Emerging Role of Artificial Intelligence (AI) in Aviation

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

The last years have seen an acceleration in the development and uptake of artificial intelligence (AI) systems by "early adopter" hospitals, caught between the pressures to "perform" and "transform" in a struggling health care system. This transformation has raised concerns among health care providers as their voices and location-specific workflows have often been overlooked, resulting in technologies that fail to integrate meaningfully into routine care and worsen rather than improve care processes. How can positive AI implementation be carried out in health care, aligned with European values? Based on a perspective that spans all stakeholders, we have created EURAID (European Responsible AI Development), a practical, human-centric framework for AI development and implementation based on agreed goals and values. We illustrate this approach through the co-development of a narrow-purpose "in-house" AI system, designed to help bridge the AI implementation gap in real-world clinical settings. This example is then expanded to address the broader challenges associated with complex, multiagent AI systems. By portraying all key stakeholders across the AI development life cycle and highlighting their roles and contributions within the process, real use cases, and methods for achieving iterative consensus, we offer a unique practical approach for safe and fast progress in hospital digital transformation in the AI age.

Readiness for Artificial Intelligence in Biobanking

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Artificial intelligence to complement rather than replace radiologists in breast screening

Study question What are the key considerations, validation frameworks, and safety guidelines required for the responsible implementation of Artificial Intelligence (AI) systems in MAR clinics? Summary answer The Croatia Consensus establishes internationally agreed-upon best practices for AI validation in MAR, ensuring patient safety, clinical excellence, regulatory compliance, and ethical implementation. What is known already AI applications are increasingly integrated into ART to optimise embryo selection, standardise clinical decision-making, and reduce variability. However, absence of internationally accepted validation frameworks, regulatory guidelines, and ethical oversight poses risks to patient safety and clinical efficacy. Current AI models often lack transparency, generalisation, and robust external validation. Bias in training datasets can lead to inequitable clinical outcomes. The need for structured AI governance in ART is pressing. The Croatia Consensus, formed by global experts (AI Fertility Society), aims to define best practices for AI validation and deployment in MAR clinics. Study design, size, duration A structured Delphi process involving 148 AI and MAR experts was conducted in 2024 to develop international guidelines for AI validation in ART. The consensus methodology included systematic literature reviews, expert panel discussions, and iterative feedback rounds. Topics covered included AI safety, validation protocols, data standardisation, regulatory compliance, and bias mitigation. The final consensus document was reviewed at the AI Fertility Society Meeting and endorsed by multidisciplinary stakeholders, including clinicians, embryologists, ethicists, and AI developers. Participants/materials, setting, methods Consensus guidelines were developed through contributions from embryologists, reproductive specialists, AI researchers, and regulatory experts. The process included a systematic review of AI applications in MAR, gap analysis of existing validation frameworks, and expert recommendations on AI validation strategies. Key aspects included standardised AI reporting (TRIPOD+AI compliance), real-world clinical validation across multiple centres, ethical risk mitigation, and transparent AI decision-making. AI system performance benchmarks were established using clinical outcome measures and patient safety indicators. Main results and the role of chance The Croatia Consensus establishes a comprehensive framework for AI validation in MAR, ensuring patient safety, regulatory compliance, and clinical efficacy. Key recommendations include multi-centre external validation of AI models to ensure generalisation across diverse patient populations, with the TRIPOD+AI framework recommended for transparent reporting. To mitigate bias, AI systems must undergo demographic audits, particularly in embryo selection, to prevent inequitable outcomes. Regulatory compliance with GDPR (EU), FDA (USA), and MHRA (UK) is required before clinical implementation. Transparency is critical; AI models must provide interpretable decisions, including confidence scores, feature importance, and performance metrics. Continuous post-implementation monitoring is essential to detect model drift and ensure patient safety over time. The consensus highlights that unvalidated AI models currently used in MAR clinics may introduce risks to patient outcomes. Implementing the Croatia Consensus framework will help standardise AI validation, mitigate risks, and ensure AI adoption in MAR is both evidence-based and clinically safe. Limitations, reasons for caution The consensus is based on expert opinions and current scientific literature; further empirical studies are required to validate AI best practices. The framework must evolve as AI capabilities and regulatory landscapes develop. Future research should focus on real-world AI deployment outcomes, patient safety, and long-term MAR success rates. Wider implications of the findings This is the first international AI validation framework in MAR. Standardising AI best practices will improve patient safety, optimise clinical outcomes, and enhance trust in AI-assisted fertility treatments. The framework provides a blueprint for MAR clinics, regulatory bodies, and AI developers, ensuring responsible AI integration into reproductive medicine. Trial registration number No

