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To ensure food safety and uphold high standards, the food business must overcome significant obstacles. In recent years, promising answers to these issues have emerged in the form of artificial intelligence (AI) and machine learning (ML). This thorough review paper analyses the various uses of AI and ML in food quality management and safety evaluation, offering insightful information for academics, business people and legislators. The evaluation highlights the value of food quality assessment and control in consideration of growing consumer demand and regulatory scrutiny. The powerful capabilities of AI and ML are touted as having the potential to revolutionize these procedures. This study illustrates the numerous uses of AI and ML in food quality management through an in-depth exploration of these technologies. Defect detection and consistency evaluation are made possible using computer vision techniques, and intelligent data analysis and real-time monitoring are made possible by natural language processing. Deep learning techniques also provide reliable approaches for pattern recognition and anomaly detection, thus maintaining consistency in quality across manufacturing batches. This review emphasizes the efficiency of AI and ML in detecting dangerous microorganisms, allergies and chemical pollutants with regard to food safety evaluation. Consumer health risks are reduced because of the rapid identification of safety issues made possible by integrating data from diverse sources, including sensors and IoT devices. The assessment discusses issues and restrictions related to the application of AI and ML in the food business while appreciating the impressive progress that has been made. Continuous efforts are being made to improve model interpretability and reduce biases, which calls for careful evaluation of data quality, quantity and privacy issues. To assure compliance with food safety norms and regulations, the article also covers regulatory approval and validation of AI-generated outcomes. The revolutionary potential of AI and ML in raising food industry standards and preserving public health is highlighted on future perspectives that concentrate on new trends and potential innovations. This comprehensive review reveals that the integration of AI and ML technologies in food quality control and safety not only enhances efficiency, minimizes risks and ensures regulatory compliance but also heralds a new era of personalized nutrition, autonomous monitoring and global collaboration, signifying a transformative paradigm in the food industry.

This perspective paper, which is the result of a collaborative effort between toxicologists and scholars in innovation and transition studies, presents a heuristic framework based on innovation system literature for understanding and appraising mission achievement to animal-free chemical safety assessment using New Approach Methodologies (NAMs). While scientific and technical challenges in this area are relatively well known, the recent establishment of missions and roadmaps to accelerate the acceptance and effective use of NAMs for chemical safety assessment raises new questions about how we can grasp the systemic nature of all changes needed in this transition. This includes recognising broader societal, institutional, and regulatory shifts necessary for NAM acceptance and uptake. Our paper discusses how the innovation system approach offers insights into key processes and associated activities that include as well as transcend the technical and scientific realm, and can help to accelerate acceptance and uptake of NAMs. Based on these insights, we present a comprehensive framework that, next to scientific and technological developments, recognises the need for coordinated efforts in areas like education, training, funding, policy-making, and public engagement to promote the acceptance and uptake of NAMs. Our framework can be used to perform structural and functional analyses of the innovation system of NAMs and animal-free safety assessment and as such provides handholds to track progress and organise collective efforts of actors to make sure we are moving in the right direction.

The Clinical Immunization Safety Assessment (CISA) Project is a network of vaccine safety experts from the Centers for Disease Control and Prevention (CDC) Immunization Safety Office (ISO) and seven medical research centers. CISA responds to inquiries from U.S. healthcare providers (HCPs) and conducts vaccine safety research. This report summarizes the CISA approach to addressing provider vaccine safety inquiries during the COVID-19 pandemic and describes these consultations. During the COVID-19 pandemic, CDC established a 24/7 on-call CISA consultation service. Inquiries were reviewed, and some of the most clinically complex were selected for comprehensive, structured CISA clinical case consultations. After confidential consultations, provider satisfaction surveys were sent, and providers were queried about patient outcomes and whether patients tolerated subsequent COVID-19 vaccinations. From December 14, 2020, through December 31, 2022, CDC staff and CISA investigators conducted 79 comprehensive clinical case consultations (73 regarding adverse events following immunization (AEFI) after COVID-19 vaccine, and 6 pre-vaccination questions). Of the 73 AEFI consultations, 31 (42 %) included neurologic and 14 (19 %) allergic or hypersensitivity symptoms. Cardiology and hematology inquiries comprised most of the remainder. Twenty-four (30 %) provider satisfaction surveys were returned; all respondents found the service helpful. Of 38 (79 total; 48 %) returned patient follow-up surveys, 14 (37 %) reported that subsequent COVID-19 vaccines were administered and were well-tolerated in case patients. Despite low participation rate in satisfaction and patient follow-up surveys, the CISA consultation service provided timely COVID-19 vaccine safety evaluations for HCPs through comprehensive clinical case consultations and vaccine safety guidance, a unique role in the pandemic response. •A consultation service to evaluate adverse events following immunization was launched after COVID-19 vaccines were authorized.•Physicians experienced in vaccine safety evaluation and subspecialty care addressed specific types of AEFI consultations.•Confidential AEFI consultations were based on medical record review, literature search, and surveillance data reviews.•Follow-up patient outcomes, including administration of subsequent COVID-19 vaccines, were documented.

The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been increasingly used for safety surveillance in the construction industry and civil engineering fields. This paper presents an aerial image-based approach using UAVs to inspect cracks and deformations in buildings. A state-of-the-art safety evaluation method termed SMART SKY EYE (Smart building safety assessment system using UAV) is introduced; this system utilizes an unmanned airplane equipped with a thermal camera and programmed with various surveying efficiency improvement methods, such as thermography, machine-learning algorithms, and 3D point cloud modeling. Using this method, crack maps, crack depths, and the deformations of structures can be obtained. Error rates are compared between the proposed and conventional methods.

The Colombian manufacturing industry's diversity and capacity for high-quality production are creating opportunities for increased participation in the aeronautical market. To tap into this potential, companies must adapt processes to adhere to global quality and safety standards. This research led to the creation of a specialized software tool capable of managing documents and risks linked to safety analysis during aeronautical product certification and manufacturing. By identifying local industry needs and essential risk-tracking features, the software was tailored to suit Colombian practices. This tool enables data management and risk monitoring throughout the manufacturing process, promoting standardization and centralization of risk-related information while meeting certification requirements and alleviating constraints tied to implementing a new risk monitoring system. Despite possessing the necessary potential, the Colombian manufacturing industry has yet to adopt the essential tools for certifying products under international standards in the aeronautical sector.