Artificial Intelligence to Detect Faults in Medical Devices Abstract Medical devices have become an essential component of the modern healthcare system. Hospitals and medical centers rely on a wide range of devices, including vital signs monitoring systems, medical imaging equipment, anesthesia and ventilation devices, and laboratory equipment. As these devices have become increasingly complex, their maintenance and the early detection of faults have emerged as important challenges facing healthcare institutions. Artificial intelligence has contributed to the development of modern approaches for detecting and predicting faults before they occur by analyzing data generated by medical devices and identifying abnormal patterns. This article aims to explain the role of artificial intelligence in detecting faults in medical devices, the main technologies used, and the benefits they provide, in addition to the challenges and requirements necessary for their safe and effective implementation. Keywords: Artificial Intelligence, Medical Devices, Fault Detection, Predictive Maintenance, Machine Learning, Hospitals, Medical Internet of Things. 1. Introduction Healthcare institutions are continuously advancing in their use of medical technology. Modern medical devices increasingly rely on sophisticated electronic and software systems to perform precise and sensitive functions. A malfunction in any of these devices can disrupt medical services and affect operational efficiency and patient safety, particularly when the fault involves critical medical equipment. Traditionally, the maintenance of medical devices has relied on periodic maintenance or intervention after a malfunction occurs. However, this approach may result in unexpected equipment downtime and increased maintenance costs. Therefore, artificial intelligence has emerged as an important technology capable of continuously analyzing device data and identifying abnormal changes that may indicate an underlying problem. This approach enables a transition from reactive maintenance, which takes place after a failure occurs, to predictive maintenance, which aims to identify early indicators of failure and intervene before the failure occurs. 2. The Concept of Artificial Intelligence in Medical Device Maintenance The use of artificial intelligence in medical device maintenance refers to employing algorithms capable of analyzing the operational data of a device and identifying patterns that may be associated with faults. Such data may include: Device temperature. Energy consumption levels. Vibrations. Operating pressure. Number of operating hours. Self-test results. Error codes and messages. Sensor data. Previous maintenance records. Artificial intelligence algorithms learn from historical data and then use what they have learned to detect abnormal conditions or predict the likelihood of future failures. 3. How Does Artificial Intelligence Detect Faults? The process begins by continuously or periodically collecting data from the medical device. The data are then transmitted to an analytical system based on artificial intelligence algorithms. The algorithm subsequently compares the current data with the normal patterns it has previously learned. If an unusual change is detected, the system can issue an alert to the maintenance team. For example, if a medical device normally operates within a specific temperature range, but the system begins to detect a gradual and continuous increase in temperature, this may indicate a potential problem with the cooling system or one of the internal components. Thus, the system does not wait for a complete failure to occur; instead, it attempts to identify the early indicators that precede the failure. 4. Major Artificial Intelligence Technologies Used 4.1 Machine Learning Machine learning is one of the most important technologies used for fault detection. Algorithms are trained using historical data that include both normal operating conditions and fault conditions. After training, the algorithm can classify new data and determine whether they indicate a normal condition or a potential malfunction. 4.2 Anomaly Detection Anomaly detection algorithms are used to identify unusual behavior in a medical device, even when there is no clear record of all possible types of faults. This technique is particularly useful when fault data are limited compared with normal operating data. 4.3 Neural Networks Neural networks can be used to process large and complex datasets and identify relationships between different variables that may be difficult to detect using traditional methods. They can be particularly useful for devices that generate multiple types of data simultaneously, such as medical imaging systems and patient monitoring systems. 4.4 Sensor Data Processing Many modern medical devices contain sensors that monitor temperature, pressure, energy consumption, vibration, and other variables. Artificial intelligence can analyze these data and detect abnormal changes. 