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

Artificial intelligence (AI) is transforming supply chain planning and decision making, enabling organizations to tackle the complexities of modern supply chains. This article explores the various applications of AI in supply chain management, including demand forecasting, inventory optimization, transportation and logistics optimization, supplier selection and risk management, and predictive maintenance and asset management. AI-powered demand forecasting models analyze historical data and market trends to predict future demand accurately, while AI-driven inventory optimization considers factors such as lead times and demand variability to determine optimal inventory levels. AI can also optimize transportation routes, modes, and schedules, and assist in supplier selection and risk assessment. Predictive maintenance using AI helps reduce equipment downtime and maintenance costs. However, organizations must consider challenges such as data quality, algorithmic bias, interpretability of AI models, and ethical considerations when adopting AI in supply chain management. As AI technologies advance and integrate with other emerging technologies, the future of AI in supply chain management looks promising, offering organizations the potential to achieve greater efficiency, agility, and competitiveness. This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges ofAI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use ofAI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

The implementation of artificial intelligence (AI) in the predictive maintenance of medical equipment is a transformative approach, particularly beneficial for rural clinics where resources and access to specialized technical support are limited. Predictive maintenance leverages AI algorithms to analyze data from medical equipment, predicting potential failures and maintenance needs before they occur, thus enhancing the reliability and availability of critical healthcare technologies. In rural clinics, maintaining the functionality of medical equipment is paramount due to the scarcity of medical devices and the limited access to timely technical support. AI-driven predictive maintenance systems utilize data from sensors embedded in medical devices to monitor their performance continuously. Machine learning algorithms analyze this data to identify patterns and anomalies that precede equipment failures. By predicting when and which parts are likely to fail, these systems enable preemptive maintenance, reducing downtime and preventing equipment malfunctions during critical medical procedures. The benefits of AI in predictive maintenance are manifold. Firstly, it enhances the operational efficiency of rural clinics by minimizing unexpected equipment failures and associated downtime. This ensures that essential diagnostic and therapeutic devices are always available, improving patient care and outcomes. Secondly, predictive maintenance extends the lifespan of medical equipment by preventing extensive damage through timely interventions. This is particularly crucial for rural clinics operating on tight budgets, as it reduces the need for costly replacements and repairs. Moreover, AI-driven predictive maintenance contributes to better resource management in rural healthcare settings. By predicting maintenance needs, clinics can optimize the scheduling of technical support visits, ensuring that equipment maintenance is conducted during non-peak hours, thus minimizing disruptions to healthcare delivery. Additionally, the data generated from predictive maintenance systems can be used to inform procurement decisions, helping clinics invest in more reliable and durable medical technologies. Despite these advantages, challenges such as the initial cost of implementing AI systems, the need for reliable internet connectivity, and the requirement for training healthcare staff in using these technologies must be addressed. Overcoming these challenges involves investing in infrastructure, fostering collaborations between technology providers and healthcare organizations, and developing user-friendly AI applications tailored to the needs of rural clinics. In conclusion, AI-driven predictive maintenance holds significant promise for enhancing the reliability and efficiency of medical equipment in rural clinics, ultimately improving healthcare delivery and patient outcomes in underserved areas.

Artificial Intelligence in Medicine: Revolutionizing Healthcare for Improved Patient Outcomes

The aviation industry is evolving, driven by advanced techology like autonomous systems, machine learning, and data analytics. Artificial intelligence (AI) applications, including predictive maintenance, flight planning, and air traffic management, are transforming operations and safety. However, integrating these technologies poses challenges and ethical dilemmas explored in this chapter. The authors analyze AI's impact on safety, efficiency, customer service, and cost-effectiveness in the airline industry. Through a systematic examination, the authors seek to offer insights into the pivotal question of whether the preference should lean towards a fully automated AI-driven system, human operation, or a harmonious AI-human partnership within the airline industry. By weighing the pros and cons of each approach, the authors aim to shed light on the path that holds the greatest promise for the future of aviation, ultimately ensuring the industry's continued excellence and sustainability.

