Potential Applications Of Artificial Intelligence Research Articles

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

AbstractOn‐skin sensors can precisely perceive important electrophysiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Despite significant advances in the development of soft materials as electrode sensors, data acquisition (DAQ) unit—another indispensable component of on‐skin electronic sensory systems—typically exhibits bulkiness or unimodal sensing, which is detrimental to the portability of the sensory system or the comprehensiveness of the perceived information. Here, a portable and multimodal DAQ unit to tackle these challenges is designed. By assembling the DAQ unit with low‐impedance (<100 Ω) laser‐induced graphene on‐skin electrode sensors, a wireless communication module, a power supply module, and a 3D printed protective shell, the completed sensory system can realize three‐in‐one monitoring of EEG, ECG, and EMG with a light weight of 22 g and a low cost of $25. Moreover, a mobile App is developed to display the perceived electrophysiological signals in real time. Human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence. The success of this inexpensive three‐in‐one portable electronic sensory system sheds light on design, fabrication, and commercialization of multifunctional wearable electronics with wide applications in fitness tracking, medical diagnostics, and human–machine interface.

State responsibility in relation to military applications of artificial intelligence

AbstractThis article explores the conditions and modalities under which a state can incur responsibility in relation to violations of international law involving military applications of artificial intelligence (AI) technologies. While the question of how to attribute and allocate responsibility for wrongful conduct is one of the central contemporary challenges of AI, the perspective of state responsibility under international law remains relatively underexplored. Moreover, most scholarly and policy debates have focused on questions raised by autonomous weapons systems (AWS), without paying significant attention to issues raised by other potential applications of AI in the military domain. This article provides a comprehensive analysis of state responsibility in relation to military AI. It discusses state responsibility for the wrongful use of AI-enabled military technologies and the question of attribution of conduct, as well as state responsibility prior to deployment, for failure to ensure compliance of AI systems with international law at the stages of development or acquisition. Further, it analyses derived state responsibility, which may arise in relation to the conduct of other states or private actors.

Artificial Intelligence in English Language Teaching: Navigating the Future with Emerging Perspectives

This research article examines the potential applications of Artificial Intelligence (AI) in English Language Teaching (ELT) in South Asia. AI is a rapidly developing technology that offers innovative and effective solutions to various educational challenges. The article explores how AI can be used to improve the teaching and learning of English as a second language in South Asian countries, which face significant language barriers due to a lack of adequate resources and training. The study highlights the various AI technologies that are currently available for ELT, such as speech recognition, machine learning, and natural language processing. It also analyses the potential benefits and challenges associated with the use of AI in ELT, including increased efficiency, personalized learning, and reduced teacher workload. The study concludes that AI has the potential to significantly improve the quality and accessibility of ELT in South Asia, and recommends further research and experimentation to fully realize its potential.

What Can Artificial Intelligence Do for Refugee Status Determination? A Proposal for Removing Subjective Fear

Abstract The drive for innovation, efficiency, and cost-effectiveness has seen governments increasingly turn to artificial intelligence (AI) to enhance their operations. The significant growth in the use of AI mechanisms in the areas of migration and border control makes the potential for its application to the process of refugee status determination (RSD), which is burdened by delay and heavy caseloads, a very real possibility. AI may have a role to play in supporting decision makers to assess the credibility of asylum seekers, as long as it is understood as a component of the humanitarian context. This article argues that AI will only benefit refugees if it does not replicate the problems of the current system. Credibility assessments, a central element of RSD, are flawed because the bipartite standard of a ‘well-founded fear of being persecuted’ involves consideration of a claimant’s subjective fearfulness and the objective validation of that fear. Subjective fear imposes an additional burden on the refugee, and the ‘objective’ language of credibility indicators does not prevent the challenges decision makers face in assessing the credibility of other humans when external, but largely unseen, factors such as memory, trauma, and bias, are present. Viewing the use of AI in RSD as part of the digital transformation of the refugee regime forces us to consider how it may affect decision-making efficiencies, as well as its impact(s) on refugees. Assessments of harm and benefit cannot be disentangled from the challenges AI is being tasked to address. Through an analysis of algorithmic decision making, predictive analysis, biometrics, automated credibility assessments, and digital forensics, this article reveals the risks and opportunities involved in the application of AI in RSD. On the one hand, AI’s potential to produce greater standardization, to mine and parse large amounts of data, and to address bias, holds significant possibility for increased consistency, improved fact-finding, and corroboration. On the other hand, machines may end up replicating and manifesting the unconscious biases and assumptions of their human developers, and AI has a limited ability to read emotions and process impacts on memory. The prospective nature of a well-founded fear is counter-intuitive if algorithms learn based on training data that is historical, and an increased ability to corroborate facts may shift the burden of proof to the asylum seeker. Breaches of data protection regulations and human rights loom large. The potential application of AI to RSD reveals flaws in refugee credibility assessments that stem from the need to assess subjective fear. If the use of AI in RSD is to become an effective and ethical form of humanitarian tech, the ‘well-founded fear of being persecuted’ standard should be based on objective risk only.

