AI Applications in Smart Mineral Processing: Ore Characterization, Sorting, and Efficiency

Focus issue: Artificial intelligence in medical physics.

PurposeThe purpose of the paper is to explore the rapidly evolving landscape of artificial intelligence (AI) tools in academic research, highlighting their potential to transform various stages of the research process. AI tools are transforming academic research, offering numerous benefits and challenges.Design/methodology/approachAcademic research is undergoing a significant transformation with the emergence of (AI) tools. These tools have the potential to revolutionize various aspects of research, from literature review to writing and proofreading. An overview of AI applications in literature review, data analysis, writing and proofreading, discussing their benefits and limitations is given. A comprehensive review of existing literature on AI applications in academic research was conducted, focusing on tools and platforms used in various stages of the research process. AI was used in some of the searches for AI applications in use.FindingsThe analysis reveals that AI tools can enhance research efficiency, accuracy and quality, but also raise important ethical and methodological considerations. AI tools have the potential to significantly enhance academic research, but their adoption requires careful consideration of methodological and ethical implications. The integration of AI tools also raises questions about authorship, accountability and the role of human researchers. The authors conclude by outlining future directions for AI integration in academic research and emphasizing the need for responsible adoption.Originality/valueAs AI continues to evolve, it is essential for researchers, institutions and policymakers to address the ethical and methodological implications of AI adoption, ensuring responsible integration and harnessing the full potential of AI tools to advance academic research. This is the contribution of the paper to knowledge.

The recent mainstreaming of Artificial Intelligence (AI) applications and tools has significantly enhanced EFL educators’ research writing process. Nevertheless, few studies exist regarding how EFL educators understand AI applications and their integration into research writing processes and techniques. The current study aims to investigate the attitudes and perceptions of (n= 40) EFL teachers at Majmaah University concerning integrating AI tools in the Research writing process, by collecting data from mixed-methods source questionnaires and interviews. Thematic analysis was employed for qualitative data, while descriptive and inferential statistics through SPSS were used for quantitative data analysis. The questionnaire results demonstrated that most respondents have positive perceptions toward integrating AI tools, believing they can improve efficiency and quality in the research writing process. However, more familiarity with existing AI applications is needed to ensure this integration process. Moreover, the study affirms the significant impact of training and support on the effective integration of AI tools. Respondents strongly agree about the benefits of AI applications in streamlining literature reviews, aiding in data analysis, reducing errors, and enhancing overall language quality. Interview responses further emphasize the possible benefits of AI tools in the research writing process, highlighting efficiency gains, assistance in various writing tasks, and improved quality of EFL teachers’ research articles. All the participants stress the importance of ethical utilization, maintaining quality, and vigilance when integrating these tools into the research writing process.

Recent applications of AI to environmental disciplines: A review

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

Will ChatGPT undermine ethical values in nursing education, research, and practice?

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Artificial Intelligence (AI) tools are rapidly transforming the landscape of modern science, engineering, and technological research and innovation. Their ability to process vast datasets, identify complex patterns, and generate predictive models far surpasses human capabilities, leading to accelerated discovery and unprecedented advancements across diverse fields. In scientific research, AI algorithms are instrumental in analyzing genomic data, predicting protein structures, and simulating complex environmental systems, significantly shortening the time required for breakthroughs in areas like medicine, climate science, and materials science. In engineering, AI is revolutionizing design optimization, predictive maintenance, and autonomous systems. Engineers are leveraging AI-powered design tools to create more efficient and sustainable structures, while predictive maintenance algorithms are reducing downtime and improving the reliability of critical infrastructure. The development of self-driving cars, autonomous robots, and smart manufacturing processes is heavily reliant on the sophisticated AI algorithms that enable these systems to perceive, learn, and adapt to their environments. Furthermore, AI is driving innovation in technological research by enabling the development of novel algorithms, hardware architectures, and computing paradigms. AI is being used to design more energy-efficient processors, create advanced materials with tailored properties, and develop new methods for data storage and retrieval. The ability of AI to automate repetitive tasks, generate hypotheses, and identify unexpected correlations is freeing up researchers to focus on more creative and strategic aspects of their work. By augmenting human intelligence and accelerating the pace of experimentation, AI tools are proving indispensable for pushing the boundaries of scientific knowledge, engineering prowess, and technological advancements, ultimately shaping a future driven by intelligent systems and data-driven insights.

