American Journal of Artificial Intelligence and Computing
The American Journal of Artificial Intelligence and Computing (AJAC) is a peer-reviewed, open-access journal dedicated to publishing high-quality research and advancements in artificial intelligence (AI) and computing published by Webscare inc. The journal serves as a platform for researchers, academicians, and industry professionals to share innovative ideas, groundbreaking technologies, and emerging trends in AI-driven applications. Scope and Topics: AJAC welcomes original research articles, review papers, and technical reports in areas including, but not limited to: Artificial Intelligence: Machine learning, deep learning, natural language processing, computer vision, expert systems, and reinforcement learning. Computing Technologies: Cloud computing, quantum computing, edge computing, and big data analytics. Robotics and Automation: AI-powered robotics, autonomous systems, and industrial automation. Cybersecurity and AI Ethics: AI for security, bias detection, responsible AI, and policy frameworks. Human-AI Interaction: Explainable AI, AI-driven user interfaces, and cognitive computing. Applications: AI in healthcare, finance, smart cities, education, and business intelligence
Machine Learning for Personalized Medicine: Transforming Patient Care with Artificial Intelligence
Machine learning revolutionizes personalized medicine by facilitating personalized healthcare based on data. This review goes through its contribution to the shift from conventional therapies to precision medicine, by incorporating genomic, clinical, imaging and wearable data. Supervised learning, Unsupervised learning, deep learning - all of these machine learning approaches are applicable to some of the medical uses of machine learning such as disease prediction, treatment optimization, drug discovery, and clinical decision support. While there has been considerable progress, there are still issues to be addressed, including data privacy, bias, interpretability, and regulatory compliance. Ethical and legal issues also are important to implementation. The future of health care delivery is anticipated to become even more accurate and individualized, with digital twins, federated learning, and real-time AI systems all on the horizon.
Copyright (c) 2026 American Journal of Artificial Intelligence and Computing
Blockchain-Enabled Artificial Intelligence: A Systematic Review of Applications in Healthcare, Electrical Grids, Data Science, and Computer Vision
In this review, we examine how Artificial Intelligence (AI) and Blockchain are applied in various domains, such as Healthcare, Electrical Systems, Data Science, and Computer Vision. AI provides intelligent data analysis, prediction, and automation, while Blockchain ensures security, transparency, and data integrity. Together, they allow for sophisticated services like secure health records, smart energy networks, privacy-enhancing data analytics, and visual data that cannot be tampered with. This study systematically covers methodologies, applications, challenges, and future directions of AI-Blockchain systems. Although it has several challenges such as scalability and computational complexity, it has great potential for creating secure, efficient, and intelligent decentralized systems in various real-world domains.
Artificial Intelligence Techniques and Block chain Integration in Healthcare, Electrical Systems, Data Science, and Computer Vision
The convergence of Artificial Intelligence (AI) and block chain is becoming a game-changer in various fields, such as healthcare, electrical systems, data science, and computer vision. AI allows for intelligent data analysis, prediction and automation while block chain provides decentralized, secure and tamper-proof data management. The two converge to make the systems more transparent, trustworthy, and efficient and overcome the challenges like data privacy, scalability, security threats and more. It is used in healthcare to enhance the diagnosis and management of records, in electrical systems for the optimal distribution of energy, in data science to process it securely and in computer vision for the proper processing of visual data. Although there are difficulties, there are great opportunities for integration of future intelligent systems.
Comprehensive Review of Artificial Intelligence Techniques in Healthcare, Electrical Systems, Data Science, Computer Vision, and Block chain Integration
Artificial Intelligence (AI) is a transformative technology, broadly utilized in the healthcare, electrical systems, data science, computer vision and block chain integration. This review examines several crucial AI technologies such as machine learning, deep learning, and reinforcement learning, and outlines their applications in enhancing diagnostics, energy management, predictive analytics, visual recognition, and secure data systems. AI contributes to efficiency, accuracy, and automation in a wide range of areas, which allows for intelligent decision-making and real-time processing. While it has some benefits, there are also several drawbacks like data privacy, bias, interpretability, and computational complexity. AI is poised to become a major force in global technological progress, with further advancements expected in the future, such as the development of explainable AI, edge computing, and quantum machine learning.