5. Applications of Artificial Intelligence in Detecting Medical Device Faults Artificial intelligence can be applied to various types of medical devices, including: Patient Monitoring Devices The performance of vital signs monitoring devices can be continuously monitored to detect abnormal changes in signals or sensor performance. Ventilators Pressure, flow, and operating-rate data can be analyzed to identify indicators that may suggest a technical problem requiring inspection. Medical Imaging Devices Artificial intelligence systems can be used to monitor operational indicators of imaging equipment, such as temperature, power consumption, and error logs, to help predict potential failures. Laboratory Equipment Laboratory devices generate large amounts of operational data that can be analyzed to detect irregularities that may affect device performance or the accuracy of test results. Anesthesia Devices Operational indicators and alarm systems can be monitored to enable early detection of technical problems that may require intervention by the maintenance team. 6. Predictive Maintenance of Medical Devices Predictive maintenance is one of the most important practical applications of artificial intelligence. Its concept is based on using data to predict the likelihood of a failure before it occurs. For example, rather than waiting for a medical device to stop unexpectedly, the system can detect a pattern indicating deterioration in one of its components and then send an alert to the biomedical engineering department to conduct an inspection or maintenance procedure. This helps healthcare institutions plan maintenance activities and reduce unexpected equipment downtime. 7. Benefits of Using Artificial Intelligence The use of artificial intelligence to detect faults in medical devices provides several benefits, including: Early detection of faults before complete equipment failure occurs. Reducing equipment downtime and service interruptions. Improving maintenance efficiency by directing maintenance teams toward devices that require intervention. Reducing long-term maintenance costs. Extending the operational lifespan of medical devices by identifying problems at an early stage. Improving spare-parts management by predicting maintenance requirements. Enhancing the reliability and safety of devices used in healthcare. Improving administrative planning by continuously monitoring the operational status of medical devices. 8. Challenges Despite its significant benefits, the implementation of artificial intelligence in medical device maintenance faces several challenges. Data Quality AI systems require accurate and sufficient data. Fault data may be limited, rare, or poorly organized, making the training of AI models more difficult. Device Diversity Medical devices vary in terms of manufacturers, technologies, and operating systems. Therefore, a single artificial intelligence model may not perform efficiently across all types of devices. Cybersecurity Connecting medical devices to networks and analytical systems may increase the need to protect these systems against cyberattacks and unauthorized access. Dependence on the System Artificial intelligence systems should not eliminate the role of biomedical engineers or maintenance technicians. AI provides alerts and predictions, while the final technical decision should remain with qualified specialists. Cost and Training Implementing these systems requires appropriate sensors, digital infrastructure, specialized software, and training for personnel to effectively use and interact with the new technologies. 9. The Role of the Biomedical Engineer The use of artificial intelligence does not mean eliminating the need for biomedical engineers. Rather, it can help them perform their work more efficiently. Instead of inspecting a large number of devices equally, an engineer can use an intelligent system to obtain a list of devices showing abnormal indicators and then conduct the necessary technical inspection and make the appropriate decision. Thus, artificial intelligence becomes a supportive tool for the biomedical engineer rather than a replacement for their expertise. 10. The Future of Artificial Intelligence in Medical Device Maintenance The use of artificial intelligence is expected to expand with the increasing adoption of network-connected medical devices and Medical Internet of Things (IoMT) technologies. Hospitals may eventually be able to establish centralized systems capable of monitoring the operational status of thousands of devices simultaneously. In the future, AI systems may also be integrated with hospital management systems to create an electronic record for each device, including its operational lifespan, fault history, maintenance activities, spare parts used, and predicted future maintenance requirements. 11. Conclusion Artificial intelligence represents an important opportunity for developing the management and maintenance of medical devices, particularly through early fault detection and predictive maintenance. Analyzing operational data helps identify abnormal patterns and predict potential problems, thereby contributing to reduced equipment downtime, improved maintenance efficiency, and better utilization of resources. However, the successful implementation of this technology requires reliable data, appropriate infrastructure, secure systems, and qualified personnel capable of interpreting AI-generated results. Therefore, the most effective approach is to integrate artificial intelligence with the expertise of biomedical engineers and healthcare professionals in order to achieve greater efficiency, enhanced safety, and improved continuity of medical services.
