Potential Applications Of Artificial Intelligence Research Articles

The Limitless Potential of Artificial Intelligence in Paediatric Dentistry

Background: The integration of artificial intelligence (AI) in paedi-atric dentistry has grown signifi-cantly, offering new possibilities for diagnostics, treatment plan-ning, and patient care. AI’s capaci-ty to handle large datasets and generate accurate predictions is transforming dental practice.Objective: This narrative review explores the potential applica-tions of AI in paediatric dentistry, focusing on its benefits, challeng-es, and future implications.Methods: A comprehensive litera-ture review was conducted using databases such as PubMed, Sco-pus, and Web of Science. Relevant studies published between 2000 and 2023 were selected based on predefined inclusion criteria. The quality of the studies was ap-praised using the Joanna Briggs Institute tools.Results: AI applications, including image analysis, diagnosis, treat-ment planning, and patient man-agement, show significant prom-ise in paediatric dentistry. AI-powered tools can improve diag-nostic accuracy, reduce treat-ment inconsistencies, and en-hance patient outcomes. Howev-er, challenges related to costs, complexity, and ethical concerns remain.Conclusion: AI will not replace paediatric dentists but will serve as a valuable tool to support clini-cal decision-making. Future re-search should focus on overcom-ing current limitations and ensur-ing safe integration into clinical practice.

Recent Advances in Hydrogel‐Based Soft Bioelectronics and its Convergence with Machine Learning

Recent advancements in artificial intelligence (AI) technologies, particularly machine learning (ML) techniques, have opened up a promising frontier in the development of intelligent soft bioelectronics, demonstrating unparalleled performance in interfacing with the human body. Hydrogels, owing to their unique combination of biocompatibility, tunable mechanical properties, and high water content, have emerged as a versatile platform for constructing soft bioelectronic devices. Functionalized hydrogels, such as conductive hydrogels, can efficiently capture biosignals from various target tissues while seamlessly forming conformal and reliable interfaces. They can also function as an intermediary layer between biological tissues and soft bioelectronics for diagnosis and therapy purposes. Meanwhile, ML has demonstrated its efficacy in processing extensive datasets collected from the bioelectronics. The convergence of hydrogel‐based soft bioelectronics and ML has unlocked a myriad of possibilities in unprecedented diagnostics, therapeutics, and beyond. In this review, the latest advances in hydrogel‐based soft bioelectronics are introduced. After briefly describing the materials and device strategies for high‐performance hydrogel bioelectronics, how ML can be integrated to augment the functionalities is discussed. Recent examples of ML‐integrated hydrogel bioelectronics are then discussed. Finally, the review is concluded by introducing future potential applications of AI in hydrogel‐based bioelectronics, alongside inherent challenges in this interdisciplinary domain.

Leveraging AI technology in sarcoidosis.

Sarcoidosis is a systemic, granulomatous disease of uncertain cause. Diagnosis may be difficult, prognosis uncertain and response to treatment unpredictable. The application of artificial intelligence to sarcoidosis may provide clinical decision support for these challenges. This review will provide an overview of current and potential future applications of artificial intelligence in sarcoidosis. The predominant application of artificial intelligence in sarcoidosis is imaging. Imaging models may differentiate sarcoidosis from other pulmonary disorders. Models, which predict survival and identify key factors relevant to prognosis are also available. The application of cluster analysis to organize sarcoidosis patients into developmental phenotypes is underway. Machine learning algorithms to evaluate the treatment response of sarcoidosis patients do not yet exist but similar models may evaluate patients with other inflammatory disease. The potential applications of artificial intelligence to sarcoidosis is vast, but there are practical limitations that warrant consideration. These include: the accessibility of data, biases in data, cost and privacy. The application of artificial intelligence in medicine is still in its early stages but models are poised to support the diagnostic and prognostic challenges in sarcoidosis patients. The predictive power of these artificial intelligence is likely to come from combining various models, trained on content-rich datasets from phenotypically heterogeneous sarcoidosis patients.

Can the Pioneering Impact of Artificial Intelligence in Anaesthetic Practice Uphold Good Medical Practice?