Advanced Data Analytics in Healthcare: Integrating Machine Learning, Security, and Ethical AI
Healthcare Advanced data analytics is an integration of machine learning, big data, security, and ethical AI to enhance the diagnosis, treatment, and decision-making processes. The transformation of conventional records to electronic systems such as EHRs, wearable technology, and genomic data has made predictive and personal medicine possible. Clinical accuracy is improved by machine learning, and patient data is safeguarded by security measures, which prevent the loss of sensitive information to cyber criminals. The ethical issues like the presence of bias, the lack of transparency, and the absence of patient consent can guarantee the responsible use of AI. The emerging technologies, including federated learning and block chain, provide future improvements, which will drive a secure, efficient, and patient-centered health system.
Next-Generation Electrical Systems: A Review of AI-Enabled Technologies
This review explores the evolution of conventional electrical systems to next-generation smart networks powered by Artificial Intelligence (AI). It outlines the shift from conventional to smart, distributed systems with integration of renewable energy, Internet of Things (IoT) and communication technologies. AI technologies like machine learning and data analytics improve grid performance through predictive maintenance, monitoring, fault detection, and energy efficiency. This work also explores the use of AI in smart grids, electrical machines, and energy management, as well as issues encountered such as cybersecurity, data integrity, and interoperability. Finally, it provides an overview of future directions of sustainable energy, decentralized systems, explainable AI, and emerging technologies in modern electrical systems around the globe.
Artificial Intelligence in Computer Vision: Methods and Applications Review
Artificial intelligence-based computer vision is an emerging technology that enables computers to see. This paper reviews the methods of artificial intelligence in computer vision including machine learning, deep learning, convolutional neural networks and transfer learning, and applications of such technology in object detection, facial recognition, medical image analysis, autonomous vehicles and security. It also explains the performance measures, issues such as bias, complexity and ethical concerns and future technologies such as vision transformers and multimodal learning. The study ends with the future of more efficient, explainable and transferable intelligent vision.
Mechanical Design and Manufacturing Processes in Medical Device Development
Medical device development heavily relies on the manufacturing techniques and mechanical design of medical devices since it ensures safety, functionality and reliability. The whole process of development is shown in this work, including the fundamentals of design, the selection of materials, method of production, prototype, quality control, and regulation. It highlights the importance of CAD modeling, biocompatible materials and particular manufacturing techniques such as machining, injection molding and additive manufacturing. Additional concerns that the work tackles include sterilization, packaging and significant concerns like cost and regulatory complexity. It explores future trends like smart devices, use of AI, and new materials and continuous innovation to improve the ambitions of health and personalized medicine.
Exploring AI Techniques in Data Analytics: A Critical Review
Artificial Intelligence (AI) has transformed the field of data analytics, which provides an efficient processing, analysis, and interpretation of large and complex data. The review discusses the most popular AI methods including machine learning, deep learning, natural language processing, and reinforcement learning and their implementation in the contemporary analytics systems. It explores its applications in the healthcare sector, finance, marketing, and smart cities, with a focus on enhanced accuracy, scalability, and decision-making. The paper also addresses issues such as data quality, privacy, bias and interpretability. An overview of AI approaches is comparatively analyzed and future trends such as explainable AI and edge computing are discussed. In general, AI is a considerable aid to data-driven decision-making and analytical skills.
Artificial Intelligence in Healthcare: Leveraging Big Data Analytics and Machine Learning
Artificial Intelligence (AI) in healthcare represents a combination of machine learning and big data analytics that enhance the experience of diagnosing, treating, and taking care of a patient. It facilitates early disease diagnosis, personalized medicine, medical imaging and analysis, drug discovery and remote monitoring. Electronic health records, wearables, and medical systems provide big data to support predictive analytics and decision-making, which are driven by AI. This incorporation improves accuracy, efficiency and cost-effectiveness in healthcare provision. Nonetheless, data privacy, bias, lack of transparency and regulatory issues are some of the challenges that limit its use. Nevertheless, regardless of these constraints, AI continues to change the healthcare systems by providing innovative, patient-focused, and data-driven solutions to achieve better health outcomes in the world and further advancements in the medical sphere.
Artificial Intelligence in Data Science: A Comprehensive Review
Artificial Intelligence (AI) has become an integral part of modern-day data science which has changed the way under which data are collected, analyzed, and interpreted. This review delves into the development of AI, the amalgamation of AI with Machine learning, Deep learning, & big data analytics, and its role in automating data workflow. Real-world applications found wherever hospitals, finance, retail stores, manufacturing, etc. AI has showcased the landscape of its ability to provide actionable insights. Despite the many advances, there are still challenges associated with data quality, model interpretability, algorithmic bias, privacy and ethics. The article also focuses on emerging trends such as explainable AI, AutoML and technological convergence that describe the future directions of AI-driven data science.