Artificial Intelligence in the Management of Healthcare and Medical Institutions Abstract Artificial intelligence (AI) has become one of the modern technologies that is increasingly influencing various sectors, with the healthcare sector being one of the most prominent areas that can benefit from its applications. AI technologies have contributed to the development of healthcare and medical institution management practices through data analysis, resource allocation optimization, administrative and medical decision support, healthcare service demand forecasting, and improved management of appointments, hospital beds, and medications. This article aims to explain the role of artificial intelligence in the management of healthcare and medical institutions, highlighting its major applications and benefits, as well as the challenges associated with its use and the requirements necessary for its effective and safe implementation. Keywords: Artificial Intelligence, Healthcare Management, Medical Institutions, Hospitals, Decision-Making, Health Data, Digital Transformation. 1. Introduction Healthcare and medical institutions are experiencing an accelerated digital transformation as a result of the significant growth in the volume of health data and the increasing need to provide high-quality healthcare services with greater efficiency. Modern healthcare management is increasingly relying on digital technologies to address challenges such as rising healthcare costs, shortages of human resources, increasing numbers of patients, and the need to make rapid decisions based on accurate data. In this context, artificial intelligence represents one of the most important technologies capable of supporting healthcare management. Artificial intelligence refers to a set of computational techniques that enable systems to perform tasks that normally require human capabilities, such as learning from data, recognizing patterns, making predictions, and supporting decision-making. The role of artificial intelligence in healthcare institutions is not limited to disease diagnosis or medical image analysis; rather, it extends to administrative and operational aspects, making AI an important tool for improving institutional efficiency and enhancing the quality of services provided to patients. 2. Importance of Artificial Intelligence in Healthcare Management Healthcare institutions deal daily with large volumes of information related to patients, physicians, appointments, medications, medical equipment, and financial and human resources. Analyzing such data using traditional methods has become increasingly difficult as its volume and complexity continue to grow. Artificial intelligence can help healthcare management transform available data into actionable information that supports decision-making. For example, machine learning algorithms can analyze historical patient data to predict increases in the number of patients during specific periods, enabling management to plan staffing levels and medical supplies accordingly. AI can also be used to improve operational processes and reduce wasted time, including appointment scheduling, waiting-list management, resource allocation, inventory monitoring, and optimizing the utilization of operating rooms and hospital beds. 3. Major Applications of Artificial Intelligence in Healthcare Institution Management 3.1 Administrative Decision Support Artificial intelligence can analyze historical and real-time data and provide indicators and forecasts that assist managers in making decisions. It can be used to identify future requirements for employees, medical equipment, and supplies, analyze the performance of different departments, and detect abnormal patterns in administrative operations. Consequently, administrative decision-making can move from relying entirely on personal experience toward a model that combines human expertise with data-driven analysis. 3.2 Human Resource Management Human resources represent one of the most important elements in the success of healthcare institutions. Artificial intelligence can assist in preparing staff shift schedules, analyzing staffing requirements across departments, and predicting periods of increased workload. AI systems can also analyze performance indicators to help management identify training needs and improve staff allocation. However, it is essential to ensure that decisions concerning employees are not based solely on an automated algorithm that may contain biases. 3.3 Appointment and Waiting-List Management Artificial intelligence can be used to predict the likelihood of a patient missing an appointment, improve appointment scheduling, and reduce waiting times. Intelligent systems can also analyze expected demand for different clinics and provide management with recommendations regarding the distribution of appointments and resources. 3.4 Hospital Bed and Operating Room Management Hospitals continuously face challenges in managing beds and operating rooms due to variations in the number of patients and their healthcare needs. Artificial intelligence can analyze data related to patient admissions and discharges and predict the expected demand for hospital beds. In operating rooms, predictive models can be used to support scheduling, estimate procedure durations, improve resource utilization, and potentially reduce delays. 