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1.
Nursing ; 49(9): 46-49, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31365455

RESUMO

Artificial intelligence (AI) is a transformational technology that will affect all healthcare providers. This article offers an overview of basic AI concepts and the role of nurses in embracing this technology in healthcare settings.


Assuntos
Inteligência Artificial , Papel do Profissional de Enfermagem , Enfermagem/organização & administração , Humanos
2.
Stud Health Technol Inform ; 264: 1696-1697, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438298

RESUMO

Many currently available Diagnostic Decision Support Systems (DDSS) are based on causal condition-symptom relations that exhibit certain shortcomings. Ada's new approach explores the capabilities of DDSS based on pathophysiology, describing a disease as a dynamically evolving process. We generated a pathophysiology model for 8 conditions and 68 findings suitable to assess this approach. Preliminary results meet our expectations while leaving space for further improvement.


Assuntos
Inteligência Artificial , Sistemas de Apoio a Decisões Clínicas , Sistemas Especialistas , Assistência à Saúde , Software
3.
Stud Health Technol Inform ; 264: 1337-1341, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438143

RESUMO

Juvenile Idiopathic Arthritis (JIA) is the most common chronic rheumatic disease of childhood, with outcomes including pain, prolonged dependence on medications, and disability. Parents of children with JIA report being overwhelmed by the volume of information in the patient education materials that are available to them. This paper addresses this educational gap by applying an artificial intelligence method, based on an extended model of argument, to design and implement a dialogue system that allows users get the educational material they need, when they need it. In the developed system, the studied model of argument was leveraged as part of the system's dialogue manager. A qualitative evaluation of the system, using cognitive walkthroughs and semi-structured interviews with JIA domain experts, suggests that these methods show great promise for providing quality information to families of children with JIA when they need it.


Assuntos
Artrite Juvenil , Doenças Reumáticas , Inteligência Artificial , Criança , Humanos , Pais , Qualidade de Vida
4.
Stud Health Technol Inform ; 264: 1464-1465, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438183

RESUMO

In the 5P medicine (Personalized, Preventive, Participative, Predictive and Pluri-expert), the general trend is to process data by displacing the barycenter of the information from hospital centered systems to the patient centered ones through his personal medical records. Today, the use of artificial intelligence for supporting this transition shows real limitations in its implementation in operational practice, both at the level of patient care, but also in the general daily life of the health professional, because of the medico-legal imperatives induced by the promises of the '5P medicine'. In this paper, we propose to fill this gap by introducing an original artificial intelligence platform, named Maxwell, which follows an unsupervised learning approach in line with the medico-legal imperatives of the '5P medicine'. We describe the functional platform characteristics and illustrate them by two examples of clustering in genomics and magnetic resonance imaging.


Assuntos
Medicina , Aprendizado de Máquina não Supervisionado , Inteligência Artificial , Genômica , Humanos , Imagem por Ressonância Magnética
7.
Stud Health Technol Inform ; 264: 1556-1557, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438229

RESUMO

The demand for AI to improve patients outcome has been increased; we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under receiver operating curve (AUROC) was 98.93% (95%CI:98.37%-99.49%). However, the overall pooled sensitivity and specificity for detecting referable diabetic retinopathy (RDR) was 74% (95% CI: 73%-74%), and 95% (95% CI: 95%-95%). The findings of this study show that deep learning had high sensitivity and specificity for identifying diabetic retinopathy.


Assuntos
Retinopatia Diabética , Algoritmos , Inteligência Artificial , Aprendizado Profundo , Humanos
8.
Stud Health Technol Inform ; 266: 83-88, 2019 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-31397306

RESUMO

The paper applies an artificial intelligence centered method to classify 12 clinical safety incident (CSI) classes. The paper aims to establish a taxonomy that classifies the CSI reports into their correct classes automatically and with high accuracy. The study investigates feasibility of applying the C4.5 decision tree (DT) classifier and the random forest (RF) classifier for this purpose. The classifiers were trained using randomly selected 3600 CSIs from an Incident Information Management System (IIMS) used by seven hospitals. The taxonomies investigated were the Generic Reference Model (GRM) and the World Health Organization (WHO) patient safety classification. The classifiers trained 13 GRM CSI classes and 9 WHO CSI classes using a bag-of-words approach. The overall taxonomies performance on the RF classifier was better than on the DT classifier. The performance achieved by the classifier applying the WHO taxonomy was better than the GRM taxonomy. Four of the five poorly performing classes in the GRM taxonomy significantly improved their performance on changing the taxonomy. To improve the WHO taxonomy performance the improved WHO (WHO-I) taxonomy was built by adding a new class that did not exist in WHO but existed in GRM. The performance of the RF classifier applied to the WHO-I taxonomy further improved.


Assuntos
Inteligência Artificial , Árvores de Decisões , Gestão de Riscos , Humanos , Segurança do Paciente
9.
Stud Health Technol Inform ; 265: 113-118, 2019 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-31431586

RESUMO

This study aimed to develop a classification scheme for retrieving information from incident reports of medication errors. This 15-category classification scheme captures minimal medication-incident related information from incident reports and thus serves as an information model for automatic information retrieval solution. The automatic solution uses recent advances in artificial intelligence methods to learn from incident report resources and is promising to the prevention of adverse drug events and promotion of safety in medical care.


