Future Development Of Artificial Intelligence Research Articles

Artificial Intelligence – Multi-directional Past, Turbulent Present, Ambiguous Future

In this article, we will try to answer the question “What is the future development of artificial intelligence (AI)?”. We will look at AI’s multi-directional past, turbulent present and attempt to indicate its ambiguous future. We will reveal how the idea of machines with intelligent behavior arose and who the pioneers of the field of informatics called Artificial Intelligence are; what the main methods of creating and training computer systems with artificial intelligence are; what applications AI has in the economy and social activities. We will try to classify different types of machine learning of artificial intelligence systems. We will look for the main problems and risks of using AI and finally see the prospects in its development. Artificial intelligence must be developed carefully, taking into account all influences – positive and negative, on mankind.

Artificial intelligence tools for optimising recruitment and retention in clinical trials: a scoping review protocol

IntroductionIn recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought about significant improvements in the efficiency and cost reduction of clinical...

Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system

BackgroundStaining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.MethodsThe stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.ResultsStain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98–0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.ConclusionThe proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.

The Future of Research in an Artificial Intelligence-Driven World

Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future.

The Application of Artificial Intelligence in Medical Diagnostics: A New Frontier

This study reviews the latest progress in the application of artificial intelligence (AI) in the medical field, focusing on the application of AI technology in clinical diagnosis, medical equipment management, dentistry, ophthalmology and surgical care. By analyzing multiple academic documents, this article deeply explores the role of AI in improving the accuracy of medical diagnosis, the efficiency of clinical skills training, and medical equipment management. In particular, the literature review points out the efficient performance of convolutional neural networks (CNN) in processing medical images, and its potential applications in periodontics and ophthalmology diagnosis. At the same time, this article also discusses the ethical and legal challenges faced by AI in the medical field, as well as potential directions for future development. These findings not only reveal the huge potential of AI technology in the medical field, but also highlight the ethical and governance issues that need to be considered when promoting these technologies. Overall, this review provides a comprehensive perspective for understanding the current applications and future development of AI in the medical field.

Artificial Intelligence in Scoliosis: Current Applications and Future Directions

Scoliosis is a three-dimensional deformity of lateral bending and rotation of the spine. Artificial intelligence (AI) is a set of theories and techniques for studying artificial intelligence, which realizes machine intelligence by simulating and expanding human intelligence. With the continuous development of the multidisciplinary integration trend of modern medicine, artificial intelligence is used more and more in the diagnosis and treatment of scoliosis. Artificial intelligence has been widely used in the study of scoliosis and has penetrated into all fields of clinical practice of scoliosis. At present, artificial intelligence has shown good application prospects in early screening, diagnosis, treatment decision making, intraoperative operation, and prognosis prediction of scoliosis. This paper mainly summarizes the application of artificial intelligence in the clinical practice of scoliosis, and briefly introduces the AI model and its specific applications. In addition, this paper also discusses the limitations and future development of artificial intelligence. In the future, artificial intelligence will bring greater convenience to the diagnosis and treatment of scoliosis and provide better therapeutic effects for patients.

The Study of an Adaptive Bread Maker Using Machine Learning.

Bread is one of the most consumed foods in the world, and modern food processing technologies using artificial intelligence are crucial in providing quality control and optimization of food products. An integrated solution of sensor data and machine learning technology was determined to be appropriate for identifying real-time changing environmental variables and various influences in the baking process. In this study, the Baking Process Prediction Model (BPPM) created by data-based machine learning showed excellent performance in monitoring and analyzing real-time sensor and vision data in the baking process to predict the baking stages by itself. It also has the advantage of improving the quality of bread. The volumes of bread made using BPPM were 127.54 ± 2.54, 413.49 ± 2.59, 679.96 ± 1.90, 875.79 ± 2.46, and 1260.70 ± 3.13, respectively, which were relatively larger than those made with fixed baking time (p < 0.05). The developed system is evaluated to have great potential to improve precision and efficiency in the food production and processing industry. This study is expected to lay the foundation for the future development of artificial intelligence and the food industry.

Risk Prediction Models for Peri-Operative Mortality in Patients Undergoing Major Vascular Surgery with Particular Focus on Ruptured Abdominal Aortic Aneurysms: A Scoping Review.