3.5 Inventory and Medication Management Artificial intelligence technologies can assist in analyzing consumption rates of medications and medical supplies and forecasting future requirements. This can reduce the likelihood of essential supplies running out or unnecessary inventory accumulating. Intelligent systems can also be integrated with inventory management systems to send alerts when stock levels decline or when certain products are approaching their expiration dates. 3.6 Performance and Quality Analysis Artificial intelligence can continuously analyze performance and quality indicators, such as hospital readmission rates, waiting times, resource utilization, and patient satisfaction indicators. These analyses help management identify problems at an early stage and determine areas that require improvement. 3.7 Support in the Medical Field In addition to administrative applications, artificial intelligence can support healthcare professionals in certain medical tasks, such as medical image analysis, diagnostic support, electronic health record analysis, and identifying patients who may be at higher risk of certain health conditions. However, these systems should be regarded as tools that assist healthcare professionals rather than replacements for human medical judgment, particularly in decisions requiring comprehensive clinical and ethical assessment. 4. Benefits of Using Artificial Intelligence in Healthcare Institutions The major expected benefits can be summarized as follows: Improving operational efficiency through the automation of certain tasks and better resource utilization. Supporting decision-making by rapidly analyzing large volumes of data. Improving the quality of healthcare services through problem detection and continuous monitoring of performance indicators. Reducing waste and costs through improved resource and inventory management. Enhancing the patient experience by reducing waiting times and improving appointment organization. Forecasting future needs for healthcare personnel, hospital beds, medications, and medical supplies. Improving risk management by detecting patterns that may indicate operational or health-related problems. 5. Challenges of Implementing Artificial Intelligence Despite its benefits, the implementation of artificial intelligence in healthcare institutions faces several challenges. 5.1 Data Privacy and Security Health data are among the most sensitive types of information. Therefore, patient data must be protected from unauthorized access or inappropriate use. This requires the implementation of strong cybersecurity policies, access-control mechanisms, and effective data governance. 5.2 Data Quality The effectiveness of artificial intelligence systems depends heavily on the quality of the data used for their training and operation. If the data are incomplete, inaccurate, or inadequately representative of the population, AI algorithms may produce unreliable predictions or decisions. 5.3 Algorithmic Bias Historical data may contain biases that already exist within the healthcare system. Consequently, these biases can be transferred to artificial intelligence systems. Therefore, AI models should be continuously evaluated to ensure their fairness and suitability for different population groups. 5.4 Cost and Infrastructure Implementing artificial intelligence requires investment in digital infrastructure, information systems, data storage, cybersecurity, and employee training. 5.5 Resistance to Change The introduction of intelligent systems may encounter resistance from some employees due to concerns about job displacement or difficulties in dealing with new technologies. Therefore, training, institutional communication, and employee involvement in the transformation process are important factors for successful implementation. 6. Requirements for Successful Artificial Intelligence Management To benefit effectively from artificial intelligence, healthcare institutions should adopt a gradual approach that begins by identifying problems that technology can help solve, rather than purchasing technological solutions without a clearly defined need. Institutions should also establish an artificial intelligence governance framework that defines responsibilities, oversight mechanisms, and evaluation procedures while ensuring data protection and transparency in the use of AI systems. It is also important that sensitive decisions remain subject to human oversight and that AI systems be tested before large-scale implementation. Their performance should also be continuously monitored after deployment to ensure that their accuracy and effectiveness are maintained. 7. The Role of Senior Management in the Transition to Artificial Intelligence Senior management has a fundamental responsibility for the success of digital transformation within healthcare institutions. This includes developing a clear AI strategy aligned with the institution's objectives, allocating the necessary resources, developing digital infrastructure, and providing training for employees. Management must also strike a balance between benefiting from technology and preserving the fundamental principles of healthcare, such as patient safety, privacy, fairness, and professional responsibility. 