Development Of Artificial Intelligence Applications Research Articles

تطبيقات الإدارة للذكاء الاصطناعي في اتخاذ القرارات الإدارية دراسة تطبيقية على شركة الاتصالات السعودية

The study aimed to investigate the impact of artificial intelligence, with its dimensions (genetic algorithms, intelligent agents, expert systems), on managerial decision-making, with its aspects (decision-making stages, decision-making determinants, decision-making elements, participation in decision-making) in Saudi Telecommunications Company. To achieve the study objectives, a descriptive-analytical approach was followed. The study population included all employees within Saudi Telecommunications Company. Due to the difficulty of conducting a comprehensive census of all individuals in the study population, the study used a simple random sampling method by distributing the questionnaire link across all social media platforms used by the study population. The study sample comprised 464 individuals who responded to the questionnaire. The study results revealed a statistically significant positive impact of the level of artificial intelligence usage on managerial decision-making in Saudi Telecommunications Company. A strong positive correlation was found between the usage of artificial intelligence and managerial decision-making in the company. The study recommended focusing on providing financial resources, logistical capabilities, and support for the development of artificial intelligence applications that can help maintain the outstanding level of artificial intelligence application usage in Saudi Telecommunications Company. Additionally, efforts should be directed towards providing the technological infrastructure that contributes to enhancing the company's ability to utilize artificial intelligence applications.

A review of artificial intelligence in video games: From preset scripts to self-learning

It is now the 21st century, with the progressive development of various science and technology, such as artificial intelligence, big data, and so on, and these ever-evolving technologies have also greatly contributed to the development of today's flourishing video game field. This paper focuses on the development of artificial intelligence applications in video games over the past two decades, from preset scripts to self-learning processes, and adopts the research method of literature review. The paper concludes that the shift from pre-scripted to self-learning AI marks a shift in video games from experiences with clear rules and controlled processes to complex, dynamic, personalized experiences. This shift brings not only new opportunities but also new challenges. In the future, we can expect to see more research and practice to explore and take advantage of more of the possibilities of self-learning AI in video games.

The impact of artificial intelligence application on enterprise environmental performance: Evidence from microenterprises

The progress of artificial intelligence (AI) technology is an important way to solve the problem of global sustainable development. This paper discusses how AI, as a new scientific force, affects the pollution reduction of enterprises from a micro perspective. In this paper, the robot data provided by the International Federation of Robotics and pollution data of industrial enterprises in China are used for empirical analysis to test the effect of AI on pollution performance of enterprises. The results show that the application of AI can reduce enterprise emissions through technological innovation effect and labor substitution effect, and this result remains robust after sample substitution and variable substitution. Further heterogeneity analysis shows that AI has a more significant impact on enterprises in big cities and southern cities, as well as large enterprises, old enterprises and non-technology-intensive enterprises. The research results of this paper provide an effective reference for solving how to achieve green and sustainable development of artificial intelligence applications.

Artificial intelligence-driven biomedical genomics

As genomic research becomes more complex and data-rich, artificial intelligence (AI) has emerged as a crucial tool for processing and analyzing high-dimensional genomic data, accelerating biomarker discovery, and enhancing genomic sequence annotations. Despite the increasing application of AI in genomic research, challenges persist, particularly regarding the integration of biomedical knowledge into algorithm development. We reviewed high-quality, AI-driven biomedical genomic studies from the past five years, covering applications in disease prediction, detection, diagnosis, and treatment. Each category highlights how different AI techniques are applied in biomedical contexts. Furthermore, we identify current challenges and potential solutions in AI-assisted biomedical genomics. This comprehensive review is designed to encourage collaboration among computer scientists, healthcare professionals, and interested communities, propelling the development of AI applications that can be smoothly integrated into routine medical services.

Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades

Literature search across different databases showed that the application of artificial intelligence (AI) in heating, ventilation and air conditioning (HVAC) equipment has been extensively studied. On the commercial front, Internet search suggested that numerous AI-equipped HVAC products have been launched. These products apply AI in very different ways, and their energy-saving effects are also different. Such divergence and uncertain energy-saving effects may hinder AI application. To overcome this difference and accelerate the development of AI applications, the present study proposed a double diamond preferred reporting items for systematic reviews and meta-analysis (PRISMA) method—an analysis method that combined literature review with design thinking. Through a process of divergence-convergence-re-divergence, this study described how to design AI functions for energy-efficient HVAC systems, taking into account more than 1,700 research papers it had reviewed. However, there was a limitation on the part re-divergence. Because the vast majority of research papers only published results of successful AI applications, no cases of failed applications were available for review, making it impossible to re-think profoundly. Instead, this study collected raw data from 88 research papers and used these data to analyze the effectiveness and ineffectiveness of AI in depth. It was concluded that AI application must be accompanied by necessary hardware improvements to achieve effective energy savings. AI-enabled energy-saving effects for chillers, air-handing units, heating systems, and air conditioners, as well as corresponding hardware upgrades, were discussed.

VARVO: a Novel Method for the Fast Detection of Vehicle Crash Events from Video Only Data

Around 1,35 million people worldwide die each year because of traffic incidents, and it is estimated that another 50 million suffers serious injuries. This picture is particularly dramatic in the Andean Region where the death toll due to traffic accidents is as high as 127 deaths per million inhabitants. In recent years the deployment of the so-called Intelligent Transport Systems (ITS) across several developed countries has helped to reduce the number of deaths due to traffic accidents. An integral part of an ITS is the automatic detection of traffic incidents from video and sensor data. However, the scarcity of curated datasets, especially those that contained a reasonable number of positive instances of traffic incidents is hampering the development of artificial intelligence applications for the domain of traffic research. Given this scenario, we pursued answering the following research question: is it possible to detect car crashes through supervised machine learning based on the estimated speeds of cars from video only-data? Here we present VARVO, a novel algorithm for the detection of traffic incidents that does not rely on sensors for cars speed detection. VARVO performs a supervised classification task based on the sequential use of convolutional network-based object detection and bi-directional tracking. We also describe how the models implemented in VARVO improved their classification accuracy by applying an oversampling algorithm to deal with class imbalance. We believe that the deployment of VARVO could be linked to static traffic video cameras and could be part of the Intelligent Transport Systems foundations in other Andean countries.

Этические риски в практике искусственного интеллекта

The article raises the question of the relationship between the practice of artificial intelligence and universal ethics. The topic is fundamentally important, since a person and society that have lost their ethical foundations are deprived of their humanity. The ethics of scientists, designers, high-level managers play a decisive role in modern processes of development and application of artificial intelligence. In their activities, an ethic of responsibility develops, originating from the Russell-Einstein Manifesto, published in 1955 in the conditions of a nuclear standoff during the Cold War. The article shows the modern indescribably rapid growth and development of artificial intelligence applications in the practical life of people. Attention is drawn to the fact of the presence of uncertainty in situations of practical application of artificial intelligence, the presence of unforeseen consequences in addition to the expected consequences, who is responsible for those consequences (individuals, corporations, governments). The responsibility lies with the one who decides on the action: the actor. The actor is in a situation of ethical risk. It is shown that the risk increases due to a number of circumstances: 1) a variety of applications; 2) uncontrolled rampant growth; 3) difficulties in tracking the empirical situation of application; 4) difficulties in theoretical analysis of the situation of action. The article focuses on the risks of the practice of peaceful use of remotely piloted aircraft, as well as their military use and automated weapons without operator confirmation. The sharp, apparently exponential growth of information technology, the practical implementation of artificial intelligence, puts people in a difficult situation, an ethical situation: what to choose ‘to have or to be’. Or is there a third choice?

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study.

Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the "pAItient" (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value) project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center. The first part of the pAItient project aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations. In addition, patients and physicians were interviewed. The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium. The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications. RR2-10.2196/42208.

The Use of Artificial Neural Networks in the Public Sector

Artificial intelligence (AI) is an extensive scientific field, part of which is the concept of deep learning, belonging to broader family of machine learning (ML) methods, based on artificial neural networks (ANNs). ANNs are active since the 1940s and are applied in many fields. There have been actions around the world for the digital transformation of the public sector and the use of new innovative technologies, but the trajectory and degree of adoption of artificial intelligence technologies in the public sector have been unsatisfactory. Similar issues must be handled, and these problems must be classified. In the present work, preparatory searches were made on Scopus and IEEE bibliographic databases in order to obtain information for the progress of the adoption of ANNs in the public sector starting from the year 2019. Then, a systematic review of published scientific articles was conducted using keywords. Among the 2412 results returned by the search and the application of the selection/rejection criteria, 10 articles were chosen for analysis. The conclusion that emerged after reading the articles was that while the scientific community has a lot of suggestions and ideas for the implementation of ANNs and their financial effects, in practice, there is no appropriate use of them in the public sector. Occasionally, there are cases of implementation funded by state or non-state bodies without a systematic application and utilization of these technologies. The ways and methods of practical application are not further specified, so there are no indications for the systematic application of specialized deep learning techniques and ANNs. The legal framework for the development of artificial intelligence applications, at least in the European Union (EU), is under design, like the necessary ISO standards from an international perspective, and the economic impact of the most recent AI-based technologies has not been fully assessed.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

Artificial Intelligence and Precision Health Through Lenses of Ethics and Social Determinants of Health: Protocol for a State-of-the-Art Literature Review

Precision health is a rapidly developing field, largely driven by the development of artificial intelligence (AI)-related solutions. AI facilitates complex analysis of numerous health data risk assessment, early detection of disease, and initiation of timely preventative health interventions that can be highly tailored to the individual. Despite such promise, ethical concerns arising from the rapid development and use of AI-related technologies have led to development of national and international frameworks to address responsible use of AI. We aimed to address research gaps and provide new knowledge regarding (1) examples of existing AI applications and what role they play regarding precision health, (2) what salient features can be used to categorize them, (3) what evidence exists for their effects on precision health outcomes, (4) how do these AI applications comply with established ethical and responsible framework, and (5) how these AI applications address equity and social determinants of health (SDOH). This protocol delineates a state-of-the-art literature review of novel AI-based applications in precision health. Published and unpublished studies were retrieved from 6 electronic databases. Articles included in this study were from the inception of the databases to January 2023. The review will encompass applications that use AI as a primary or supporting system or method when primarily applied for precision health purposes in human populations. It includes any geographical location or setting, including the internet, community-based, and acute or clinical settings, reporting clinical, behavioral, and psychosocial outcomes, including detection-, diagnosis-, promotion-, prevention-, management-, and treatment-related outcomes. This is step 1 toward a full state-of-the-art literature review with data analyses, results, and discussion of findings, which will also be published. The anticipated consequences on equity from the perspective of SDOH will be analyzed. Keyword cluster relationships and analyses will be visualized to indicate which research foci are leading the development of the field and where research gaps exist. Results will be presented based on the data analysis plan that includes primary analyses, visualization of sources, and secondary analyses. Implications for future research and person-centered public health will be discussed. Results from the review will potentially guide the continued development of AI applications, future research in reducing the knowledge gaps, and improvement of practice related to precision health. New insights regarding examples of existing AI applications, their salient features, their role regarding precision health, and the existing evidence that exists for their effects on precision health outcomes will be demonstrated. Additionally, a demonstration of how existing AI applications address equity and SDOH and comply with established ethical and responsible frameworks will be provided. PRR1-10.2196/40565.