It is the era of personalized medicine, ushering us into a new healthcare era of treatment based on the individual characteristics of each. Using advances in genomics, artificial intelligence (AI), and multi-omics technologies, this revolutionary approach promises diagnosis, prevention, and treatment strategies that go far beyond the “one size fits all” model of the past[1]. From Genomics to Multi-Omics: The Precision Healthcare Foundation The completion of the Human Genome Project was a major step forward in modern medicine, unveiling the sequence of the genetic code that defines each of us. However, the human genome was not the end of the story. With the advent of personalized medicine, we define it through its multi-omics nature, which integrates genomics, transcriptomics, proteomics, metabolomics, and the microbiome. These layers of data give us an understanding of the biological mechanisms driving disease that allow targeted intervention[2]. For instance, genetic biomarkers have made a sea change in oncology. Targeted therapies improve the outcome in breast cancer (e.g. BRCA1/BRCA2) and lung cancer (e.g. mutant EGFR) by detecting such mutations in genomic profiling. Likewise, technologies such as liquid biopsy are advancing cancer care by providing real-time monitoring of circulating tumor DNA without invasive monitoring[3]. Artificial Intelligence and Digital Twins: Accelerating Progress AI and machine learning have become the new indispensable tools for personalized medicine. However, the vastness of datasets, such as genetic profiles, electronic health records (EHRs), and wearable device data, can be analyzed by AI algorithms to predict disease risks, advise treatments, and optimize clinical decision-making. For example, AI-driven models have shown themselves capable of detecting breast cancer with similar accuracy to radiologists, identifying new biomarkers for the prediction of disease, and personalizing pharmacotherapy[4]. A very exciting advance is digital twins. These are so-called virtual replicas of individual patients who are created using real-time health data, simulations, and predictive models. Digital twins enable healthcare providers to test treatment plans in a virtual environment before applying them in the real world. This innovation reduces risks, shortens clinical trial timelines, and paves the way for truly individualized care[5]. Personalized Medicine in Clinical Practice Personalized medicine is already being translated into the clinic, albeit at a slower pace. For example, pharmacogenomics helps clinicians optimize drug therapy for an individual’s genetic makeup. Examples include genetic testing-guided dosing of warfarin or the use of targeted therapies in cancers with defined molecular signatures. In addition, smart devices and digital health tools promote continuous health monitoring and allow patients to take an active role in managing their health[6]. Advances in genomics are allowing us to identify people at high risk for cardiovascular disorders, or diabetes, among other diseases, and intervene before the problems happen. For example, BRCA1 mutation carriers have taken proactive steps, like Angelina Jolie has, to mitigate breast and ovarian cancer risks[7]. Challenging Issues and Ethical Issues The promise of personalized medicine has not gone unchallenged. First, it is still expensive for many healthcare systems to perform multi-omics analysis, AI tools, and genetic testing. If we don’t address equity in access, health disparities will continue to widen[8]. Second, these massive amounts of data are problematic because of the problems those data create around privacy, security, and ethical use. Strong policies and regulations must cover the issue of informed consent and data ownership, as well as protection against the misuse of genetic information[9]. Clinicians and patients alike need to be educated and trained on the many facets of personalized medicine. Streamlined workflows, interoperable health systems, and clinical guidelines are needed for integration into routine care[10]. The Road Ahead: Personal, Predictive, Preventive Technology, as well as our increased knowledge of biology, is the future of personalized medicine. If you keep investing in genomics, AI, and digital tools we are about to enter a world where disease prevention, early detection, and targeted treatment are the norm[11]. This is really future enabled by truly personalized health, predictive health through advanced models to predict health outcomes, and preventive health to prevent before a disease strikes. The future holds the promise not only of improved individual health outcomes but a more efficient, less costly, more equitable healthcare system on a global scale[12]. Conclusion In a future of personalized medicine, we have enormous promise from technological advances and a greater understanding of human biology. The move towards more precise, efficient, and patient-centric healthcare is seeing these integrated with genomic variation, AI, and digital tools. However, to get to this future, there are issues of accessibility, ethical issues, and data security. As we stand on the cusp of a new era, we can not achieve personalized medicine without collaboration between researchers, clinicians, policymakers, and technologists. By doing this, not only will we improve individual health outcomes, but we will also change the global healthcare landscape for generations to come.