The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs. However, reliance on technology may reduce clinical acumen but furthermore there are issues surrounding data quality, privacy as well as legal and ethical concerns, which require further evaluation. Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.

Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.

The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.

Generative artificial intelligence and the personalization of health professional education: A narrative review.

This narrative review examined the intersection of generative artificial intelligence (GAI) and the personalization of health professional education (PHE). This review aims to the elucidate the current condition of GAI technologies and their particular uses in the field of PHE. Data were extracted and analyzed from studies focusing on the demographics and professional development preferences of healthcare workers, the competencies required for personalized precision medicine, and the current and potential applications of artificial intelligence (AI) in PHE. The review also addressed the ethical implications of AI implementation in this context. Findings indicated a gender-balanced healthcare workforce with a predisposition toward continuous professional development and digital tool utilization. A need for a comprehensive educational framework was identified to include a spectrum of skills crucial for precision medicine, emphasizing the importance of patient involvement and bioethics. AI was found to enhance educational experiences and research in PHE, with an increasing trend in AI applications, particularly in surgical education since 2018. Ethical challenges associated with AI integration in PHE were highlighted, with an emphasis on the need for ethical design and diverse development teams. Core concepts in AI research were established, with a spotlight on emerging areas such as data science and learning analytics. The application of AI in PHE was recognized for its current benefits and potential for future advancements, with a call for ethical vigilance. GAI holds significant promise for personalizing PHE, with an identified need for ethical frameworks and diverse developer teams to address bias and equity in educational AI applications.

The Possibilities of Using Artificial Intelligence as a Key Technology in the Current Employee Recruitment Process

The current business environment faces numerous new challenges closely linked to the rapid development of information and communication technologies, which influence the corporate landscape. This article focuses on exploring the possibilities of integrating artificial intelligence, as one of the key technologies of today, into the recruitment process. Its aim is to examine the potential applications of artificial intelligence across various stages of employee recruitment. To achieve this goal, the authors employed various methods and techniques, including the PICOS framework, scientific mapping, and case study analysis. The outcome of this study identifies opportunities for leveraging artificial intelligence in the employee recruitment process within corporate settings. The results reflect the current research gaps concerning the analysis of the personnel processes and conceptualizing the implementation possibilities of artificial intelligence in these processes. The contribution of this article to the academic community lies in its conceptualization, providing a foundation for further research focused on analyzing the impacts of integrating AI into recruitment processes.

Artificial Intelligence in Public Health: A Review Article

Background: Artificial intelligence (AI) is the simulation of human intelligence processes by machines, such as learning, reasoning, and problem-solving. AI has the potential to transform the field of public health, which is concerned with promoting and protecting the health of populations and preventing diseases. AI can help public health organizations perform their essential functions more efficiently, effectively, and equitably; AI can transform the public health field, but it also poses some challenges and risks that must be addressed carefully and responsibly. Objectives: This paper reviews the current and potential applications of AI in public health, discusses the opportunities and challenges of AI for public health, and provides recommendations for the ethical and responsible use of AI in public health. Results: AI can improve the speed and accuracy of diagnosis, screening, and treatment of various diseases and support disease surveillance, outbreak response, and health systems management. However, AI poses significant challenges and risks, such as ethical, legal, and social implications, data quality and security, algorithmic bias and fairness, and environmental impact. Conclusion: AI has the potential to revolutionize public health, but it comes with risks that must be addressed. Promoting digital literacy, establishing modern data governance frameworks, and investing in advanced data infrastructure and procedures are essential. Public health organizations must also train their workforce to collaborate with AI. By doing so, they can improve health outcomes, reduce health disparities, and advance public health science and practice.

Harnessing AI and NLP Tools for Innovating Brand Name Generation and Evaluation: A Comprehensive Review

The traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce© or SnapChat©. Furthermore, brands such as Kodak©, Xerox©, Google©, Häagen-Dazs©, and Twitter© have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike.

Potential application of artificial intelligence in cancer therapy.