8. The Future of Artificial Intelligence in Healthcare Management The use of artificial intelligence in healthcare management is expected to expand with the development of machine learning, natural language processing, and big data analytics. Healthcare systems may become increasingly capable of forecasting demand for healthcare services, optimizing resource allocation, and providing timely administrative and medical information. The use of digital assistants in administrative tasks, health record analysis, and report preparation is also expected to increase, while human oversight and effective governance will remain essential. 9. Conclusion Artificial intelligence has become one of the most important tools capable of contributing to the development of healthcare and medical institution management. Its role is not limited to diagnostic applications but extends to human resource management, appointments, hospital beds, inventory, quality management, performance analysis, and administrative decision support. Nevertheless, the successful implementation of artificial intelligence does not depend on technology alone. It requires high-quality data, appropriate infrastructure, qualified personnel, clear governance frameworks, and strong privacy and information security measures. Therefore, the goal of transitioning toward artificial intelligence should be to enhance the capabilities of healthcare professionals and improve the quality and efficiency of healthcare services, rather than replacing human judgment in sensitive decisions. Accordingly, healthcare institutions that adopt artificial intelligence as part of an integrated and well-planned strategy will be better equipped to address future challenges and improve the efficiency of their services, provided that they adhere to the ethical and regulatory principles governing the use of these technologies.
Neuralink: When Human Intelligence Meets Artificial Intelligence – Towards a New Era of Brain–Computer Interfaces By: Prof. Dr. Mahdi Abadi Mani Director, Al-Mustaqbal Center for Artificial Intelligence Applications – Al-Mustaqbal University Introduction Artificial intelligence is no longer limited to software systems operating inside computers and smart devices. It is increasingly moving toward one of the most complex domains of human existence: the human brain. This transformation has contributed to the rapid development of Brain–Computer Interfaces (BCIs), a field of neurotechnology that seeks to establish a direct communication pathway between neural activity and digital systems. Within this emerging field, Neuralink has attracted considerable scientific and public attention through its development of implantable brain–computer interface technologies. The company's early human studies have focused on evaluating the safety and functionality of its neural implant and its potential to assist individuals with severe motor disabilities in interacting with digital devices. The significance of Neuralink therefore extends beyond a single technological project. It represents a convergence of neuroscience, artificial intelligence, biomedical engineering, robotics, and computer science that may fundamentally reshape the relationship between humans and machines. What Are Brain–Computer Interfaces? A Brain–Computer Interface is a technological system designed to acquire neural activity, process it, and translate it into commands that can be interpreted and executed by an external computer or device. When a person intends to perform a particular movement, the brain generates patterns of neural activity associated with that intention. A BCI attempts to capture these patterns and use signal-processing and artificial-intelligence algorithms to decode them. The basic process can therefore be represented as: Neural activity → Signal acquisition → Data processing → AI-based decoding → Command generation → External action Artificial intelligence is particularly important because neural activity does not naturally appear as simple digital instructions. Instead, it consists of complex and dynamic patterns that require computational models capable of identifying meaningful relationships between neural signals and intended actions. Neuralink: From Concept to Application Neuralink's approach combines an implanted neural device, microscopic electrodes, signal-processing electronics, wireless communication, and a specialized robotic system designed to assist with implantation. The scientific objective is to record neural activity through a large number of neural contacts and translate the resulting signals into useful commands. Available scientific and technical descriptions indicate that the N1 implant incorporates 1,024 electrodes distributed across 64 flexible threads, a configuration designed to provide extensive neural recording capability within a compact implanted system. However, the scientific value of such a system is not determined simply by the number of electrodes. The central challenge is whether the recorded neural signals can be translated into reliable, accurate, and clinically meaningful information while maintaining long-term safety. Artificial Intelligence: The Computational Layer of the BCI Artificial intelligence can be considered one of the central computational components of modern BCI systems. The brain produces complex electrical activity that changes over time, whereas digital systems require interpretable inputs. Machine-learning and deep-learning methods can therefore be used to identify patterns in neural activity and associate them with intended movements or commands. A system may, for example, learn the relationship between a particular neural pattern and a user's intention to move a computer