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artificial general intelligence

Min Yang, Wenting Tu, Qiang Qu, Zhou Zhao, Xiaojun Chen, Jia Zhu
Open-domain conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. The goal of this paper is to further improve the response generation, using personalization criteria. We propose a novel method called PRGDDA (Personalized Response Generation by Dual-learning based Domain Adaptation) which is a personalized response generation model based on theories of domain adaptation and dual learning...
April 5, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Angel Jimenez-Molina, Jorge Gaete-Villegas, Javier Fuentes
New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, highlighted by multiple international organizations as a core issue in future healthcare. Despite the myriad of exciting new developments, each application and system is designed and implemented for specific purposes and lacks the flexibility to support different healthcare concerns...
April 5, 2018: Journal of Biomedical Informatics
Seok Won Chung, Seung Seog Han, Ji Whan Lee, Kyung-Soo Oh, Na Ra Kim, Jong Pil Yoon, Joon Yub Kim, Sung Hoon Moon, Jieun Kwon, Hyo-Jin Lee, Young-Min Noh, Youngjun Kim
Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods - 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset...
March 26, 2018: Acta Orthopaedica
J C Montoya, Y Li, C Strother, G-H Chen
BACKGROUND AND PURPOSE: Deep learning is a branch of artificial intelligence that has demonstrated unprecedented performance in many medical imaging applications. Our purpose was to develop a deep learning angiography method to generate 3D cerebral angiograms from a single contrast-enhanced C-arm conebeam CT acquisition in order to reduce image artifacts and radiation dose. MATERIALS AND METHODS: A set of 105 3D rotational angiography examinations were randomly selected from an internal data base...
March 22, 2018: AJNR. American Journal of Neuroradiology
Fernando Yepes-Calderon, Marvin D Nelson, J Gordon McComb
The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system's daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders...
2018: PloS One
Vedran Dunjko, Hans J Briegel
Quantum information technologies, on the one side, and intelligent learning
 systems, on the other, are both emergent technologies that will likely have a
 transforming impact on our society in the future.
 The respective underlying fields of basic research -- quantum information (QI) versus
 machine learning and artificial intelligence (AI)
 -- have their own specific questions and challenges, which have hitherto been
 investigated largely independently. However, in a growing body of recent work,
 researchers have been probing the question to what extent these fields can indeed
 learn and benefit from each other...
March 5, 2018: Reports on Progress in Physics
Tanveer Syeda-Mahmood
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era...
March 2018: Journal of the American College of Radiology: JACR
Fernando Aparicio, María Luz Morales-Botello, Margarita Rubio, Asunción Hernando, Rafael Muñoz, Hugo López-Fernández, Daniel Glez-Peña, Florentino Fdez-Riverola, Manuel de la Villa, Manuel Maña, Diego Gachet, Manuel de Buenaga
BACKGROUND: Student participation and the use of active methodologies in classroom learning are being increasingly emphasized. The use of intelligent systems can be of great help when designing and developing these types of activities. Recently, emerging disciplines such as 'educational data mining' and 'learning analytics and knowledge' have provided clear examples of the importance of the use of artificial intelligence techniques in education. OBJECTIVE: The main objective of this study was to gather expert opinions regarding the benefits of using complementary methods that are supported by intelligent systems, specifically, by intelligent information access systems, when processing texts written in natural language and the benefits of using these methods as companion tools to the learning activities that are employed by biomedical and health sciences teachers...
April 2018: International Journal of Medical Informatics
Lei Xing, Elizabeth A Krupinski, Jing Cai
Recently there has been a significant surge in applications of artificial intelligence (AI) in medicine, suggesting that AI may soon dramatically change the landscape of healthcare and thus medical physics. It is generally expected that AI will improve overall healthcare in an enormous way. Some believe that AI will similarly impact medical physics research and practice, while others believe that these expectations to be unrealistic due to issues of technical validation and practical limitations of AI. This is the premise debated in this month's Point/Counterpoint...
February 24, 2018: Medical Physics
Huan Tao, Qian Li, Qin Zhou, Jie Chen, Bo Fu, Jing Wang, Wenzhe Qin, Jianglong Hou, Jin Chen, Li Dong
BACKGROUND: It's difficult but urgent to achieve the individualized rational medication of the warfarin, we aim to predict the individualized warfarin stable dose though the artificial intelligent Adaptive neural-fuzzy inference system (ANFIS). METHODS: Our retrospective analysis based on a clinical database, involving 21,863 patients from 15 Chinese provinces who receive oral warfarin after the heart valve replacement. They were allocated into four groups: the external validation group (A group), the internal validation group (B group), training group (C group) and stratified training group (D group)...
February 15, 2018: BMC Surgery
Byoung Chul Ko
Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms...
January 30, 2018: Sensors
Agnaldo S Cruz, Hertz C Lins, Ricardo V A Medeiros, José M F Filho, Sandro G da Silva
INTRODUCTION: The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. METHODS: A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms "Neural Network", "Osteoporosis Machine Learning" and "Osteoporosis Neural Network"...
January 29, 2018: Biomedical Engineering Online
Xia Zhang, Bei Ding, Ran Cheng, Sebastian C Dixon, Yao Lu
In recent years, state-of-the-art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature-inspired superhydrophobic behavior...
January 2018: Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
Hong Men, Songlin Fu, Jialin Yang, Meiqi Cheng, Yan Shi, Jingjing Liu
Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin...
January 18, 2018: Sensors
Jacob W Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A Goodrich, Iyad Rahwan
Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions...
January 16, 2018: Nature Communications
Daniel Merk, Lukas Friedrich, Francesca Grisoni, Gisbert Schneider
Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model...
January 2018: Molecular Informatics
Thomas Gisiger, Mounir Boukadoum
We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections...
February 2018: Neural Networks: the Official Journal of the International Neural Network Society
Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang
BACKGROUND: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain...
January 5, 2018: JMIR Medical Informatics
C H Fang, Y Y Lau, W P Zhou, W Cai
Digital medical technology is a powerful tool which has forcefully promoted the development of general surgery in China. In this article, we reviews the application status of three-dimensional visualization and three-dimensional printing technology in general surgery, introduces the development situation of surgical navigation guided by optical and electromagnetic technology and preliminary attempt to combined with mixed reality applied to complicated hepatectomy, looks ahead the development direction of digital medicine in the era of artificial intelligence and big data on behalf of surgical robot and radiomics...
December 1, 2017: Zhonghua Wai Ke za Zhi [Chinese Journal of Surgery]
Elvira Moreno Barriga, Irene Pueyo Ferrer, Miquel Sánchez Sánchez, Montserrat Martín Baranera, Josep Masip Utset
OBJECTIVES: To analyze agreement between diagnoses issued by the Mediktor application and those of an attending physician, and to evaluate the usefulness of this application in patients who seek emergency care. MATERIAL AND METHODS: Prospective observational study in a tertiary care university hospital emergency department. Patients with medical problems and surgical conditions (surgery and injuries) who did not require immediate emergency care responded to the Mediktor questions on a portable computer tablet...
2017: Emergencias: Revista de la Sociedad Española de Medicina de Emergencias
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