The present scoping review aims to describe and analyze available clinical data on the most commonly reported risk prediction indices in vascular surgery for perioperative mortality, with a particular focus on ruptured abdominal aortic aneurysm (rAAA). A scoping review following the PRISMA Protocols Extension for Scoping Reviews was performed. Available full-text studies published in English in PubMed, Cochrane and EMBASE databases (last queried, 30 March 2023) were systematically reviewed and analyzed. The Population, Intervention, Comparison, Outcome (PICO) framework used to construct the search strings was the following: in patients with aortic pathologies, in particular rAAA (population), undergoing open or endovascular surgery (intervention), what different risk prediction models exist (comparison), and how well do they predict post-operative mortality (outcomes)? The literature search and screening of all relevant abstracts revealed a total of 56 studies in the final qualitative synthesis. The main findings of the scoping review, grouped by the risk score that was investigated in the original studies, were synthetized without performing any formal meta-analysis. A total of nine risk scores for major vascular surgery or elective AAA, and 10 scores focusing on rAAA, were identified. Whilst there were several validation studies suggesting that most risk scores performed adequately in the setting of rAAA, none reached 100% accuracy. The Glasgow aneurysm score, ERAS and Vancouver score risk scores were more frequently included in validation studies and were more often used in secondary studies. Unfortunately, the published literature presents a heterogenicity of results in the validation studies comparing the different risk scores. To date, no risk score has been endorsed by any of the vascular surgery societies. The use of risk scores in any complex surgery can have multiple advantages, especially when dealing with emergent cases, since they can inform perioperative decision making, patient and family discussions, and post hoc case-mix adjustments. Although a variety of different rAAA risk prediction tools have been published to date, none are superior to others based on this review. The heterogeneity of the variables used in the different scores impairs comparative analysis which represents a major limitation to understanding which risk score may be the "best" in contemporary practice. Future developments in artificial intelligence may further assist surgical decision making in predicting post-operative adverse events.

Current state and future development of artificial intelligence in marketing

Artificial intelligence (AI) has emerged as a trend in various industries. In the marketing field, artificial intelligence has been the main driver of its growth and an important tool for companies, organisations, and marketing specialists. However, there is a gap between the existing AI research and the actual application of the technologies in marketing. To bridge the gap, this study focuses on the current state of research on artificial intelligence in marketing and AI technologies used in marketing. Research articles on this topic are searched from the ScienceDirect database and the IEEE Xplore database and the non-empirical studies are filtered out by selection criteria. Then the selected articles are analysed using the method of bibliometrics and content analysis. This study describes the landscape of research in artificial intelligence in marketing, summarises AI technologies applied in the marketing field, provides advice on applying AI applications to marketing for marketing specialists, and presents future research directions for AI specialists. Results show that the application of AI in marketing will increase fast in the coming years. In particular, companies and organisations may greatly benefit from the use of AI technology in marketing to increase sales, cut costs, improve firm productivity, boost innovation, and achieve organisational goals.

종합적 사고경향, 지각된 유용성, 인공지능 윤리적 위험인식이 인공지능 기술의 태도와 사용 의도에 미치는 영향

This study verifies AI attitudes and usage intentions through three levels of thinking tendency, usefulness, and AI ethical risk perception. The results show that the three variables have a positive impact on AI attitudes, and AI ethical risk perception has a positive impact on use intention. And confirmed the mediation effect of attitude. Provide theoretical and practical significance for the future development of artificial intelligence.

Regulation of artificial intelligence in medicine

A study on the regulation of artificial intelligence (AI) in healthcare, includes a brief overview of the current state of use of AI in healthcare and its potential benefits and risks. The article summarizes the current regulations that exist for AI in healthcare, including any relevant laws, guidelines, and best practices, including information on regulatory bodies such as the FDA and HIPAA. The ethical considerations arising from the use of AI in healthcare, such as patient confidentiality and data security, bias in algorithms, and transparency in decision making, are given. Examples of AI in healthcare are given that illustrate the challenges and opportunities provided by the technology, including both successful and unsuccessful implementations. Future developments in AI and healthcare are described, including emerging technologies and trends, and predictions of how rules might evolve in response to these developments. Summarize and provide recommendations for addressing regulatory challenges related to AI in healthcare.

Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence. Considering the importance of swarm intelligence for the future development of artificial intelligence, we discuss and analyze swarm intelligence from a broader and deeper perspective. In a broader sense, we are talking about not only bio-inspired swarm intelligence, but also human-machine hybrid swarm intelligence. In a deeper sense, we discuss the research using a three-layer hierarchy: in the first layer, we divide the research of swarm intelligence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence; in the second layer, the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence; and in the third layer, we review single-population, multi-population and human-machine hybrid models from different perspectives. Single-population swarm intelligence is inspired by biological intelligence. To further solve complex optimization problems, researchers have made preliminary explorations in multi-population swarm intelligence. However, it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive intelligent behavior that meets the needs of human cognition. Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence. In addition to single-population swarm intelligence, we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper. We also discuss the applications of swarm intelligence in optimization, big data analysis, unmanned systems and other fields. Finally, we discuss future research directions and key issues to be studied in swarm intelligence.

Recognition of Postoperative Cystography Features by Artificial Intelligence to Predict Recovery from Postprostatectomy Urinary Incontinence: A Rapid and Easy Way to Predict Functional Outcome.

