A noteworthy change in recent medical research is the rapid increase of research using big data obtained from electrical medical records (EMR), order communication systems (OCS), and picture archiving and communication systems (PACS). It is often difficult to apply traditional statistical techniques to research using big data because of the vastness of the data and complexity of the relationships. Therefore, the application of artificial intelligence (AI) techniques which can handle such problems is becoming popular. Classical machine learning techniques, such as k-means clustering, support vector machine, and decision tree are still efficient and useful for some research problems. The deep learning techniques, such as multi-layer perceptron, convolutional neural network, and recurrent neural network have been spotlighted by the success of deep belief networks and convolutional neural networks in solving various problems that are difficult to solve by conventional methods. The results of recent research using artificial intelligence techniques are comparable to human experts. This article introduces technologies that help researchers conduct medical research and understand previous literature in the era of AI.
Anesthesia* ; Artificial Intelligence* ; Decision Trees ; Humans ; Learning ; Machine Learning ; Medical Records ; Neural Networks (Computer) ; Radiology Information Systems ; Support Vector Machine

With the advent of the era of big data, artificial intelligence based on machine learning, especially artificial neural network has rapidly developed and applicated in the field of stomatology, owning huge potential in segmentation and labelling of three-dimensional intraoral anatomical features. Automatic segmentation, labelling and diagnosis can assist dentists and technicians to complete heavy and repeat work, change stomatology from subjective perception to objective science, and help to make diagnosis and treatment plan efficiently and accurately. This review briefly summarized related knowledge and development of machine learning and artificial neural network, its application status and existing problems in the field of segmentation and labelling of three-dimensional intraoral anatomical features, and provided an outlook of its future development.
Artificial Intelligence ; Machine Learning ; Neural Networks, Computer

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.
Humans ; Artificial Intelligence ; Machine Learning ; Algorithms ; Neural Networks, Computer ; Medicine

This paper descries a new non-invasive method for diagnosis of breathing disorders based on adaptive-network-based fuzzy inference system (ANFIS). In this method, PetCO2, SpO2 and HR are chosen as inputs, and the breathing condition is selected as output ofANFIS. The inputs and output are then classified into fuzzy subsets by experts' knowledge. After, the fuzzy IF-THEN rules are built up according to the corresponding membership functions by set up of fuzzy subsets. The neural network was finally established and the membership functions and fuzzy rules were optimized by training. The results of experiment shows that ANFIS is more effective than BP Network regarding the diagnosis of breathing disorders.
Artificial Intelligence ; Fuzzy Logic ; Humans ; Neural Networks (Computer) ; Respiration Disorders ; diagnosis

Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression of the discipline toward a vision of how clinical medicine, empowered by technology, can achieve our national healthcare objectives of delivering value-based and patient-centric care. In this article, we consider 3 core questions relating to AI in radiology, and review the barriers to the widespread adoption of AI in radiology. We propose solutions and describe a "Centaur" model as a promising avenue for enabling the interfacing between AI and radiologists. Finally, we introduce The Radiological AI, Data Science and Imaging Informatics (RADII) subsection of the Singapore Radiological Society. RADII is an enabling body, which together with key technological and institutional stakeholders, will champion research, development and evaluation of AI for radiology applications.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Neural Networks (Computer) ; Radiology ; Singapore ; Societies, Medical

Artificial intelligence (AI) is one of the most rapidly evolving fields in biomedicine during the past decade. Represented by radiomics, machine learning and deep neural network, AI has been increasingly favored by researchers due to its ability to obtain feature information and discover the potential relationship between data and medical outcomes from high-throughput medical data. The incidence and mortality of gastric cancer (GC) has remained high in China. Through combining AI technology with medical examination such as endoscopy, imaging, pathological examination and sequencing, clinical researchers have made great progress in the auxiliary diagnosis, disease staging, prognosis and curative effect prediction of patients with GC. Although the intervention of AI in the medical industry has greatly improved the effective utilization of high-throughput data and accelerated the intelligent process of disease diagnosis and treatment, a number of problems has been raised in medical ethics, patient privacy and the legal status of medical AI at the same time. In the future, rational planning and management of AI technology will provide a strong impetus to promote the development of medicine and reshape the medical industry.
Artificial Intelligence ; Humans ; Machine Learning ; Neural Networks, Computer ; Stomach Neoplasms/therapy* ; Technology

PURPOSE: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). METHODS: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. RESULTS: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%–91.2%) for premolars and 73.4% (95% CI, 59.9%–84.0%) for molars. CONCLUSIONS: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.
Area Under Curve ; Artificial Intelligence ; Bicuspid ; Boidae ; Dataset ; Diagnosis ; Learning ; Machine Learning ; Methods ; Molar ; Periodontal Diseases ; ROC Curve ; Sensitivity and Specificity ; Supervised Machine Learning ; Tooth ; Weights and Measures

Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro
Artificial Intelligence ; China ; Humans ; Language ; Natural Language Processing ; Neural Networks, Computer

The protein spectrometry holds such characteristics of complex and large volumes of data that the general statistical methods can't satisfy the demand of disease prediction or classification. Several kinds of main methods of mass spectrometry data mining,such as decision tree analysis, partial least squares, artificial neural networks and support vector machines is overviewed in bioinformatics perspective. And examples of different methods used to diagnose disease are illustrated. These show an important role of mass spectrometry in identification and prediction of disease.
Artificial Intelligence ; Computational Biology ; Data Mining ; Decision Trees ; Least-Squares Analysis ; Mass Spectrometry ; Neural Networks (Computer) ; Support Vector Machine

Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.
Algorithms ; Artificial Intelligence ; Brain ; Neural Networks, Computer