AI, ML, and Data Analytics at the Core of Predictive Healthcare and Smart Medical Supply Chains: Systematic Perspectives
The fast development of the Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics is changing the sphere of healthcare with the predictive models of healthcare and intelligent medical supply chains. The predictive healthcare uses evidence-based information to understand the risk factors of patients, allow early identification of diseases, and allow tailored treatment plans, whereas smart supply chains can improve access to the required medical materials and enhance their distribution. Combining these domains into one active, flexible ecosystem makes clinical forecasting and logistics planning to be in harmony, which leads to improved patient outcomes and reduced operational costs. Resilience and transparency of the systems are also improved by emerging technologies such as digital twins, IoT, automation, and explainable AI. Although data quality, interoperability, ethical concerns, and regulatory standards are some of the challenges of the convergence of AI, ML and data analytics, the potential impact of the convergence of AI, ML and data analytics on healthcare delivery is immense. This review reviews the existing trends, applications, constraints, and directions, and how computational intelligence can streamline both treatments and management of medical resources to provide a sustainable, efficient and patient-centred healthcare ecosystem.
Copyright (c) 2025 American Journal of Artificial Intelligence and Computing
Predictive Healthcare and Smart Medical Supply Chains: A Systematic Review of Artificial Intelligence and Machine Learning Techniques
Healthcare informatics has been changed drastically with the rapid development of healthcare data and digital technologies. The data analytics, Artificial Intelligence (AI), and Machine Learning (ML) are relevant to enhancing healthcare decision-making, predicting disease, and operational efficiency. This review examines the use of AI-based data analytics in predictive health care and management of the healthcare supply chain. It notes the adoption of big data, cloud computing, and edge computing technologies and the Internet of Medical Things (IoMT) to assist with intelligent healthcare services. Machine learning models are useful in early disease detection, assessment of the risk in the patient, and planning the treatment. Regardless of such obstacles as data privacy and interoperability, AI has a great potential to improve healthcare delivery and patient outcomes.
Data Analytics in Healthcare Informatics: A Review of Artificial Intelligence and Machine Learning for Predictive Healthcare and Supply Chain Management
This review examines the disruptive nature of data analytics, Artificial Intelligence (AI) and machine learning (ML) in healthcare informatics, namely predictive healthcare and the supply chain. It emphasizes that AI and ML allow preempting diseases, risk classification in the patient, individual therapy, and operating efficiently by implementing predictive analytics. In real-world practices, there is an increase in the accuracy of the diagnosis, allocation resources, and optimization of inventory. Other issues discussed in the paper include data privacy, algorithmic bias, interoperability and ethical issues. The future of healthcare systems has new trends such as IoT integration, precision medicine, smart healthcare ecosystems, and explainable AI, which provide a chance to develop proactive, patient-centered, and efficient healthcare systems.
Securing Modern Healthcare Systems: A Review of Artificial Intelligence, Machine Learning, Predictive Analytics, and Data Analytics in Healthcare Informatics and Supply Chains
Modernization of healthcare systems is becoming more and more dependent on the application of Artificial Intelligence (AI), Machine Learning (ML) as well as predictive analytics and data analytics that can be used to provide better care to patients as well as operational efficiency and systems security. This review examines how these technologies can be used in healthcare informatics and supply chain management and mentions their implementation in the fields of diagnostics, personalized treatment, predictive modeling, and optimization of resources. It also covers the issues of data privacy, cybersecurity, algorithmic bias, interoperability and ethical considerations. Precision medicine, real-time monitoring using IoT devices, and interoperable and resilient healthcare infrastructures are the destinations of the future. Altogether, smart data-driven systems are an innovative direction of safe, effective, and patient-centered care.
IoT Generated Big Data Analytics: Trends and Scalability Issues
The high rate at which Internet of Things (IoT) devices are being created has resulted in the creation of large, heterogeneous, and fast datasets, which form the IoT-generated Big Data. The effective processing of such data is essential to allow making intelligent decisions, making predictions, and automation in various areas of application including smart cities, healthcare, industrial solutions, and transportation. The current trends, architectures and analytics methods used in IoT Big Data systems are discussed in this review with a specific emphasis on the scalability issue, security considerations and strategies of managing data. Also, the latest technologies like edge computing, AI-based analytics, digital twins and blockchain are addressed. The paper outlines the open research issues and prospects, providing an overall insight to the creation of scalable, secure, and intelligent IoT analytics systems.