The convergence of Artificial Intelligence (AI) and Human Resource Management (HRM) has transformed the functioning of organizations, particularly in dynamic industries such as organized retail apparel. With the retailing industry becoming more competitive by the day, efficient handling of human resources becomes a crucial factor for organizational success. This study investigates the influence of AI-based technologies on HRM processes in the organized retail apparel industry in India. It seeks to evaluate the ways in which AI technologies are revolutionizing conventional HR practices like recruitment, onboarding, performance appraisal, training, employee engagement, and workforce analytics. The research follows a descriptive research design and employs primary data gathered through structured questionnaires filled by HR professionals, managers, and employees employed in top retail apparel brands. Secondary data has also been considered to know the trends and adoption of AI in retail HRM. The reports identify that AI applications like chatbots, automated resume filtering, predictive analytics for employee attrition, and customized learning modules are highly enhancing operational efficiency, decision-making, and employee experience. In recruitment, AI allows quicker and unbiased candidate shortlisting, while in performance management, it supports continuous feedback and data-driven assessments. Yet, the study also cites some major challenges posed by AI implementation, such as heavy upfront investments, change resistance, and shortage of technical capabilities among HR professionals, as well as ethical issues like data privacy and biased algorithms. In spite of such problems, most of the respondents hold the opinion that AI augments strategic HR capabilities and supports the objectives of digital transformation in retail companies. This research adds to the existing pool of knowledge on how AI and HRM converge, especially in the case of the structured retail apparel industry. It offers insights for policymakers, HR professionals, and business managers regarding utilizing AI not merely for administrative productivity but also for strategic talent management. The paper concludes with actionable suggestions to refine AI adoption and maximize its advantages while mitigating related risks. Additional research is proposed to study long-term effects and cross-functional incorporation of AI in all retail businesses.

Artificial Intelligence (AI) is revolutionizing the field of health sciences, reshaping how we teach, learn, and practice medicine. As AI technologies become increasingly integrated into healthcare systems, their impact on health sciences education cannot be overstated. From personalized learning experiences to advanced diagnostic training, AI is poised to enhance the quality and accessibility of education for future healthcare professionals. However, this transformation also raises critical questions about ethics, equity, and the future role of educators in an AI-driven world. The transformative role of Artificial Intelligence (AI) in health sciences education is increasingly recognized as a pivotal factor in shaping the future of medical training and practice. As AI technologies continue to evolve, their integration into educational curricula presents both opportunities and challenges that must be carefully navigated to enhance the learning experience for future healthcare professionals. One of the most significant contributions of AI to health sciences education is its ability to personalize learning. Traditional teaching methods often follow a one-size-fits-all approach, which can leave some students struggling to keep up while others are not sufficiently challenged. AI-powered platforms, such as adaptive learning systems, analyze individual student performance and tailor content to meet their unique needs. For example, tools like Osmosis and AMBOSS use AI to provide customized study plans, ensuring that students focus on areas where they need the most improvement (Topol, 2019). This personalized approach not only improves learning outcomes but also fosters a more inclusive educational environment. AI is also transforming clinical training by simulating real-world scenarios. Virtual patient simulations, powered by AI, allow students to practice diagnosing and treating conditions in a risk-free environment. These simulations can replicate rare or complex cases that students might not encounter during their clinical rotations. For instance, platforms like Touch Surgery and SimX use AI to create immersive surgical and emergency care simulations, providing students with hands-on experience before they enter the operating room (McGaghie et al., 2011). Such tools bridge the gap between theory and practice, preparing students for the complexities of modern healthcare. Moreover, AI is enhancing the role of educators by automating administrative tasks and providing data-driven insights into student performance. Grading, attendance tracking, and even curriculum design can be streamlined using AI, allowing educators to focus on mentoring and engaging with students. AI-driven analytics can also identify at-risk students early, enabling timely interventions to support their academic success (Wartman & Combs, 2018). By augmenting the capabilities of educators, AI empowers them to deliver more impactful and student-centered teaching. AI's potential to revolutionize health sciences education lies in its ability to personalize learning experiences and improve educational outcomes. For instance, AI-driven tools can facilitate realistic simulations and automated assessments, allowing students to engage in practical scenarios that mimic real-world clinical situations (Santos & Lopes, 2024). This capability not only enhances the learning process but also prepares students for the complexities of patient care in a technology-driven environment (Grunhut et al., 2022). Furthermore, the incorporation of AI into curricula can foster critical thinking and decision-making skills, essential for navigating the ethical dilemmas that arise in medical practice (Grunhut et al., 2022). Despite the promising applications of AI in education, the integration of these technologies into medical curricula has been slow. A scoping review highlighted that many medical schools have yet to adopt AI training, primarily due to a lack of systematic evidence supporting its implementation (Lee et al., 2021). Additionally, concerns regarding data protection and the ethical implications of AI use in healthcare education have been raised, indicating a need for comprehensive AI education that addresses these issues (Veras et al., 2023; Frehywot & Vovides, 2023). Students have expressed a desire for more robust training in AI, emphasizing the importance of understanding its role in healthcare delivery and decision-making processes (Ahmad et al., 2023; Derakhshanian et al., 2024). Moreover, the rapid advancement of AI technologies necessitates continuous curriculum updates to keep pace with emerging trends. As noted in recent literature, the integration of AI into biomedical science curricula should include subjects related to informatics, data sciences, and digital health (Sharma et al., 2024). This approach not only equips students with the necessary skills to utilize AI effectively but also prepares them for the evolving landscape of healthcare, where AI will play an integral role in diagnostics, treatment personalization, and patient management (Santos & Lopes, 2024; Secinaro et al., 2021). However, the implementation of AI in health sciences education is not without challenges. Ethical considerations surrounding AI's impact on healthcare equity and the potential for bias in AI algorithms must be addressed (Frehywot & Vovides, 2023; Han et al., 2019). Ensuring that AI technologies are used responsibly and equitably in education and practice is crucial to avoid exacerbating existing disparities in healthcare access and outcomes (Rigby, 2019). Furthermore, the lack of faculty expertise in AI poses a significant barrier to its integration into medical education, highlighting the need for targeted training and resources for educators (Derakhshanian et al., 2024). However, the integration of AI into health sciences education is not without challenges. Ethical concerns, such as data privacy and algorithmic bias, must be addressed to ensure that AI tools are used responsibly. Additionally, there is a risk of over-reliance on AI, potentially undermining the development of critical thinking and clinical judgment skills. Educators must strike a balance between leveraging AI’s capabilities and preserving the human elements of teaching and learning. Equity is another pressing issue. While AI has the potential to democratize education, access to these technologies remains uneven. Institutions in low-resource settings may struggle to adopt AI-driven tools, exacerbating existing disparities in global health education. Policymakers and educators must work together to ensure that the benefits of AI are accessible to all, regardless of geographic or socioeconomic barriers. In conclusion, AI is a powerful tool that holds immense promise for transforming health sciences education. By personalizing learning, enhancing clinical training, and supporting educators, AI can help prepare the next generation of healthcare professionals to meet the demands of an increasingly complex healthcare landscape. However, its integration must be guided by ethical principles and a commitment to equity, However, the successful integration of AI into educational curricula requires a concerted effort to address ethical concerns, update training programs, and equip both students and faculty with the necessary knowledge and skills. As the healthcare landscape continues to evolve, embracing AI in education will be essential for fostering a new generation of healthcare providers who are adept at leveraging technology to improve patient care. As we embrace this technological revolution, we must remember that AI is not a replacement for human expertise but a complement to it. The future of health sciences education lies in the synergy between human ingenuity and artificial intelligence.