cursor in a specific direction. With repeated use, adaptive algorithms may also learn to accommodate changes in neural signals. This creates the possibility of a mutual learning relationship between humans and machines, rather than a simple one-way transfer of information. This aspect makes Neuralink particularly relevant to artificial intelligence research, as future BCIs may increasingly rely on adaptive models capable of processing neural information in real time. Medical Applications The most significant potential contribution of implantable BCIs is their medical application. Individuals with spinal cord injuries or severe neurological disorders may retain their cognitive abilities while losing the ability to move or communicate through conventional physical mechanisms. In such circumstances, a BCI may provide an alternative pathway between neural intention and external technology. Potential applications include: 1. Computer Control Neural interfaces may assist individuals with severe motor impairments in controlling computer cursors, navigating applications, typing, and interacting with digital environments. 2. Communication Restoration Decoding neural activity associated with speech represents one of the most promising directions in BCI research. In the future, neural signals may potentially be used to support communication for individuals who have lost the ability to speak. 3. Robotic and Assistive Devices BCIs may enable users to control prosthetic limbs or robotic assistive systems through decoded motor intentions. Neuralink has also expanded its research toward the control of assistive robotic systems, representing a potential transition from digital interaction toward the restoration of physical capabilities. 4. Neurological Rehabilitation BCI technologies are also being investigated as tools for neurological rehabilitation, including approaches intended to support the recovery of motor functions. Scientific Ambition and Clinical Reality Despite the enormous attention surrounding Neuralink, it is scientifically important to distinguish between future possibilities and clinically established outcomes. Implantable BCIs remain an active area of research and development. Clinical studies continue to evaluate safety, functionality, signal stability, and long-term outcomes. A 2025 scientific analysis emphasized that Neuralink and related BCI technologies have considerable potential for addressing severe disabilities while also raising unresolved questions concerning implant safety, signal quality, device longevity, and long-term user experience. From a scientific perspective, the success of neural technology should therefore not be measured solely by short-term demonstrations. It must ultimately be evaluated according to safety, reliability, stability, reproducibility, and long-term clinical benefit. Technical Challenges Neuralink and other implantable BCIs face several major scientific and engineering challenges. Long-Term Stability Maintaining reliable neural recordings over extended periods is difficult because biological and mechanical factors may influence the interface between neural tissue and implanted electrodes. Biocompatibility Any device implanted into the brain must be compatible with a highly sensitive biological environment. Biocompatibility is therefore a fundamental requirement for long-term neural interfaces. Neural Decoding Neural activity is dynamic and highly complex. Developing algorithms that can accurately decode intentions despite noise and changes in neural patterns remains a central research challenge. Cybersecurity When the brain becomes connected to a digital system, cybersecurity acquires an entirely new dimension. Protecting data is no longer limited to securing files and conventional personal information. It may also involve protecting neural data itself. Neuroprivacy: A New Dimension of Privacy Privacy may become one of the most sensitive issues associated with BCIs. Neural data differs from conventional digital data because it may contain information related to motor, cognitive, or neurological activity. This has contributed to the emergence of the concept of neuroprivacy, emphasizing the need to protect information derived from the brain through strong technical, legal, and ethical safeguards. Recent research has highlighted cybersecurity and privacy concerns associated with implantable BCIs, including unauthorized access to neural data and the possibility of manipulation of connected systems. The future of neural technology must therefore be accompanied by comprehensive frameworks for neural-data governance. Ethics: Where Does Technology End and Humanity Begin? As technology moves closer to the human brain, ethical questions become increasingly important. If a neural interface can translate brain activity into external action, who should be responsible if the system malfunctions? If the system is hacked, should this be regarded merely as a cyberattack against a digital device, or as a fundamentally different type of violation involving the individual? These technologies raise important questions concerning autonomy, informed consent, identity, responsibility, equality, and access to advanced technology. A multidisciplinary 2025 analysis published in Frontiers in Human Dynamics examined Neuralink from medical, ethical, legal, and philosophical perspectives and emphasized the need for