Data Driven Organic Chemistry: Role of Artificial Intelligence in Molecular Discovery
Organic chemistry has been transformed by artificial intelligence (AI) that has facilitated the use of data in discovering molecules, predicting reactions, and optimizing syntheses. Conventional methods which depend on human intuition and trial and error tests are usually time consuming and narrow-minded. The use of AI with large chemical datasets to predict properties of molecules, create new compounds, optimize reactions, and plan synthetic pathways through machine learning, deep learning, and generative models is efficient. It is used in drug discovery, catalysis, materials science, and sustainable chemistry, which lessens the amount of work required in experiments, increases precision and efficiency. Although AI has some challenges including the quality of data, interpretation, and generalization, it supplements human knowledge, providing a breakthrough method to rapid, rational, and sustainable chemical innovation.
Predictive Analytics for Efficient and Smart Supply Chain Optimization
Predictive analytics is changing the LSCM in that it allows making decisions based on data, enhances efficiency, and minimizes operational expenses. The models of machine learning and data analysis on historical data help organizations predict demand, optimize inventory, and automate procurement and logistics. Predictive tools are effective in managing risks because they detect the possibility of disruption and allow proactive plans, and assist in making real-time operational changes. The present review examines the use of predictive analytics in the optimization of the supply chain and the role it plays in enhancing resilience, agility, and customer satisfaction. The results imply that the implementation of predictive analytics in supply chains is crucial to making the supply chain more intelligent, efficient, and competitive.
Predictive Analytics, Supply Chain Optimization, Demand Forecasting, Risk Management, Logistics, Machine Learning, Efficiency.
Artificial Intelligence in Organic Chemistry: Transforming Reaction Prediction, Design, and Drug Discovery
Artificial Intelligence (AI) is changing the field of organic chemistry by improving prediction, optimization, and automation in different processes. Machine learning-based models are speeding up the processes of molecular design, optimization of chemical reactions, or forecasting the properties and toxicity of the compound faster and more accurately than ever before. The AI in drug discovery supports virtual screening, lead optimization, and predictions of ADMET, saves on time and cost of novel therapeutic development. Also, AI is automating the synthesis processes, making it possible to optimize closed-loop and autonomous labs. With the further development of AI technologies, it is guaranteed that they will transform the way chemist’s research and work, making them faster to innovate, more efficient, and allowing them to find new molecules and materials.
AI, organic chemistry, molecular design, drug discovery, reaction prediction, machine learning, computational chemistry.
Artificial Intelligence and Its Expanding Role in Computer Science
Artificial Intelligence (AI) is now a revolutionary phenomenon in Computer Science (CS) that affects its principles, uses, and prospective development. This review examines the changing association of AI with CS, pointing to the ways AI positively influences conventional fields, including software engineering, data science, networking, cybersecurity, and human-computer interaction. It also reviews the new uses of natural language processing, computer vision, cloud computing, and robotics as well as the issues such as ethics, bias, privacy, and computational constraints. Lastly, the review highlights the future opportunities of explainable AI, sustainable computing, quantum integration and human-AI collaboration. AI and CS used together can make the technology relevant once again in innovation and development of the society.
Artificial Intelligence and the Future of Healthcare: A Comprehensive Review
Artificial Intelligence (AI) is quickly changing the landscape of healthcare, opening up new potential in diagnostics, patient care, operations, and preventive medicine. This review provides an overview of the benefits of applying AI for increased diagnostic accuracy, for more effective personalized treatment, and for better patient engagement via virtual assistants, wearables, and telehealth. It also notes the role that automation can play in simplifying hospital workflows and minimizing administrative burdens. We also critically explore data privacy, bias, and accountability concerns, as well as challenges related to cost, integration, and equitable access. In the context of predictive and preventive medicine, however, the review sounds a positive note about the potential of AI to reshape an entire industry towards a proactive paradigm from a reactive one. Ultimately, Artificial Intelligence (AI) is not the replacement for the clinician but the highly skilled partner that compliments human expertise and contributes value to a more patient-centered healthcare system.