The urgency of this research lies in understanding the critical role of Artificial Intelligence (AI) in the rapidly evolving landscape of Industry 4.0. As businesses strive to remain competitive, AI's integration into industrial processes has become essential. The primary aim of this study is to examine how AI drives innovation and efficiency within Industry 4.0. Using a literature review methodology, this research synthesizes findings from existing studies to provide a comprehensive overview of AI's impact on various business sectors, including manufacturing, logistics, and supply chain management. The results reveal that AI significantly enhances operational efficiency, predictive maintenance, and decision-making capabilities. Moreover, the study identifies key challenges in AI implementation, such as data privacy and the need for skilled workforce, while also highlighting the vast opportunities for future advancements. This research underscores the importance of AI in achieving higher levels of customization, quality, and competitiveness, paving the way for sustainable business growth in the digital era

Artificial intelligence (AI) is widely considered to be the new technical advancement capable of a large-scale modernization of health care. Considering AI's potential impact on the clinician-patient relationship, health care provision, and health care systems more widely, patients and the wider public should be a part of the development, implementation, and embedding of AI applications in health care. Failing to establish patient and public engagement and involvement (PPIE) can limit AI's impact. This study aims to (1) understand patients' and the public's perceived benefits and challenges for AI and (2) clarify how to best conduct PPIE in projects on translating AI into clinical practice, given public perceptions of AI. We conducted this qualitative PPIE focus-group consultation in the United Kingdom. A total of 17 public collaborators representing 7 National Institute of Health and Care Research Applied Research Collaborations across England participated in 1 of 3 web-based semistructured focus group discussions. We explored public collaborators' understandings, experiences, and perceptions of AI applications in health care. Transcripts were coanalyzed iteratively with 2 public coauthors using thematic analysis. We identified 3 primary deductive themes with 7 corresponding inductive subthemes. Primary theme 1, advantages of implementing AI in health care, had 2 subthemes: system improvements and improve quality of patient care and shared decision-making. Primary theme 2, challenges of implementing AI in health care, had 3 subthemes: challenges with security, bias, and access; public misunderstanding of AI; and lack of human touch in care and decision-making. Primary theme 3, recommendations on PPIE for AI in health care, had 2 subthemes: experience, empowerment, and raising awareness; and acknowledging and supporting diversity in PPIE. Patients and the public can bring unique perspectives on the development, implementation, and embedding of AI in health care. Early PPIE is therefore crucial not only to safeguard patients but also to increase the chances of acceptance of AI by the public and the impact AI can make in terms of outcomes.