Estimating sewage flow rate in Jefferson County, Kentucky, using machine learning for wastewater-based epidemiology applications

Abstract Direct measurement of the flow rate in sanitary sewer lines is not always feasible and is an important parameter for the normalization of data used in wastewater-based epidemiology applications. Machine learning to estimate past wastewater influent flow rates supporting public health applications has not been studied. The aim of this study was to assess wastewater treatment plant influent flow rates when compared with weather data and to retrospectively estimate flow rates in Louisville, Kentucky (USA), based on other data-types using machine learning. A random forest model was trained using a range of variables, such as feces-related indicators, weather data that could be associated with dilution in sewage systems, and area demographics. The developed algorithm successfully estimated the flow rate with an accuracy of 91.7%, although it did not perform as well with short-term (one-day) high flow rates. This study suggests that using variables such as precipitation (mm/day) and population size are more important for wastewater flow estimation. The fecal indicator concentration (cross-assembly phage and pepper mild mottle virus) was less important. Our study challenges currently accepted opinions by showing the important public health potential application of artificial intelligence in wastewater treatment plant flow rate estimation for wastewater-based epidemiological applications.

Understanding the potential applications of Artificial Intelligence in Agriculture Sector

Artificial Intelligence (AI) has been extensively applied in farming recently. To cultivate healthier crops, manage pests, monitor soil and growing conditions, analyse data for farmers, and enhance other management activities of the food supply chain, the agriculture sector is turning to AI technology. It makes it challenging for farmers to choose the ideal time to plant seeds. AI helps farmers choose the optimum seed for a particular weather scenario. It also offers data on weather forecasts. AI-powered solutions will help farmers produce more with fewer resources, increase crop quality, and hasten product time to reach the market. AI aids in understanding soil qualities. AI helps farmers by suggesting the nutrients they should apply to increase the quality of the soil. AI can help farmers choose the optimal time to plant their seeds. Intelligent equipment can calculate the spacing between seeds and the maximum planting depth. An AI-powered system known as a health monitoring system provides farmers with information on the health of their crops and the nutrients that need to be given to enhance yield quality and quantity. This study identifies and analyses relevant articles on AI for Agriculture. Using AI, farmers can now access advanced data and analytics tools that will foster better farming, improve efficiencies, and reduce waste in biofuel and food production while minimising the negative environmental impacts. AI and Machine Learning (ML) have transformed various industries, and the AI wave has now reached the agriculture sector. Companies are developing several technologies to make monitoring farmers' crop and soil health easier. Hyperspectral imaging and 3D laser scanning are the leading AI-based technologies that can help ensure crop health. These AI-powered technologies collect precise data on the health of the crops in greater volume for analysis. This paper studied AI and its need in Agriculture. The process of AI in Agriculture and some Agriculture parameters monitored by AI are briefed. Finally, we identified and discussed the significant applications of AI in agriculture.