ARTIFICIAL INTELLIGENCE AS A COMMON HERITAGE OF MANKIND

Artificial intelligence technologies today employ techniques known as machine learning and deep learning, which apply datasets to a suitable mathematical or statistical technique known as an algorithm. This in turn produces a model that can be employed to predict an outcome, given a new set of data that was previously unseen by the model. The principle of common heritage of mankind, which has originated in the 19th century, promotes the concept that humanity as a whole has rights and responsibilities over territories or outer space. This study aims to advance the concept of treating the components of artificial intelligence as an intellectual common in the form of a common heritage of mankind, in order to promote the discovery and the development of more novel artificial intelligence applications for the benefit of people around the world. This work employs a mix of legal doctrinal research related to intellectual property law and conceptual theoretical discussion. The potential application of open access and open data licensing is discussed. The history of the common heritage of mankind is covered, and the potential benefit of recognizing basic artificial intelligence components as a common heritage of mankind is explored. Finally, a novel method for implementing this idea is proposed. This work is significant in advancing a method to liberate certain artificial intelligence technologies from intellectual property rights protection, in order to promote greater experimentation and the development of artificial intelligence applications for the greater good of humanity.

Applications of Artificial Intelligence in Auditing

Objective: The purpose of this research is to examine the application of artificial intelligence in auditing. Methodology: This study is applied in objective and descriptive-survey in terms of data collection, which was conducted with a qualitative approach. The population of this study consisted of experts and opinion leaders in the field of accounting and auditing, university professors and lecturers, and the board of directors of the national auditing organization, from which 24 individuals were selected using a purposive sampling method. Semi-structured interviews were utilized for data collection until theoretical saturation was achieved. For data analysis, systematic grounded theory and for analyses, three types of coding: open, axial, and selective, based on Strauss and Corbin's coding technique, were used. Findings: The findings indicate that the model of artificial intelligence application in auditing includes causal conditions (human resources, financial resources, empowerment development, and competitiveness), contextual factors (accessibility, standardization, incentivization), strategies (strategy formulation and planning), and intervening conditions (management, cultural, infrastructure) outcomes (efficiency, accuracy, and speed). Conclusion: Considering the research findings, it is necessary to develop the prerequisites of artificial intelligence in a process-oriented manner to create the context and motivation among auditors for the development of artificial intelligence applications in auditing in the country.

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study.

The past decade has seen major advances in the use of artificial intelligence (AI) to solve various biomedical problems, including cancer. This has resulted in more than 6000 scientific papers focusing on AI in oncology alone. The expansiveness of this research area presents a challenge to those seeking to understand how it has developed. A scientific analysis of AI in the oncology literature is therefore crucial for understanding its overall structure and development. This may be addressed through bibliometric analysis, which employs computational and visual tools to identify research activity, relationships, and expertise within large collections of bibliographic data. There is already a large volume of research data regarding the development of AI applications in cancer research. However, there is no published bibliometric analysis of this topic that offers comprehensive insights into publication growth, co-citation networks, research collaboration, and keyword co-occurrence analysis for technological trends involving AI across the entire spectrum of oncology research. The purpose of this study is to investigate documents published during the last decade using bibliometric indicators and network visualization. This will provide a detailed assessment of global research activities, key themes, and AI trends over the entire breadth of the oncology field. It will also specifically highlight top-performing authors, organizations, and nations that have made major contributions to this research domain, as well as their interactions via network collaboration maps and betweenness centrality metric. This study represents the first global investigation of AI covering the entire cancer field and using several validated bibliometric techniques. It should provide valuable reference material for reorienting this field and for identifying research trajectories, topics, major publications, and influential entities including scholars, institutions, and countries. It will also identify international collaborations at three levels: micro (that of an individual researcher), meso (that of an institution), and macro (that of a country), in order to inform future lines of research. The Science Citation Index Expanded from the Web of Science Core Collection was searched for articles and reviews pertaining exclusively to AI in cancer from 2012 through 2022. Annual publication trends were plotted using Microsoft Excel 2019. CiteSpace and VOSViewer were used to investigate the most productive countries, researchers, journals, as well as the sharing of resources, intellectual property, and knowledge base in this field, along with the co-citation analysis of references and keywords. A total of 6757 documents were retrieved. China produced the most publications of any country (2087, 30.89%), and Sun Yat Sen University the highest number (167, 2.47%) of any institute. WEI WANG was the most prolific author (33, 0.49%). RUI ZHANG ranked first for highest betweenness centrality (0.21) and collaboration criteria. Scientific Reports was found to be the most prolific journal (208, 3.18%), while PloS one had the most co-citations (2121, 1.55%). Strong and ongoing citation bursts were found for keywords such as "tissue microarray", "tissue segmentation", and "artificial neural network". Deep learning currently represents one of the most cutting-edge and applicable branches of AI in oncology. The literature to date has dealt extensively with radiomics, genomics, pathology, risk stratification, lesion detection, and therapy response. Current hot topics identified by our analysis highlight the potential application of AI in radiomics and precision oncology.

Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building

Artificial intelligence (AI) applications have become prevalent in all walks of life, but efforts to promote AI literacy to educated citizens from diverse study backgrounds are limited. There is a research gap in refocussing AI literacy courses from combining conceptual learning with mathematical formulae and programming codes to emphasising conceptual building from the beginning. This study fills the knowledge gap by evaluating AI literacy courses that aim to build conceptual understanding among university students from diverse study backgrounds. Eighty-two volunteers completed two AI literacy courses comprising 7 h of machine learning and 9 h of deep learning. The results of their pre- and post-course conceptual tests, surveys and self-reflective writing tasks indicated that the courses successfully equipped the participants with a conceptual understanding of AI. The participants felt empowered by the significant gains in their literacy and conceptual understanding of AI. The AI literacy courses successfully lower the barrier to entry for AI literacy and address a public need. The courses will be expanded to incorporate the development of AI applications together with discussion of ethical issues regarding the wide use of AI in society. This study can be used to guide future research on fostering AI literacy among educated citizens from diverse study backgrounds.

Radyasyon Onkolojisinde Yapay Zeka Kullanımı ve Türkiye’deki Uzmanların Yaklaşımının Araştırılması

Radiation Oncology is a treatment method that uses high technologies in cancer treatment and produces large amounts of medical images and data. GOAL: To investigate artificial intelligence (AI) based applications used in the Radiation Oncology workflow and to determine the ap-proaches, predictions, and expectations of experts (Radiation Oncologists and Medical Physics Specialists) in this field in Turkey. METHOD: An online questionnaire consisting of 10 questions was sent to a total of 51 people, consisting of Radi-ation Oncologists (27) and Medical Physicists (24) actively working in the field of radiation oncology in Turkey, and the results were analyzed. RESULTS: 11.76% of the experts par-ticipating in the research and working on this subject in Tur-key have no knowledge of AI and this group was excluded from the analysis. 88.98% of the participants agree that AI has beneficial applications in the field of medicine. While 46.67% of the participants do not think that AI applications can be used instead of their current work, 97.78% of the par-ticipants think that they can get support from AI applications in their transactions. The area where AI applications can be most beneficial is “Personalized or Adaptive therapy applica-tions” with 42.22%. The most important advantage of using AI has been identified as “It can produce meaningful results by processing large amounts of clinically meaningful data in real-time” with 55.56%. In case the decisions made by AI and the decisions made by individuals are different, 57.78% of the participants consider seeking a secondary expert opini-on. 52.27% of participants think that the person who applies the process should be considered responsible for the prob-lems that AI will create. The biggest obstacle in the deve-lopment of AI applications in Turkey is “the current human resources are closed to technological developments” by 40% of the participants.

Discovering the first US FDA-approved computer-aided polyp detection system.

