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Artificial Intelligence Market Fintech Forecast 2017 2022 PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Artificial Intelligence Market Fintech Forecast 2017 2022 articles that have been published worldwide.
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The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artificial intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care.
This paper presents results of an artificial stock market and tries to make it more consistent with the statistical features of real stock data. Based on the SFI-ASM, a novel model is proposed to make agents more close to the real world. Agents are divided into four kinds in terms of different learning speeds, strategy-sizes, utility functions, and level of intelligence; and a crucial parameter has been found to ensure system stability. So, some parameters are appended to make the model which contains zero-...
The purpose of this article is to highlight best practices for writing and reviewing articles on artificial intelligence for medical image analysis.
Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fund data for perfectly timed (by hindsight) all or nothing quarterly switching between two asset classes, US stocks and bonds over the time period 1993-2017. The historical optimal timing path ...
To assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine.
Artificial Intelligence (AI) is a branch of computer science that deals with the development of algorithms that seek to simulate human intelligence. We provide an overview of the basic principles in AI that are essential to the understanding of AI and its application to health care. We also present a descriptive analysis of the current state of AI in various fields of medicine, especially ophthalmology. Finally, we review the potential limitations and challenges that come along with the development and impl...
There has been wide interest in using artificial intelligence (AI)-based grading of retinal images to identify diabetic retinopathy, but such a system has never been deployed and evaluated in clinical practice.
Despite decades-long reductions in cardiovascular disease (CVD) mortality, CVD mortality rates have recently plateaued and even increased in some subgroups, and the prevalence of CVD risk factors remains high. Million Hearts 2022, a 5-year initiative, was launched in 2017 to address this burden. This report establishes a baseline for the CVD risk factors targeted for reduction by the initiative during 2017-2021 and highlights recent changes over time.
The evaluation of Acne using ordinal scales reflects the clinical perception of severity but has shown low reproducibility both intra- and inter-rater. In this study, we investigated if Artificial Intelligence trained on images of Acne patients could perform acne grading with high accuracy and reliabilities superior to those of expert physicians.
The doctor-patient relationship has been evolving from benevolent paternalism to a more patient-centered relationship in the modern era. Although artificial intelligence (AI) has the potential to improve nearly every aspect of health care, many physicians are skeptical about integrating AI into their current medical practice. The purpose of this article is to explore what AI means for the doctor-patient relationship and for breast imaging radiologists.
The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma.
Artificial intelligence (AI) has been heralded as the next big wave in the computing revolution and touted as a transformative technology for many industries including health care. In radiology, considerable excitement and anxiety are associated with the promise of AI and its potential to disrupt the practice of the radiologist. Radiology has often served as the gateway for medical technological advancements, and AI will likely be no different. We present a brief overview of AI advancements that have driven...
The interest in Expert systems has increased in the medical area. Some of them are employed even for diagnosis. With the variability of transcatheter prostheses, the most appropriate choice can be complex. This scenario reveals an enabling environment for the use of an Expert system. The goal of the study was to develop an Expert system based on artificial intelligence for supporting the transcatheter aortic prosthesis selection.
To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM).
Despite its preventability, cardiovascular disease remains a leading cause of morbidity, mortality, and health care costs in the United States. This study describes the burden, in 2016, of nonfatal and fatal cardiovascular events targeted for prevention by Million Hearts 2022, a national initiative working to prevent one million cardiovascular events during 2017-2021.
An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly availabl...
Diagnosis of psychiatric disorders is based primarily on subjective symptoms, and neuroimaging or other biological examinations are used for excluding organic disorders. Advances in artificial intelligence technologies, such as machine learning, may enable us to utilize neuroimaging for individual diagnosis of psychiatric disorder or treatment response prediction. In addition, such technologies may elucidate the underlying pathophysiology of psychiatric disorders. In this article, we review studies that uti...
The endocytoscopic system (ECS) helps in virtual realization of histology and can aid in confirming histological diagnosis in vivo. We propose replacing biopsy-based histology for esophageal squamous cell carcinoma (ESCC) by using the ECS. We applied deep-learning artificial intelligence (AI) to analyse ECS images of the esophagus to determine whether AI can support endoscopists for the replacement of biopsy-based histology.
Digitalization is transforming every aspect of life, it is also transforming deeply medicine. The digitalization era is characterized by a large production of new data streams while existing processes are progressively migrated, such as writing or imaging. The very large and fast-growing amount of data available requires new storage, transport and analytical tools. This paper presents some of them, such as natural language processing, artificial intelligence, and graph databases. A short introduction to the...
To give a high-level summary to current approaches for implementing artificial intelligence (AI), we explain the key commonalities and major differences between Turing's approach and Wiener's approach in this perspective. Especially, the problems, successful achievements, limitations, and future research directions of existing approaches that follow Weiner's ideas are addressed, respectively, aiming to provide readers with a good start point and a roadmap. Some other related topics, for example, the role of...
Deep learning is a subset of the medical application of artificial intelligence. Its significant results are garnering attention, particularly in radiographic image interpretation, pathological diagnosis, gene analysis, and prediction of cancer recurrence. In this study, we summarize the concept of deep learning. The human body structure, from the molecule to physical functions, is a complex system. Deep learning is a new way to analyze its complex systems. An essential point of the analysis is the categori...
Evidence that artificial intelligence (AI) is useful for predicting risk factors for hypertension and its management is emerging. However, we are far from harnessing the innovative AI tools to predict these risk factors for hypertension and applying them to personalized management. This review summarizes recent advances in the computer science and medical field, illustrating the innovative AI approach for potential prediction of early stages of hypertension. Additionally, we review ongoing research and futu...