This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat

AbstractFusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end‐use quality. Phenotyping of FHB resistance traits, Fusarium‐damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB‐resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)‐based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI‐based FDK (FDK_QVIS/FDK_QNIR) showed a two‐fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI‐based FDK was evaluated along with other traits in multi‐trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single‐trait GS model. We next used hyperspectral imaging of FHB‐infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single‐trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB‐infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best‐performing MT GS models. This study demonstrates the potential application of AI and vision‐based platforms to improve PA for FHB‐related traits using genomic and phenomic selection.

Artificial intelligence in forensic medicine and related sciences – selected issues = Sztuczna inteligencja w medycynie sądowej i naukach pokrewnych – wybrane zagadnienia

Aim. The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Materials and methods. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as „artificial intelligence,” „deep learning,” „machine learning,” „forensic medicine,” „legal medicine,” „forensic pathology” and „medicine” were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

Artificial intelligence in forensic medicine and related sciences – selected issues = Sztuczna inteligencja w medycynie sądowej i naukach pokrewnych – wybrane zagadnienia

Aim. The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Materials and methods. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as „artificial intelligence,” „deep learning,” „machine learning,” „forensic medicine,” „legal medicine,” „forensic pathology” and „medicine” were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

Brief Notes on the European Geographical Indication Law: Among Sustainability Implications and Artificial Intelligence Applications

Purpose: Geographical indications (GIs) within the European Union are – as is known – legally protected designations highlighting unique qualities of products tied to their place of origin; accordingly, GIs can promote sustainable agricultural practices, preserve traditional knowledge and contribute to rural development, but, notwithstanding, issues in verifying and authenticating GI products persist. Within this framework, this paper investigates the intersection of EU geographical indication law with sustainability goals and potential applications of artificial intelligence (AI) in streamlining GI compliance and enhancing consumer trust. Study design/methodology/approach: The behind research employs a mixed-methods approach; in fact, it includes a systematic review of relevant EU legislation and policy documents alongside qualitative case studies exploring the use of AI-based technologies (e.g., precision agriculture, terroir monitoring, traceability systems…) in GI value chains. Findings: The analysis confirms the wanted complex interplay between GI law and sustainability; in this way, while GIs can be powerful tools for promoting environmentally responsible production, existing frameworks may not fully capture all dimensions of sustainability; in addition, case studies demonstrate the promise of AI in improving product traceability, combating fraud and supporting informed consumer choices regarding GI products. Originality/value: Therefore, the paper argues for a more holistic approach to GI law that integrates sustainability metrics beyond geographic origin, suggesting that responsible AI adoption presents the potential to significantly strengthen GI systems: policy recommendations should include incentivising the development of ethical AI solutions for GI verification, promoting data sharing along supply chains, and raising consumer awareness of the sustainability benefits associated with GI products.

The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support.

Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real-time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an 'ideal' source for data-driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI-based tools in the setting of temporary MCS with an Impella device. We explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management. We identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility-related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI-driven insights in the setting of MCS with an Impella device. Temporary MCS with an Impella device has great potential to benefit from the integration of AI-based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision-making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.

The Saudi Community View of the Use of Artificial Intelligence in Health Care.

Artificial intelligence (AI) holds the promise to revolutionize the field of medicine and enhance the well-being of countless patients. Its capabilities span various areas, including disease prevention, accurate diagnosis, and the development of innovative treatments. Moreover, AI has the potential to streamline health-care delivery and lower expenses. The community should be aware of the potential applications of AI in health care, so that they can advocate for its development and adoption. Hence, the objective of this study is to assess the community's perspectives regarding the utilization of AI in health care. A cross-sectional, questionnaire-based study was conducted in Saudi Arabia during the period of June to October 2023. The questionnaire was distributed to people on various social media platforms using a convenience sampling method. The collected data were analyzed using Statistical Package for the Social Sciences. The study included 771 individuals, with 42.5% having a positive outlook on the use of AI in health care, 31.8% having a neutral view, and 7.5% having a negative view. The only factor associated with a positive opinion was regional differences (P = 0.006). Moreover, participants who used medical apps or programs (P = 0.026), wearables (P = 0.027), felt more confident in using technology (P < 0.001), enjoyed using technology (P < 0.001), found it easier to familiarize themselves with new devices or programs (P < 0.001), and had more knowledge about AI (P < 0.001) had more positive opinions regarding the use of AI in health care. The study found that most Saudis, especially those who were familiar with the use of technology, support the use of AI in health care, with a positive or neutral view. Yet, targeted campaigns in certain regions are needed to educate the entire community about AI's potential benefits.

