Artificial Intelligence Revolution Research Articles

Competencies for the Use of Artificial Intelligence in Primary Care.

The artificial intelligence (AI) revolution has arrived for the health care sector and is finally penetrating the far-reaching but perpetually underfinanced primary care platform. While AI has the potential to facilitate the achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician well-being), inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities. The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians; therefore, appropriate medical education and training will be crucial to maximize potential benefits and minimize harms. To facilitate this training, we propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the "side effects" of this tool?). Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape. Nonetheless, even incremental increases in AI-relevant training may be beneficial, and the sooner these challenges are tackled, the sooner the primary care workforce and those served by it will begin to reap the benefits.

Deep Learning Approaches in Histopathology.

The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers' routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems.

Artificial intelligence in cardiology: Hope for the future and power for the present.

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

With the arrival of the third revolution of artificial intelligence, the applications of artificial intelligence in the fields of automatic driving, image recognition, smart home, machine translation, medical services, e-sports, and so on can be seen everywhere, and topics about artificial intelligence are constantly emerging. Since 2017, the discussion on artificial intelligence in the field of law has become more and more active. In this context, the application of artificial intelligence in the field of legal judgment and the hypothetical system based on this technology in court judgment has also become the object of discussion from time to time. In this paper, based on the artificial intelligence decision-making method of the deep neural network, aiming at the three subtasks of legal judgment prediction, namely, crime prediction, law recommendation, and sentence prediction, a multi-task judgment prediction model BERT12multi and a sentence interval prediction model BERT-Text CNN are proposed, which improve the prediction accuracy and adopt the knowledge distillation strategy to compress the model parameters and improve the reasoning speed of the judgment model. Experiments on the CAIL2018 data set show that the performance of the deep neural network model in crime prediction and law recommendation tasks can be significantly improved by adopting the pre training model adaptive training, grouping focus loss, and gradient confrontation training strategies. Using a step-by-step sentence prediction strategy can realize the weight sharing of pre training model and make use of the prediction results of charges and laws in sentence prediction. The recall training-prediction strategy can avoid error accumulation and improve the accuracy of sentence prediction. By integrating the artificial intelligence decision-making method, the case reasoning speed can be greatly improved, the highest compressible model volume can be about 11% of the original one, and the reasoning speed can be increased by about 8 times. At the same time, performance close to that of the deep neural model can be obtained, which is superior to other legal decision prediction models based on word embedding.

Novel complex-valued deep learning applied to automatic classification of heart sounds

Abstract Introduction Cardiovascular disease (CVD) is a problem facing governments around the world. Early detection saves the healthcare system thousands of dollars by allowing prevention and avoiding costly treatments when the disease has progressed to advanced stages. According to the World Health Organization (WHO), cardiovascular diseases represent the leading cause of mortality worldwide (WHO, 2019). That is why, we have decided to develop novel artificial intelligence models for the detection of heart disease, with accuracies that allow us to put the obtained algorithms into production. The revolution of artificial intelligence allows the application Deep Learning techniques of two-dimensional images in the domain of both real numbers and complex value numbers, as classifiers of heart sounds, for the detection of normality or abnormality in the functioning of the heart. Purpose In the present work, we propose the comparison of a novel 2D convolutional neural network (CNN) algorithm in the domain of complex value numbers with its counterpart in the domain of real numbers for the automatic classification of heart sounds into normal or abnormal. Material The database we decided to use for our research is Pascal, which has audio files of cardiac activity, distributed in 351 normal sounds and 129 pathological sounds. Methods The following steps were applied to get the objectives of our work: 1) automatic segmentation of a single heartbeat, 2) conversion of the segmented sound into its associated image scalogram using the Hilbert transform, 3) classification of the sounds into normal and abnormal using the proposed algorithms, and 4) measurement and comparison of the results obtained by performing a two-tailed t-student hypothesis test and cross-validation. Results We present a comparative table between the two proposed models, finding that Accuracy, F1 Score, Precision and Recall metrics obtained using complex-valued convolution networks present significant improvements compared with the real valued one. The following table show us the obtained numbers. For all cases, the t-student test shows us p-values less than 0.05%, giving statistical evidence that the means are significantly different between the two proposed models. Besides, in all cases, the performance of the Complex-valued model is better compared with the Real-valued one. Conclusion Complex-valued neural networks propose a significant advance around Deep learning, since they present a better performance than the traditional counterparts based on real numbers. This proposes an experimental basis for the construction of a new Deep learning paradigm, where information in another numerical domain, is better exploited with the help of mathematical transforms. The latter is a significant advance in health sciences, where the demand is higher, in terms of performance of the proposed models. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): eVIDA research Group

Artificial intelligence revolution in structure prediction for entire proteomes

Artificial intelligence revolution in structure prediction for entire proteomes

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Response to comments on: Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence

Dear Editor, We thank the authors, Gurnani et al., for reading our published work on “Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence” and stressing how data annotators are the unacclaimed heroes of the artificial intelligence (AI) revolution in ophthalmology.[12] We do agree with Gurnani et al. and would like to add the following important points on how data annotation is a game changer in ophthalmic AI and warrants further exploration. “Big data” has become the buzzword of the decade and has led to breakthroughs for major technology tycoons like Google. But unlike the technology giants, most ophthalmic set-ups are limited to “small data”. And for creating a successful ophthalmic AI model, it is imperative to have a huge dataset.[3] Small data makes the data comprehensible for humans and can be easily stored on local servers and laptops, whereas big data has an insane amount of structured and unstructured data that cannot be analyzed using conventional processing methods, which requires complex algorithms.[4] Considering how data annotation is an important armament in creating the AI algorithm, the majority of the ophthalmic practitioners, irrespective of the magnitude of the set-up, can make massive progress with small data as it makes it more feasible for annotations.[5] Moreover, there are ways in which you can deliver small data in a big way, by annotating every single clinical finding intricately, such as splinter hemorrhage, laminar dot sign, bayoneting sign, etc., for AI diagnostics. Also, certain techniques such as transfer learning and collective learning have helped transform small data into big data. Transfer learning encompasses the transfer of knowledge via open-sourced machine learning models from one domain to another domain with fewer data. Collective learning is the process by which multiple individual companies with small data, use a third-party AI and consolidate their data to create a sophisticated AI model.[346] To summarize, the lack of big data should not be a deterrent to venture into ophthalmic AI, and it is time to wield the tools at hand to create a successful AI model in ophthalmology with customized annotations. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Fifth Industrial Revolution

While we are rushing towards the future quickly and without stopping, there is a debate in the scientific community and business forums today about the upcoming industrial revolution and what it is related to, is it the revolution of artificial intelligence, or the revolution of nanotechnology, or the revolution of sustainability, or is it the revolution of the Internet of things, cloud computing and 3D printing? We believe that the Fifth Industrial Revolution will be the result of all these technologies. And through our research paper, we will address the concept of the industrial revolution in general and the major industrial revolutions in human history and their historical context. Then we will look at the Fourth Industrial Revolution, whose events we are living today, its most important technologies, and its future development. Then we will look at the future features of the upcoming Fifth Industrial Revolution and the controversy surrounding it from the point of view of several experts and the most important opportunities and challenges that are likely to emerge

Growth trends for selected occupations considered at risk from automation

Breakthroughs in artificial intelligence (AI) and robotics have led to substantial concern that large-scale job losses are imminent. Selected occupations are often cited as illustrations of technological displacement that is or will become a more general problem, but these discussions are often impressionistic. This article compiles a list of specific occupations cited in the automation literature and examines the occupations’ employment trends since 1999 and projected employment to 2029. There is little support in U.S. Bureau of Labor Statistics data or projections for the idea of a general acceleration of job loss or a structural break with trends pre-dating the AI revolution with respect to the occupations cited as examples. Offsetting factors and other limitations of the automation thesis are discussed.

Diagnostic and therapeutic approach of artificial intelligence in neuro-oncological diseases

Neuro-oncological diseases are rare and their fatality rate is increased in patients due to advance disease development despite of the recent outcomes on neuro-oncological therapies. Artificial intelligence (AI) approaches and the exponential expansion of computing algorithms are set to increase the precision of diagnostic and therapeutic approaches in medicine. Medical imaging is one of the common AI applications where it assists radiologists in diagnosis. Radiomics has been successfully applied in neuro-oncology and it will be at forefront of AI revolution. Various AI methods can define numerous infiltrating margins of neuro-oncological diseases and it differentiates pseudo-progression from real progression and envisage recurrence and survival better than the methods used in routine practice. The present review deliberates the common neuro-oncological diseases such as glioblastoma, meningioma, spinal cord tumor and neurofibroma (NF1) and its AI algorithms related to imaging techniques such as computed (MRI) and computed tomography (CT). Also, we have discussed the beneficial aspect of AI and recent trends in diagnosis. From the study, the management of neuro-oncological diseases using AI can be revolutionized and the need of omics analysis is essential in future.

Artificial Intelligence-Based Translation Technology in Translation Teaching.

With the advent of the era of artificial intelligence revolution, deep learning is undoubtedly injecting new vitality into machine translation technology. In the context of artificial intelligence, teachers must find new and effective teaching ideas to improve the actual effect of translation teaching, which has drawn the attention of university teachers in recent years. With the development of society and the change of the times, English is becoming one of the implicit requirements of society for talents. Not only in the context of the new changes in curriculum, English teaching in schools and teachers is no longer limited to examinations, but more importantly, it is also significant to cultivate students' comprehensive English ability so that they can really use their English knowledge and solve practical problems in life. On this basis, this paper investigates the practice of teaching English in college in the context of artificial intelligence.

The Ethical and Societal Considerations for the Rise of Artificial Intelligence and Big Data in Ophthalmology.

Medical specialties with access to a large amount of imaging data, such as ophthalmology, have been at the forefront of the artificial intelligence (AI) revolution in medicine, driven by deep learning (DL) and big data. With the rise of AI and big data, there has also been increasing concern on the issues of bias and privacy, which can be partially addressed by low-shot learning, generative DL, federated learning and a “model-to-data” approach, as demonstrated by various groups of investigators in ophthalmology. However, to adequately tackle the ethical and societal challenges associated with the rise of AI in ophthalmology, a more comprehensive approach is preferable. Specifically, AI should be viewed as sociotechnical, meaning this technology shapes, and is shaped by social phenomena.