AI and ChatGPT Innovations in Healthcare Cybersecurity, Computer Science, Poultry Science, and Food Production
Artificial Intelligence (AI) and more-sophisticated language models such as ChatGPT are transforming a wide variety of fields, including health care cybersecurity, computer science, poultry science, and food production. This article examines their transformational uses, with an emphasis on predictions, automation, and decision making using AI. In healthcare, AI can improve data security, increase threat detection and operational efficiency, and ChatGPT can provide knowledge synthesis and intelligent support. In computer science, AI automates the software development and solving of problems. Artificial intelligence-fuelled surveillance, targeted management, and supply chains drive poultry science and food production to higher levels of productivity and sustainability. Some of the challenges that are faced by this technology include data quality, ethical, and environmental effects. Trans-domain tic integration is expected to be innovative, efficient and resilient.
Artificial Intelligence and Project Management: An Integrative Review of Current Approaches and Future Directions
Artificial intelligence (AI) is already changing the face of project management due to its ability to boost processes involved in project planning, project execution, project monitoring, and project decision-making. The paper will discuss the basis of the use of AI in project management, and will consider the ways in which it is used today in project management in the form of scheduling, resource optimization, risk prediction, communication, performance tracking. Some of the advantages connected with AI incorporation include a higher level of accuracy, efficiency, cost-saving, and more substantial stakeholder cooperation. Issues like data quality, ethical issues, high expenses, and reluctance to use present a challenge to the realization of widespread implementation. Two possible future directions are emerging technologies such as generative AI, digital twins and autonomous agents, all seemingly well-suited to complement IoT, block chain, and cloud platforms. The review demonstrates unaddressed research gaps in some series of empirical validation, sector-specific applications, ethics and skill development, and the need to create a balanced human-AI working relationship to meet goals of sustainable projects.
Artificial Intelligence at the Crossroads: Transformative Applications in Healthcare, Poultry Science, Computer Science, ChatGPT, Cybersecurity, and Food Processing
Artificial Intelligence (AI) is transforming every walk of life such as healthcare, poultry science, computer science, conversational AI, cybersecurity, and food processing. This assessment explains how AI can be used in improving efficiencies, correctness, and decisions in industries. In healthcare AI is useful in diagnostics, personalized medicine and patient management, whereas in poultry science precision farming and disease monitoring are possible. The foundation of AI is through the developments of computer sciences and ChatGPT is an example of conversational innovations. Artificial intelligence helps in detecting threats to cybersecurity but threats will always be there. AI is used in food processing to check the quality, streamline the supply chain, and reduce wastage. Though it has the transformative power, it still has challenges of data privacy, bias, transparency and ethical regulation. The responsible integration foreseen in the article allows achieving sustainable, fair, and trustworthy AI adoption.
Advancing Healthcare and Food Security through AI and Computer Science: Cybersecurity Challenges and ChatGPT Applications in the Poultry Industry
Artificial Intelligence (AI) is transforming the healthcare, food and poultry business by facilitating decision-making based on data, predictive analyses and optimization of operations. When speaking of healthcare, AI is used to improve the diagnostics, personalized therapeutic treatment, and clinical decision-making, and the tool such as ChatGPT is used to engage with patients and streamline administrative work. AI can support precision farming, quality control and healthy food production methods within the food and poultry industry, which enhances productivity and food safety. The implementation of AI also comes with emerging issues and concerns like the risk of cybersecurity, data privacy, and ethical concerns like the risk of algorithmic bias and algorithmic transparency. These challenges can be addressed by implementing AI, which is integrated with strong computer science foundations, secure systems, and ethical governance. This review provides an overview of how AI is being utilized, what its challenges and opportunities are, and presents the groundbreaking possibilities of ChatGPT and AI-driven technology facilitating healthcare in improving its outcomes and food security.
Artificial Intelligence in Healthcare: Technological Progress and Ethical Frontiers
Artificial intelligence (AI) is revolutionizing the healthcare industry by optimizing diagnostics, personalizing care, automating the administrative process and turning patients into healthier individuals. This review reflects upon the history of the development of AI, with references to the early forms of expert systems and more advanced models of deep learning and explains the key technologies like machine learning, natural language processing, and computer vision. The common applications are clinical diagnosis, predictive analytics, workflow streamlining, and off-site patient tracking. The paper presents the most important advantages, viz, better accuracy, efficiency, and access to care, and the problems, namely the quality of data, interoperability, bias of algorithms, and a lack of explain ability. The issue of privacy and informed consent is considered along with other ethical and legal regulations on the topic, such as accountability and fairness. The ways to go are characterized by transparency, mitigation of bias and regulatory adaptation. A more predictive, personalized, and equitable healthcare system is the promise of responsible integration of AI.