Reforming truancy by addressing Students’ global engagement is made possible by using Artificial Intelligence (AI) tools. AI technology facilitates real-time student monitoring, behavior and emotion analysis as well as designing custom learning experiences thus allowing educators to nurture evolving and encompassing classrooms. Using behavior analysis systems and individualized Instructional design tools of AI moderates better engagement and motivation thereby decreasing rates of truancy. Matched emotion recognition using deep learning models, passive biometric feedback interaction systems for engagement measurement and performance flakes are a few AI tools available today. Technologies such as these proactively allow classroom management to improve motivation rather than waiting for an issue to arise. Using interactive tutorials also helps students who find engaging with teachers hard through increasing talking and better content. Monitoring progress and providing feedback through AI also aids teachers in recognizing students who are unable to seek assistance on their own. AI in education as a whole faces a plethora of ethical problems such as data misuse, restrictive access or biased algorithms that AI has to incorporate to be successful. AI integration should be gradual as human intervention will become even more overshadowed by AI tools. AI applications in education can offer real-time assistance to students, help them to improve attendance and also create tailored, fun learning activities, which might help them in the longer term. This research also shows the great promise that AI holds in the field of education while stressing the need for a moral and prudent framework for its use

Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of the current AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

This work analyzes the effectiveness of an artificial intelligence (AI) community- building workshop designed for high school teachers and it focuses on contemporary issues related to AI concepts and applications. A group of high school teachers from local education districts attended a one-day AI hands-on workshop at our university. The workshop included several AI-related topics and hands-on examples and exercises aiming to introduce AI concepts and tools relevant to pre-college education. The participating teachers were expected to become a part of a collaborative network created to design, develop, and implement novel AI learning modules for high school students. Initial and a post-training surveys have been used to measure the impact of this training and to obtain a better understanding of teachers’/students’ readiness for additional hands-on AI experiences and further training. The surveys showed that the teachers gained valuable AI knowledge, AI tools, and attitudes that could help them when introducing AI concepts, applications, and AI ethics to their students. The teachers also explored various AI-based teaching tools that they could use to improve learning outcomes for their students. Based on the positive results of this work, one of the authors developed and implemented a three-credit graduate elective course ED 537 AI in K-12 Education at the School of Education at our university. This is an updated and improved version of the ASEE conference paper titled “Engaging High School Teachers in Artificial Intelligence Concepts and Applications.”

Since last few years, there has been a significant rise in the development and application of artificial intelligence (AI) tools in the field of pathology. Artificial intelligence (AI) is basically a field of research in which technology is incorporated to mimic human intelligence. These are empowered through different learning methods like machine learning (ML) and deep learning (DL). Machine and deep learning are the techniques which need data for working and that data is supervised to extract the probable outcomes. The application and use of Artificial intelligence is extended to a large extent. Diagnostic ability and outcome of the doctors can be compromised due to various factors like heavy workload, complexity of work and potential fatigue which can be encountered by AI tools. These tools reduce the workload and enhances the working efficiency. This paper focuses on the importance of artificial intelligence and discusses the recent advances in AI and deep learning in the field of oral histopathology.