Colorectal cancer is the third most common cancer worldwide. Because of the slow progression of the precancerous precursors, an efficient endoscopic surveillance strategy may be expected. It seems that around one-fourth of colorectal malignancies are still missed during colonoscopy. Several endoscopic technologies have been introduced, without radical changes. Interest in the development of artificial intelligence applications in the medical field has grown in the past decade. Artificial intelligence can help to highlight a specific region of interest that needs closer examination for the identification of polyps. The aim of this review is to report the first clinical experiences with the first US FDA-approved, real-time, deep-learning, computer-aided detection system (GI Genius™, Medtronic).

Yea or nay to artificial intelligence? More questions than answers under Malaysian copyright law

AbstractThe development of artificial intelligence draws heavily on training data which may be comprised of copyright works. Utilization of copyright works as such raises questions on copyright infringement as well as the possibilities of the usage being excused under the existing law. There are also concerns with respect to accessing copyright works in digital formats that are commonly protected by digital rights management tools. Restrictions on such usage may hamper the research and development of artificial intelligence applications. This paper examines the said issues with specific focus on first, the applicability of a seemingly Americanized fair dealing exception to copyright infringement under the Malaysian Copyright Act 1987; and second, the exceptions to legal protection of digital rights management. In doing so, the paper studies the existing models of the United States, United Kingdom, and the European Union. It ends with suggestions as to how copyright law may respond to the emerging needs and provide adequate latitude with the aim to promote research and development of artificial intelligence which would ultimately benefit the public at large.

CARJ 2021: Year in Review.

The past year has been one of unprecedented challenge for the modern world and especially the medical profession. This review explores some of the most impactful topics published in the CARJ during the COVID-19 pandemic including physician wellbeing and burnout, patient safety, and technological innovations including dual energy CT, quantitative imaging and ultra-high frequency ultrasound. The impact of the COVID-19 pandemic on trainee education is discussed and evidence-based tips for providing value-added care are reviewed. Patient privacy considerations relevant to the development of artificial intelligence applications for medical imaging are explored. These publications in the CARJ demonstrate that although this year has brought adversity, it has also been a harbinger for new and exciting areas of focus in our field.

The current state of artificial intelligence in cardiac transplantation.

The field of heart transplantation is a complex practice that combines both science and art to optimize the quality and quantity of an organ transplant recipient's life span. In the current age of Transplant Medicine there are many limiting factors that prevent potentially usable organs to be transplanted in addition to the many unknown factors in assessing the risk of posttransplant complications in a proactive manner. This review focuses on the current state, and potential use, and implementation of artificial intelligence technologies in the field of heart transplantation. Furthermore, the utilization of predictive algorithms to assess donor quality, graft function, posttransplant complications and prediction of high-risk complications will be discussed. Artificial intelligence technologies in the pretransplant population is also explored. Artificial intelligence process use has been increasing over the past decade. Early adoption in radiology and laboratory medicine have shown promise for future applications. Implementation of nascent technologies within the field of transplant medicine remains in its infancy. Cardiac and renal medicine have been recent focuses of large-scale artificial intelligence projects because of the wealth of data, the main limiting factor for producing accurate models. Understanding the true role of artificial intelligence in medicine is key - and has been divided into three areas of focus: data quality, interpretation, and clinical application. These areas allow the clinician to translate problems facing patients into algorithms utilized by data scientists to create solutions, which may provide in-depth analysis and detection of relationships not immediately clear. Although some published data has led to commercial products for cardiac, diabetic, and dermatologic applications -- widespread adoption remains limited to specialized centers. Artificial intelligence applications with clinically relevant models in transplant medicine have the potential to optimize organ utilization, prediction of complications, and potential pretransplant management, which may mitigate the need for transplant. Further translational projects are under development at major centers, with proof of concepts demonstrating validity and safety in the clinical setting. Limiting factors of infrastructure, expertise, and data availability continue to be addressed. Ongoing efforts for commercialization and large-scale trials will provide a foundation for the development of artificial intelligence applications in transplant medicine.