responsible oversight alongside technological development. Neuralink and Human Enhancement A distinction should be made between therapy and human enhancement. Therapeutic applications aim to restore or compensate for functions lost through injury or disease. Human enhancement, by contrast, refers to using technology to extend human capabilities beyond typical biological performance. This raises an important scientific and philosophical question: Should the objective of neurotechnology be to restore human capabilities, or eventually to transcend them? There is no simple answer. Nevertheless, this question will likely become increasingly important as artificial intelligence and neural interfaces continue to evolve. Neuralink and the Future of Human–Machine Interaction One of the most significant implications of Neuralink may be its potential to redefine how humans interact with technology. Today, interaction occurs primarily through keyboards, screens, voice commands, gestures, and other conventional interfaces. Brain–computer interfaces propose a different model: Human → Neural intention → Artificial intelligence → Digital system → Action If these technologies achieve sufficient accuracy, reliability, and safety, neural activity may eventually become another important interface for interacting with digital systems. However, this future must be built on rigorous science and ethical responsibility rather than technological optimism alone. From Artificial Intelligence to Hybrid Intelligence Perhaps one of the most intriguing concepts emerging from this field is hybrid intelligence—the collaboration between human cognitive abilities and machine computational capabilities. Humans possess creativity, contextual understanding, experience, and complex judgment, while computers offer extraordinary computational power, data-processing capabilities, and machine learning. Brain–computer interfaces may eventually provide new mechanisms for bringing these capabilities closer together. Yet the most meaningful objective should not be to replace human beings with machines, but rather to empower human beings through technology. This is where academic institutions and artificial intelligence centers have an important role: guiding innovation toward applications that improve human life while preserving dignity, autonomy, and privacy. A Future Perspective Neuralink should not be regarded as the final destination of neurotechnology, but as one stage in a much broader scientific journey toward understanding the relationship between the human brain and technology. The coming years will determine whether researchers can overcome challenges related to implant safety, neural-signal stability, algorithmic accuracy, cybersecurity, cost, equitable access, and ethical governance. The field may gradually expand from controlling digital cursors and computers toward communication systems, rehabilitation technologies, prosthetic devices, and assistive robotics. The true measure of success, however, should not be how closely technology can approach the brain, but how effectively it can address human needs without compromising human rights. Conclusion Neuralink represents one of the clearest examples of the transition from conventional artificial intelligence toward technologies increasingly integrated with human biology. Brain–computer interfaces offer promising scientific and medical opportunities, particularly for individuals who have lost the ability to move or communicate. At the same time, they introduce unprecedented responsibilities involving neuroprivacy, cybersecurity, autonomy, safety, equality, and ethical governance. The future of Neuralink and similar neural technologies should therefore not be viewed merely as a technological race. It should be understood as a shared scientific and human responsibility. The closer technology moves toward the brain, the more important it becomes for researchers, institutions, and society to understand its limits, responsibilities, and consequences. The true future of artificial intelligence will not be defined solely by the ability of machines to imitate human intelligence. It will also be defined by their ability to empower human beings, enhance their independence, protect their dignity, and create new opportunities for human potential. In this context, artificial intelligence centers and academic institutions have a crucial role in leading informed scientific discussions about these transformations and contributing to a future in which technology serves human development and well-being rather than technological progress alone. References Lavazza, A., Balconi, M., Ienca, M., Minerva, F., Pizzetti, F. G., Reichlin, M., Samorè, F., Sironi, V. A., Sosa Navarro, M., & Songhorian, S. (2025). Neuralink’s brain-computer interfaces: medical innovations and ethical challenges. Frontiers in Human Dynamics, 7, 1553905. https://doi.org/10.3389/fhumd.2025.1553905. Wilkins, R. B., Coffin, T., Pham, M., Klein, E., & Marathe, M. (2025). Mind the gap: bridging ethical considerations and regulatory oversight in implantable BCI human subjects research. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2025.1633627. Neuralink. Clinical Trials and Brain–Computer Interface Research. Neuralink official information. ClinicalTrials.gov. PRIME Study: A First-in-Human Early Feasibility Study of the N1 Implant and R1 Robot. Clinical trial registry.