Trustworthiness has become a key concept for the ethical development and application of artificial intelligence (AI) in medicine. Various guidelines have formulated key principles, such as fairness, robustness, and explainability, as essential components to achieve trustworthy AI. However, conceptualizations of trustworthy AI often emphasize technical requirements and computational solutions, frequently overlooking broader aspects of fairness and potential biases. These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. This viewpoint article presents an interdisciplinary approach to analyzing trust in AI and trustworthy AI within the medical context, focusing on (1) social sciences and humanities conceptualizations and legal perspectives on trust and (2) their implications for trustworthy AI in health care. It focuses on real-world challenges in medicine that are often underrepresented in theoretical discussions to propose a more practice-oriented understanding. Insights were gathered from an interdisciplinary workshop with experts from various disciplines involved in the development and application of medical AI, particularly in oncological imaging and genomics, complemented by theoretical approaches related to trust in AI. Results emphasize that, beyond common issues of bias and fairness, knowledge and human involvement are essential for trustworthy AI. Stakeholder engagement throughout the AI life cycle emerged as crucial, supporting a human- and multicentered framework for trustworthy AI implementation. Findings emphasize that trust in medical AI depends on providing meaningful, user-oriented information and balancing knowledge with acceptable uncertainty. Experts highlighted the importance of confidence in the tool's functionality, specifically that it performs as expected. Trustworthiness was shown to be not a feature but rather a relational process, involving humans, their expertise, and the broader social or institutional contexts in which AI tools operate. Trust is dynamic, shaped by interactions among individuals, technologies, and institutions, and ultimately centers on people rather than tools alone. Tools are evaluated based on reliability and credibility, yet trust fundamentally relies on human connections. The article underscores the development of AI tools that are not only technically sound but also ethically robust and broadly accepted by end users, contributing to more effective and equitable AI-mediated health care. Findings highlight that building AI trustworthiness in health care requires a human-centered, multistakeholder approach with diverse and inclusive engagement. To promote equity, we recommend that AI development teams involve all relevant stakeholders at every stage of the AI lifecycle—from conception, technical development, clinical validation, and real-world deployment.

UNSTRUCTURED Trustworthiness has become a key concept for the ethical development and application of artificial intelligence (AI) in medicine. Various guidelines have formulated key principles, such as fairness, robustness, and explainability, as essential components to achieve trustworthy AI. However, conceptualizations of trustworthy AI often emphasize technical requirements and computational solutions, frequently overlooking broader aspects of fairness and potential biases. These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. This viewpoint article presents an interdisciplinary approach to analyzing trust in AI and trustworthy AI within the medical context, focusing on (1) social sciences and humanities conceptualizations and legal perspectives on trust and (2) their implications for trustworthy AI in health care. It focuses on real-world challenges in medicine that are often underrepresented in theoretical discussions to propose a more practice-oriented understanding. Insights were gathered from an interdisciplinary workshop with experts from various disciplines involved in the development and application of medical AI, particularly in oncological imaging and genomics, complemented by theoretical approaches related to trust in AI. Results emphasize that, beyond common issues of bias and fairness, knowledge and human involvement are essential for trustworthy AI. Stakeholder engagement throughout the AI life cycle emerged as crucial, supporting a human- and multicentered framework for trustworthy AI implementation. Findings emphasize that trust in medical AI depends on providing meaningful, user-oriented information and balancing knowledge with acceptable uncertainty. Experts highlighted the importance of confidence in the tool's functionality, specifically that it performs as expected. Trustworthiness was shown to be not a feature but rather a relational process, involving humans, their expertise, and the broader social or institutional contexts in which AI tools operate. Trust is dynamic, shaped by interactions among individuals, technologies, and institutions, and ultimately centers on people rather than tools alone. Tools are evaluated based on reliability and credibility, yet trust fundamentally relies on human connections. The article underscores the development of AI tools that are not only technically sound but also ethically robust and broadly accepted by end users, contributing to more effective and equitable AI-mediated health care. Findings highlight that building AI trustworthiness in health care requires a human-centered, multistakeholder approach with diverse and inclusive engagement. To promote equity, we recommend that AI development teams involve all relevant stakeholders at every stage of the AI lifecycle—from conception, technical development, clinical validation, and real-world deployment.