Knowledge and attitude of artificial intelligence (AI) technology among clinical researchers in the Kingdom of Saudi Arabia

Background: Artificial intelligence (AI) has grown in popularity over the last few decades, and its application in clinical trials is on the rise around the world. Despite Sophie, the world's first humanoid robot to be awarded personality and citizenship by Saudi Arabia, and AI Arabic bots designed to improve customer service, the Saudi region is yet to completely embrace the AI Revolution. Over the last few years, the clinical usage of AI systems in medicine has grown in popularity. Several surgical AI platforms have been tested and found to improve preoperative diagnostic and decision making, outcome prediction, surgical planning, and intraoperative surgical guidance. This study examines clinical researchers' knowledge and attitudes toward Artificial Intelligence (AI) technology in Saudi Arabia. Objective: To find out knowledge about artificial intelligence among clinical researchers of Saudi Arabia and their attitude towards the same. Materials and methods: This study was carried out utilising an online questionnaire-based survey that was created with Google forms and emailed to clinical researchers in the friend circle. A total of 118 people replied to the survey, and the sample population was chosen using a convenience sampling technique. They were asked about their demographics, knowledge of AI, and attitudes toward it.

Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms

Industrial robots are a key enabling technology of Industry 4.0 and the artificial intelligence revolution, which is of great significance in sustainable development. Existing research has focused on the economic effects of industrial robot application, but research on the environmental effects remains insufficient. Based on the environmental Kuznets curve (EKC) model and using sample data from 35 countries from 1993 to 2017, this paper empirically examines the carbon emission reduction effects of industrial robot application. Three key findings emerged. First, the application of industrial robots significantly reduces carbon intensity. The application of industrial robots leads to increased productivity, the optimisation of factor structures, and technological innovation in production, which improve energy efficiency and reduce carbon intensity. Second, there is a two-dimensional heterogeneity in the carbon intensity reduction effects of industrial robot application in terms of the application fields and possible countries for application. Compared to other fields, the application of industrial robots in manufacturing, agriculture, and electricity, gas, and water supply fields significantly promotes carbon intensity reduction. Furthermore, industrial robots in developed countries have better emission reduction effects than in developing countries. Third, the application of industrial robots has a dual-channel mediating mechanism for carbon intensity reduction: first, there is a mediating role of green total factor productivity and energy intensity; second, absorptive capacity plays a moderating role. On the one hand, high absorptive capacity brings about a better innovation environment and enhances the effects of carbon emission reduction; on the other hand, the application of industrial robots promotes carbon intensity reduction by positively influencing the improvement of green total factor productivity and energy intensity. Finally, policy recommendations are provided based on the results.

You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly.

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Guest Editorial Revolution of AI and Machine Learning With Processing-in-Memory (PIM): From Systems, Architectures, to Circuits

This Special Issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to demonstrating the latest research progress on the revolution of artificial intelligence (AI) and machine learning (ML) with processing-in-memory (PIM): from systems, architectures, to circuits.

Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles

We are currently experiencing a revolution in data production and artificial intelligence (AI) applications. Data are produced much faster than they can be consumed. Thus, there is an urgent need to develop AI algorithms for all aspects of modern life. Furthermore, the medical field is a fertile field in which to apply AI techniques. Breast cancer is one of the most common cancers and a leading cause of death around the world. Early detection is critical to treating the disease effectively. Breast density plays a significant role in determining the likelihood and risk of breast cancer. Breast density describes the amount of fibrous and glandular tissue compared with the amount of fatty tissue in the breast. Breast density is categorized using a system called the ACR BI-RADS. The ACR assigns breast density to one of four classes. In class A, breasts are almost entirely fatty. In class B, scattered areas of fibroglandular density appear in the breasts. In class C, the breasts are heterogeneously dense. In class D, the breasts are extremely dense. This paper applies pre-trained Convolutional Neural Network (CNN) on a local mammogram dataset to classify breast density. Several transfer learning models were tested on a dataset consisting of more than 800 mammogram screenings from King Abdulaziz Medical City (KAMC). Inception V3, EfficientNet 2B0, and Xception gave the highest accuracy for both four- and two-class classification. To enhance the accuracy of density classification, we applied weighted average ensembles, and performance was visibly improved. The overall accuracy of ACR classification with weighted average ensembles was 78.11%.

SPIE Computer-Aided Diagnosis conference anniversary review.

The SPIE Computer-Aided Diagnosis conference has been held for 16 consecutive years at the annual SPIE Medical Imaging symposium. The conference remains vibrant, with a core group of submitters as well as new submitters and attendees each year. Recent developments include a marked shift in submissions relating to the artificial intelligence revolution in medical image analysis. This review describes the topics and trends observed in research presented at the Computer-Aided Diagnosis conference as part of the 50th-anniversary celebration of SPIE Medical Imaging.