Artificial Intelligence in Healthcare, Cybersecurity, Machine Learning, and Food Processing: A Cross-Industry Review
Artificial intelligence (AI) is changing some of the major industries such as healthcare, cybersecurity, machine learning and food processing at a very high pace. In healthcare, AI promotes diagnostics, treatment planning and P4, making it even more accurate and efficient. Within the field of cybersecurity, artificial intelligence allows dynamic defense systems and predications in detecting and preventing threats, but with a possibility to be misused. Machine learning is what gives AI the power of learning, inculcating changes and making predictions across fields. AI also helps in the food industry because there is enhanced quality control, optimization of the supply chains, and safety management. Even though these areas have different use cases, they have common issues: dependence on high-quality data, ethical issues, explain ability, and employee adaptation. AI convergence and development place a strong emphasis on the need to work across disciplines, ethical development and governance. The future of AI is a quest to integrate it, not only between different technologies, industries, but also with the role of human beings, which revolutionizes the way society innovates, protects itself, and develops. This paper discusses this overlapping of forces and how we all have a duty and responsibility in guiding this potential of AI.
Evolution of Computer Science: A Historical and Technological Overview
This comprehensive review traces the historical evolution and multifaceted growth of computer science, highlighting its foundational theories, technological advancements, societal impact, and future directions. Beginning with early computational concepts and the invention of programming languages, the article outlines how the development of operating systems and software engineering fueled the rise of personal computing. It then examines the transformative role of the internet in enabling global connectivity, followed by the emergence of artificial intelligence (AI) and machine learning (ML), which have revolutionized automation, data analysis, and decision-making processes. The review explores contemporary applications across sectors including healthcare, finance, education, entertainment, and cybersecurity, emphasizing how deeply computer science is embedded in modern life. It also addresses ethical concerns such as privacy, algorithmic bias, job displacement, and environmental impact. It highlights emerging trends like quantum computing, edge computing, and sustainable technologies. The article concludes by recognizing computer science as a dynamic, interdisciplinary field that continuously evolves to meet the needs and challenges of an increasingly digital world.
Revolutionizing Healthcare with AI and ChatGPT: The Future of Intelligent Automation
AI, ML, and tools to work with natural language processing, such as ChatGPT, are gaining a lot of popularity in the healthcare field, changing it in unexpected ways. These technologies are improving diagnostics, individualizing treatment, streamlining operations and transforming medical education. Predictive analytics and operational tools based on AI are enhancing resources distribution, patient flow, and administration efficiency, whereas precision medicine field is using the capacity of AI to personalize the treatment respectively on the patient genetic and lifestyle data. ChatGPT will improve the patient-provider communication and provide patients and medical trainees with real-time access to educational assistance. The concept of interdisciplinary uses, like application of the aerodynamics-based models to cancer studies, is evidence of a wide range that AI has to offer. Nevertheless, the innovations also bring up key ethical questions about the privacy and data security, bias of their algorithms, and responsibility. Increasingly incorporating AI into medical services, it is also necessary to make its implementation responsible by adhering to transparent practices and ethics. Finally, AI and ChatGPT will not replace human knowledge, but will supplement it and create a more personalized, efficient, and everyone in the field of medicine.
AI in Healthcare and Food Systems: Exploring Machine Learning, ChatGPT, and Emerging Applications
The scope of the review is discussing the potential impacts of artificial intelligence (AI) in terms of transforming the healthcare and food systems industries touching upon the machine learning systems and natural language processing technologies. The reality that AI is reshaping new domains in healthcare may rewrite the process of diagnosis, personalized medicine, and revolutionize the sector of patient care to make the process of diagnosis more correct and treatment of patients more efficient. The machine learning algorithms are also enhancing drug discovery and predictive analytics. With AI, the food system is being simplified, precision farming, better supply chain, and less wastage. It is also involved in the stimulation of sustainable food production and customized nutrition. Nonetheless, the development that contains the AI is connected with some ethical issues, among which we could notice the data privacy, bias of algorithms, and unequitable access, to be more specific. This is another impact of the AI on the health system and food systems that cannot be measured as long as this technology keeps advancing and it must be done with caution, ethics and fairness to reap the best out of it to the better benefit of mankind.