Assuntos
Erros Médicos , Inteligência Artificial , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Erros de Medicação , Gestão de Riscos
10.
Stud Health Technol Inform ; 264: 561-565, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437986

RESUMO

This paper presents a pioneering and practical experience in the development and implementation of a clinical decision support system (CDSS) based on natural language processing (NLP) and artificial intelligence (AI) techniques. Our CDSS notifies primary care physicians in real time about recommendations regarding the healthcare process. This is, to the best of our knowledge, the first real-time CDSS implemented in the Spanish National Health System. We achieved adherence rate improvements in eight out of 18 practices. Moreover, the provider's feedback was very positive, describing the solution as fast, useful, and unintrusive. Our CDSS reduced clinical variability and revealed the usefulness of NLP and AI techniques for the evaluation and improvement of health care.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Processamento de Linguagem Natural , Inteligência Artificial , Procedimentos Clínicos , Registros Eletrônicos de Saúde
11.
Stud Health Technol Inform ; 264: 1993-1994, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438445

RESUMO

Significant efforts are being made to develop artificial intelligence technologies for health settings. In a health system that has been notoriously slow to adopt innovative technologies, it is important to consider the implementation of a new technology early in the development stage, especially one that will have added challenges of trust and transparency. To facilitate this process, an implementation framework for artificial intelligence technologies in clinical settings has been created.


Assuntos
Inteligência Artificial , Assistência à Saúde
12.
Medicine (Baltimore) ; 98(32): e16379, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31393347

RESUMO

BACKGROUND: More and more automated efficient ultrasound image analysis techniques, such as ultrasound-based computer-aided diagnosis system (CAD), were developed to obtain accurate, reproducible, and more objective diagnosis results for thyroid nodules. So far, whether the diagnostic performance of existing CAD systems can reach the diagnostic level of experienced radiologists is still controversial. The aim of the meta-analysis was to evaluate the accuracy of CAD for thyroid nodules' diagnosis by reviewing current literatures and summarizing the research status. METHODS: A detailed literature search on PubMed, Embase, and Cochrane Libraries for articles published until December 2018 was carried out. The diagnostic performances of CAD systems vs radiologist were evaluated by meta-analysis. We determined the sensitivity and the specificity across studies, calculated positive and negative likelihood ratios and constructed summary receiver-operating characteristic (SROC) curves. Meta-analysis of studies was performed using a mixed-effect, hierarchical logistic regression model. RESULTS: Five studies with 536 patients and 723 thyroid nodules were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) for CAD system were 0.87 (95% confidence interval [CI], 0.73-0.94), 0.79 (95% CI 0.63-0.89), 4.1 (95% CI 2.5-6.9), 0.17 (95% CI 0.09-0.32), and 25 (95% CI 15-42), respectively. The SROC curve indicated that the area under the curve was 0.90 (95% CI 0.87-0.92). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and DOR for experienced radiologists were 0.82 (95% CI 0.69-0.91), 0.83 (95% CI 0.76-0.89), 4.9 (95% CI 3.4-7.0), 0.22 (95% CI 0.12-0.38), and 23 (95% CI 11-46), respectively. The SROC curve indicated that the area under the curve was 0.96 (95% CI 0.94-0.97). CONCLUSION: The sensitivity of the CAD system in the diagnosis of thyroid nodules was similar to that of experienced radiologists. However, the CAD system had lower specificity and DOR than experienced radiologists. The CAD system may play the potential role as a decision-making assistant alongside radiologists in the thyroid nodules' diagnosis. Future technical improvements would be helpful to increase the accuracy as well as diagnostic efficiency.


Assuntos
Diagnóstico por Computador/métodos , Diagnóstico por Computador/normas , Radiologistas/normas , Nódulo da Glândula Tireoide/diagnóstico , Inteligência Artificial , Diagnóstico Diferencial , Humanos , Curva ROC , Sensibilidade e Especificidade , Neoplasias da Glândula Tireoide/diagnóstico , Neoplasias da Glândula Tireoide/patologia , Nódulo da Glândula Tireoide/diagnóstico por imagem , Nódulo da Glândula Tireoide/patologia , Ultrassonografia
14.
Stud Health Technol Inform ; 262: 1-6, 2019 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-31349251

RESUMO

This lecture is dealing with new, future forms of collaboration and with its (hopefully existing) extended synergies, which may now will come in our era of digitization. Entities in this collaboration are we, the human beings, and other living entities such as animals with 'natural intelligence' as well as non-living entities, in particular functionally comprehensive machines, with 'artificial intelligence'. Based on lessons learned during the last years, among others in a task force on synergy and intelligence (SYnENCE) of the Braunschweig Scientific Society, five consequences for future health care with respect to this collaboration are put for discussion: (1) functional comprehensive 'intelligent' machines should be regarded as entities, not as modalities, (2) such machines have to become users of information systems in health, in addition to human entities, appropriate (3) legal and (4) ethical frameworks have to be developed, (5) extended collaboration in medicine and health care needs to be evaluated in accordance with good scientific practice. The statements of Karl Jaspers, made in 1946 on medicine and on technology, may help us to find a good way.