Integration of Artificial Intelligence in Science Teaching in Primary Education: Applications for Teachers

The purpose of this study is to serve as the central notion that the whole research endeavour revolves around. It provides a framework for examining the potential applications of artificial intelligence (AI) for teachers operating in the field of scientific education. A clear framework for an in-depth analysis that tries to shed light on the opportunities and challenges associated with the use of AI technology in primary scientific education is provided by this thesis statement, which specifies the purpose of the research as well as the scope of the investigation. The project's objective is to broaden the existing body of knowledge and provide insights into the ways in which educators might make use of technologies that are powered by AI to enhance the instructional techniques they use and the outcomes of student learning. In order to do this, a well-prepared thesis statement will ideally be used.

Applying eXplainable AI Techniques to Interpret Machine Learning Predictive Models for the Analysis of Problematic Internet Use among Adolescents

This research focusses on the potential application of artificial intelligence (AI) techniques in the analysis of behavioural addictions, specifically addressing problematic Internet use among adolescents. Using tabular data from a representative sample from Serbian high schools, the authors investigated the feasibility of employing eXplainable AI (XAI) techniques, placing special emphasis on feature selection and feature importance methods. The results indicate a successful application to tabular data, with global interpretations that effectively describe predictive models. These findings align with previous research, which confirms both relevance and accuracy. Interpretations of individual predictions reveal the impact of features, especially in cases of misclassified instances, underscoring the significance of XAI techniques in error analysis and resolution. Although AI’s influence on the medical domain is substantial, the current state of XAI techniques, although useful, is not yet advanced enough for the reliable interpretation of predictions. Nevertheless, XAI techniques play a crucial role in problem identification and the validation of AI models.

Using artificial intelligence to improve poultry productivity – a review

Abstract A recent study investigated the potential applications of artificial intelligence (AI) in poultry farming. One area where AI can be helpful is in the early detection of diseases. By analyzing data from various sources, such as sensor readings and health records, AI algorithms can identify potential disease outbreaks or health risks in flocks, allowing farmers to take timely preventive measures. Another area where AI can be applied is in controlling the environmental conditions of farms. By analyzing data from sensors that monitor temperature, humidity, ventilation, and lighting conditions, AI algorithms can help farmers create a comfortable and healthy environment for birds, improving their growth and reducing their stress. AI can also optimize the management of healthcare supplies for poultry. By analyzing the nutritional requirements of birds and the availability and prices of different ingredients, AI algorithms can help farmers optimize feed formulations, reducing waste and environmental impacts. Finally, the study explored the use of robots in poultry care. Robots can be used for cleaning, feeding, and monitoring individual birds. By automating these tasks, farmers can reduce labor costs and improve the efficiency of their operations. Overall, the study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation.

Modern imaging of acute pulmonary embolism

The first-choice imaging test for visualization of thromboemboli in the pulmonary vasculature in patients with suspected acute pulmonary embolism (PE) is multidetector computed tomography pulmonary angiography (CTPA) – a readily available and widely used imaging technique. Through technological advancements over the past years, alternative imaging techniques for the diagnosis of PE have become available, whilst others are still under investigation. In particular, the evolution of artificial intelligence (AI) is expected to enable further innovation in diagnostic management of PE. In this narrative review, current CTPA techniques and the emerging technology photon-counting CT (PCCT), as well as other modern imaging techniques of acute PE are discussed, including CTPA with iodine maps based on subtraction or dual-energy acquisition, single-photon emission CT (SPECT), magnetic resonance angiography (MRA), and magnetic resonance direct thrombus imaging (MRDTI). Furthermore, potential applications of AI are discussed.