A Deep Reinforcement Learning Quality Optimization Framework for Multimedia Streaming over 5G Networks

Media applications are amongst the most demanding services. They require high amounts of network capacity as well as computational resources for synchronous high-quality audio–visual streaming. Recent technological advances in the domain of new generation networks, specifically network virtualization and Multiaccess Edge Computing (MEC) have unlocked the potential of the media industry. They enable high-quality media services through dynamic and efficient resource allocation taking advantage of the flexibility of the layered architecture offered by 5G. The presented work demonstrates the potential application of Artificial Intelligence (AI) capabilities for multimedia services deployment. The goal was targeted to optimize the Quality of Experience (QoE) of real-time video using dynamic predictions by means of Deep Reinforcement Learning (DRL) algorithms. Specifically, it contains the initial design and test of a self-optimized cloud streaming proof-of-concept. The environment is implemented through a virtualized end-to-end architecture for multimedia transmission, capable of adapting streaming bitrate based on a set of actions. A prediction algorithm is trained through different state conditions (QoE, bitrate, encoding quality, and RAM usage) that serves the optimizer as the encoding values of the environment for action prediction. Optimization is applied by selecting the most suitable option from a set of actions. These consist of a collection of predefined network profiles with associated bitrates, which are validated by a list of reward functions. The optimizer is built employing the most prominent algorithms in the DRL family, with the use of two Neural Networks (NN), named Advantage Actor–Critic (A2C). As a result of its application, the ratio of good quality video segments increased from 65% to 90%. Furthermore, the number of image artifacts is reduced compared to standard sessions without applying intelligent optimization. From these achievements, the global QoE obtained is clearly better. These results, based on a simulated scenario, increase the interest in further research on the potential of applying intelligence to enhance the provisioning of media services under real conditions.

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.

AsthmaKGxE: An asthma–environment interaction knowledge graph leveraging public databases and scientific literature

Motivation: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathophysiology and etiology of asthma. In this paper, we propose an asthma knowledge graph (KG) built using a hybrid methodology for graph-based modeling of asthma complexity with a focus on environmental interactions. Using a heterogeneous set of public sources, we construct a genetic and pharmacogenetic asthma knowledge graph. The construction of this KG allowed us to shed more light on the lack of curated resources focused on environmental influences related to asthma. To remedy the lack of environmental data in our KG, we exploit the biomedical literature using state-of-the-art natural language processing and construct the first Asthma–Environment interaction catalog incorporating a continuously updated ensemble of environmental, psychological, nutritional and socio-economic influences. The catalog’s most substantiated results are then integrated into the KG. Results:The resulting environmentally rich knowledge graph ”AsthmaKGxE” aims to provide a resource for several potential applications of artificial intelligence and allows for a multi-perspective study of asthma. Our insight extraction results indicate that stress is the most frequent asthma association in the corpus, followed by allergens and obesity. We contend that studying asthma–environment interactions in more depth holds the key to curbing the complexity and heterogeneity of asthma. Availability:A user interface to browse and download the extracted catalog as well as the KG are available at http://asthmakgxe.moreair.info/. The code and supplementary data are available on github (https://github.com/ChaiAsaad/MoreAIRAsthmaKGxE).

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

A Primer into the Current State of Artificial Intelligence in Gastroenterology.

Artificial intelligence (AI) is not a new idea or field of research. However, recent advancements in computing technology as well as increasing worldwide experience in applying AI to various fields have enabled us to hope that applying it to the medical practice could improve outcomes. The aim of the article was to evaluate potential applications of artificial intelligence (AI) in the clinical practice of gastroenterology. AI algorithms can offer many benefits over the traditional diagnostic, prognostic, therapeutic scores, or non-AI enhanced procedures that we perform today. It can very easily process complex mathematical data, which would allow to account for many parameters and with highly sophisticated formulas to determine a conclusion, to a degree that would be impractical or impossible for a human to do alone in the clinical practice. This could allow for significantly more individualized treatments for each patient. As more data becomes available, it can be fed into the algorithm, which allows it to change based on the new data. However, we must be aware that most studies performed today are still early retrospective studies, and their utility still needs to be evaluated in future large scale prospective studies. Apart from the medical challenges, it must be noted that the impact of large-scale implementation of artificial intelligence will have challenges related to ethical and financial aspects as well.

Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview.

BackgroundInnovative technologies, such as machine learning, big data, and artificial intelligence (AI) are approaches adopted for personalized medicine, and psychological interventions and diagnosis are facing huge paradigm shifts. In this literature review, we aim to highlight potential applications of AI on psychological interventions and diagnosis.MethodsThis literature review manifest studies that discuss how innovative technology as deep learning (DL) and AI is affecting psychological assessment and psychotherapy, we performed a search on PUBMED, and Web of Science using the terms “psychological interventions,” “diagnosis on mental health disorders,” “artificial intelligence,” and “deep learning.” Only studies considering patients' datasets are considered.ResultsNine studies met the inclusion criteria. Beneficial effects on clinical symptoms or prediction were shown in these studies, but future study is needed to determine the long-term effects.LimitationsThe major limitation for the current study is the small sample size, and lies in the lack of long-term follow-up-controlled studies for a certain symptom.ConclusionsAI such as DL applications showed promising results on clinical practice, which could lead to profound impact on personalized medicine for mental health conditions. Future studies can improve furthermore by increasing sample sizes and focusing on ethical approvals and adherence for online-therapy.

Artificial intelligence as a pioneering step in school institutions

In this paper, we deal with the potential applications of Artificial Intelligence in educational institutions. One of the main goals of applying the mentioned technology is to make the education system more efficient if possible. Based on research in this area in the world, our impression is that there is a long-term plan for introducing AI in schools, as a new kind of learning standard. The idea is not for artificial intelligence to completely take over the domain of education, but for some processes to be better organized or improved, so that the result of learning would be of better quality. New technologies, in the form of modern learning platforms that incorporate AI as one of the most important components of the system, significantly improve the communication and research work of students. It is evident that new generations are coming whose experience will be significantly different from ours, and that the new technologies from the domain of AI that we consider in this paper will significantly affect the design processes in the future.

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Outracing champion Gran Turismo drivers with deep reinforcement learning.

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits1. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. Here we describe how we trained agents for Gran Turismo that can compete with the world's best e-sports drivers. We combine state-of-the-art, model-free, deep reinforcement learning algorithms with mixed-scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing's important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world's best Gran Turismo drivers. By describing how we trained championship-level racers, we demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.

Application of Noninvasive Imaging to Combined Immune Checkpoint Inhibitors for Breast Cancer: Facts and Future.

With the application of mono-immunotherapy in cancer, particularly immune checkpoint inhibitors, improved outcomes have been achieved. However, there are several limitations to immunotherapy, such as a poor response to the drugs, immune resistance, and immune-related adverse events. In recent years, studies of preclinical animal models and clinical trials have demonstrated that immune checkpoint inhibitors for breast cancer can significantly prolong the overall survival and quality of patients' lives. Meanwhile, combined immune checkpoint inhibitor treatment has attracted researchers' attention and showed great potential in the comprehensive treatment of breast cancer patients. Additionally, noninvasive imaging enables physicians to predict response to combined immunotherapeutic drugs, achieve treatment efficacy, and lead to better clinical management. Herein, we review the background of combined immune checkpoint inhibitor therapy and summarize its targeted imaging as well as progress in noninvasive imaging aimed at evaluating therapeutic outcomes. Finally, we describe several factors that may influence the outcome of this combined immunotherapy, the future direction of medical imaging, and the potential application of artificial intelligence in breast cancer. With further development of noninvasive imaging for the guidance of combined immune checkpoint inhibitors, cures for this disease may be achieved.

The application of artificial intelligence in nuclear cardiology.

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated a lot of interest in medical imaging research including nuclear cardiology. AI has a potential to reduce cost, save time and improve image acquisition, interpretation, and decision-making. This review summarizes recent researches and potential applications of AI in nuclear cardiology and discusses the pitfall of AI.

Role of artificial intelligence in MS clinical practice

Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches.

The potential applications of artificial intelligence in drug discovery and development.

Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.