Post-operative cystography has been used to predict the recovery of postprostatectomy urinary incontinence (PPI) in patients with localized prostate cancer. This study aimed to validate the predictive value of cystography for PPI and utilize a deep learning model to identify favorable and unfavorable features. Medical records and cystography images of patients who underwent robotic-assisted radical prostatectomy for localized prostate cancer were retrospectively reviewed. Specific cystography features, including anastomosis leakage, a downward bladder neck (BN), and the bladder neck angle, were analyzed for the prediction of PPI recovery. Favorable and unfavorable patterns were categorized based on the three cystography features. The deep learning model used for transfer learning was ResNet 50 and weights were trained on ImageNet. We used 5-fold cross-validation to reduce bias. After each fold, we used a test set to confirm the model's performance. A total of 170 consecutive patients were included; 31.2% experienced immediate urinary continence after surgery, while 93.5% achieved a pad-free status and 6.5% were still incontinent in the 24 weeks after surgery. We divided patients into a fast recovery group (≤4 weeks) and a slow recovery group (&gt;4 weeks). Compared with the slow recovery group, the fast recovery group had a significantly lower anastomosis leakage rate, less of a downward bladder neck, and a larger bladder neck angle. Test data used to evaluate the model's performance demonstrated an average 5-fold accuracy, sensitivity, and specificity of 93.75%, 87.5%, and 100%, respectively. Postoperative cystography features can predict PPI recovery in patients with localized prostate cancer. A deep-learning model can facilitate the identification process. Further validation and exploration are required for the future development of artificial intelligence (AI) in this field.

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

Artificial Intelligence

Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation, and robotic intervention. In this article, the recent successful and influential applications of AI in surgery are reviewed from pre-operative planning and intra-operative guidance to the integration of surgical robots. It ends with summarizing the current state, emerging trends, and major challenges in the future development of AI in surgery. Robotic surgery is the use of computer technologies working in conjunction with robot systems to perform medical procedures. The technology is also known as computer-aided surgery and robot-assisted surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments in the field of Neuro Surgery.

The application of artificial intelligence in gastrointestinal endoscopy: a state-of-the-art review

Endoscopy is an important tool for detecting and diagnosing digestive diseases. However, the performance of endoscopists varies, which significantly impacts the health outcomes of patients. In recent years, with the continuous development of science and technology, artificial intelligence (AI) has also set off a new wave. Nowadays, AI has been widely studied in the medical field and has shown great potential, especially in gastrointestinal endoscopy. The application of AI in endoscopy mainly includes the detection of lesions, classification of diseases, selection of best therapy, prognosis judgment, quality control, etc. What&amp;#039;s more, the effectiveness and safety of the application of AI in gastrointestinal endoscopy have been confirmed in clinical trials. In this paper, we review the current research status and future development of AI in gastrointestinal endoscopy. Core Tips: Endoscopy is an important tool for detecting and diagnosing digestive diseases. However, the level of endoscopists varies, which significantly impacts the health outcomes of patients. In recent years, with the continuous development of science and technology, artificial intelligence (AI) has also set off a new wave. AI is increasingly widely used in the medical field nowadays and has shown great potential, especially in gastrointestinal endoscopy. The application of AI in endoscopy mainly includes the detection of lesions, classification of diseases, selection of best therapy, prognosis judgment, quality control, etc. What&amp;#039;s more, the effectiveness and safety of the application of AI in gastrointestinal endoscopy have been confirmed in clinical trials. In this paper, we review the current research status and future development of AI in gastrointestinal endoscopy.

The application of artificial intelligence in gastrointestinal endoscopy: a state-of-the-art review

Endoscopy is an important tool for detecting and diagnosing digestive diseases. However, the performance of endoscopists varies, which significantly impacts the health outcomes of patients. In recent years, with the continuous development of science and technology, artificial intelligence (AI) has also set off a new wave. Nowadays, AI has been widely studied in the medical field and has shown great potential, especially in gastrointestinal endoscopy. The application of AI in endoscopy mainly includes the detection of lesions, classification of diseases, selection of best therapy, prognosis judgment, quality control, etc. What's more, the effectiveness and safety of the application of AI in gastrointestinal endoscopy have been confirmed in clinical trials. In this paper, we review the current research status and future development of AI in gastrointestinal endoscopy.

Research on Application of Artificial Intelligence in Communication Network

Artificial intelligence has been widely utilized in people’s lives, especially in communication. The article provides a review of the recent research status of the application of artificial intelligence in communication, the usefulness of big data, machine learning, and cloud computation in this field. Several examples of how artificial intelligence is used to communicate are illustrated to discuss better and form a framework of modern artificial intelligence applications. The aspects of concern in ethics of using artificial intelligence are also mentioned, as ethics are the principles judging whether the technologies are suitable for humans to use or not. In order to predict the future development of communication, an introduction of previous progress in the field of communication is provided and sorted into categories of wireless communication, ethical concerns, and network monitor and control. This study confirms that the future development of artificial intelligence is mainly in two aspects: SDN (Software-defined network) and NFV (Network function visualization). AI will thus be used in enhancing communication efficiency.

Optimization of Composite Properties by Artificial Intelligence

This paper will summarize and study the application of artificial intelligence to composites in the past and present, and predict the development of materials by using artificial intelligence in the future. This paper will use the method of summarizing and summarizing the law from other literature. The summary of artificial intelligence in composites predicts the future development of artificial intelligence, and summarizes and clarifies the development path of artificial intelligence in composites in the future. In the context of the rapid development of artificial intelligence, using artificial intelligence to synthesize or optimize composites has become the trend of material synthesis. This will bring a new way of material synthesis.

Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research.

The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.