Assuntos
Inteligência Artificial , Assistência à Saúde , Animais , Humanos , Inteligência
15.
Stud Health Technol Inform ; 262: 268-271, 2019 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-31349319

RESUMO

Venture Capital (VC) funding raised by companies producing Artificial Intelligence (AI) or Machine Learning (ML) solutions is on the rise and a driver of technology development. In healthcare, VC funding is distributed unevenly and certain technologies have attracted significantly more funding than others have. We analyzed a database of 106 Healthcare AI companies collected from open online sources to understand factors affecting the VC funding of AI companies operating in different areas of healthcare. The results suggest that there is a significant connection between higher funding and having research organizations or pharmaceutical companies as the customer of the product or service. In addition, focusing on AI solutions that are applied to direct patient care delivery is associated with lower funding. We discuss the implications of our findings for public health technology funding institutions.


Assuntos
Inteligência Artificial , Financiamento de Capital , Assistência à Saúde , Comércio , Humanos , Aprendizado de Máquina
16.
Soins ; 64(837): 24-27, 2019.
Artigo em Francês | MEDLINE | ID: mdl-31345304

RESUMO

The use of artificial intelligence and robotics in health care means ethical principles need to be established. Artificial and human intelligence must be implemented in such as way as to complement each other. From humanism to anthropotechnics, the definitions of human and humanism are not set in stone. A philosophical reflection can enable their definition to be shaped.


Assuntos
Inteligência Artificial , Obrigações Morais , Assistência à Saúde/organização & administração , Humanismo , Humanos , Robótica
17.
N C Med J ; 80(4): 219-223, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31278181

RESUMO

As the health care industry adopts artificial intelligence, machine learning, and other modeling techniques, it is seeing benefits to both patient outcomes and cost reduction; however, it needs to be cognizant of and ensure proper management of the risks, including bias. Lessons learned from other industries may provide a framework for acknowledging and managing data, machine, and human biases that arise while implementing AI.


Assuntos
Inteligência Artificial , Viés , Aprendizado de Máquina , Análise de Dados , Assistência à Saúde , Humanos
18.
Brain Nerve ; 71(7): 649-655, 2019 Jul.
Artigo em Japonês | MEDLINE | ID: mdl-31289239

RESUMO

Artificial intelligence and brain science have kept a swinging relationship with opposing views: "Artificial realization of intelligence should be free from biological constraints" and "We should reverse-engineer the best existing implementation of intelligence." In this article, we first review today's achievements of artificial intelligence and its impacts on brain and life sciences. We then discuss how progresses in brain science can contribute to future developments in artificial intelligence.


Assuntos
Inteligência Artificial/tendências , Encéfalo , Neurobiologia/tendências , Humanos
19.
Brain Nerve ; 71(7): 665-680, 2019 Jul.
Artigo em Japonês | MEDLINE | ID: mdl-31289241

RESUMO

Half a century ago, cerebellar learning models based on a simple perceptron were proposed independently by Marr and Albus. Soon, these models were combined with Ito's flocculus hypothesis that the cerebellar flocculus controls the vestibulo-ocular reflex through teacher signal-dependent learning, and consequently integrated into the so-called Marr-Albus-Ito cerebellar learning hypothesis. Ten years later, Ito found the synaptic plasticity of long-term depression at cerebellar Purkinje cell synapses, which underlies cerebellar learning. The liquid-state machine (LSM) model, which adds the random inhibitory recurrent neural network composed of granule cells --Golgi cells loop to a simple perceptron, explained the learning of timing in eyeblink conditioning, the learning of gains in ocular reflex, and the formation of short- and long-term motor memories in the cerebellum. The LSM model is now extended to the cerebellar internal model-based voluntary movement control and cognitive function. Artificial intelligence (AI) based on the neural network models originating from a simple perceptron, has now developed to deep learning. As the LSM model of the cerebellum is the counterpart of deep learning in the brain, the cerebellum is considered to be the origin of current AI. Finally, we discuss the impact of the evolution of AI on future clinical cerebellar neurology.


Assuntos
Inteligência Artificial , Cerebelo/fisiologia , Modelos Neurológicos , Plasticidade Neuronal , Humanos , Redes Neurais (Computação) , Reflexo Vestíbulo-Ocular , Sinapses
20.
Brain Nerve ; 71(7): 705-713, 2019 Jul.
Artigo em Japonês | MEDLINE | ID: mdl-31289244

RESUMO

In Japan, the revision of the Copyright Act and the Unfair Competition Prevention Act has been made toward the use of artificial intelligence (AI) and data. The Ministry of Economy, Trade, and Industry have issued "Contract guidelines on the use of AI data" and other rules are being developed. In the medical field, in addition to these general rules, it is important to protect patient rights and to protect intellectual property for appropriate evaluation of medical personnel for data creation.


Assuntos
Inteligência Artificial , Propriedade Intelectual , Japão
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