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1.
J Am Coll Radiol ; 16(11): 1516-1521, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31585696

RESUMO

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

2.
Can Assoc Radiol J ; 70(4): 329-334, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31585825

RESUMO

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.


Assuntos
Inteligência Artificial/ética , Radiologia/ética , Canadá , Consenso , Europa (Continente) , Humanos , Radiologistas/ética , Sociedades Médicas , Estados Unidos
3.
Insights Imaging ; 10(1): 101, 2019 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-31571015

RESUMO

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine.AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice.This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future.The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

4.
Radiology ; 293(2): 436-440, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31573399

RESUMO

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

6.
Radiology ; 288(2): 318-328, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-29944078

RESUMO

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.


Assuntos
Aprendizado de Máquina , Sistemas de Informação em Radiologia , Radiologia/métodos , Radiologia/tendências , Humanos
7.
J Am Coll Radiol ; 15(2): 350-359, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29158061

RESUMO

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.


Assuntos
Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Reconhecimento Automatizado de Padrão/métodos , Radiologia , Algoritmos , Humanos , Fluxo de Trabalho
8.
Radiology ; 285(3): 713-718, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-29155639

RESUMO

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.


Assuntos
Sistemas de Apoio a Decisões Clínicas/tendências , Diagnóstico por Imagem/tendências , Previsões , Interpretação de Imagem Assistida por Computador/métodos , Aprendizado de Máquina/tendências , Radiologia/tendências , Algoritmos , Humanos , Reconhecimento Automatizado de Padrão/tendências , Software
9.
J Digit Imaging ; 30(4): 392-399, 2017 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28516233

RESUMO

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.


Assuntos
Congressos como Assunto , Coleta de Dados , Conjuntos de Dados como Assunto , Diagnóstico por Imagem/métodos , Aprendizado de Máquina , Inteligência Artificial , Humanos , Informática Médica
10.
J Am Coll Radiol ; 14(6): 811-817, 2017 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-28372961

RESUMO

The 38th radiology Intersociety Committee reviewed the current state and future direction of clinical data science and its application to radiology practice. The assembled participants discussed the need to use current technology to better generate and demonstrate radiologists' value for our patients and referring providers. The attendants grappled with the potentially disruptive applications of machine learning to image analysis. Although the prospect of algorithms' interpreting images automatically initially shakes the core of the radiology profession, the group emerged with tremendous optimism about the future of radiology. Emerging technologies will provide enormous opportunities for radiologists to augment and improve the quality of care they provide to their patients. Radiologists must maintain an active role in guiding the development of these technologies. The conference ended with a call to action to develop educational strategies for future leaders, communicate optimism for our profession's future, and engage with industry to ensure the ethics and clinical relevance of developing technologies.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Aprendizado de Máquina , Sistemas de Informação em Radiologia , Radiologia , Algoritmos , Previsões , Humanos , Sociedades Médicas
11.
AJR Am J Roentgenol ; 208(4): 754-760, 2017 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-28125274

RESUMO

OBJECTIVE: The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. CONCLUSION: Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.


Assuntos
Algoritmos , Pesquisa Biomédica/organização & administração , Interpretação de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Reconhecimento Automatizado de Padrão/métodos , Radiologia/organização & administração , Humanos , Aumento da Imagem/métodos , Padrões de Prática Médica , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Estados Unidos
12.
J Am Coll Radiol ; 13(12 Pt A): 1519-1524, 2016 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-28233533

RESUMO

The current practice of peer review within radiology is well developed and widely implemented compared with other medical specialties. However, there are many factors that limit current peer review practices from reducing diagnostic errors and improving patient care. The development of "meaningful peer review" requires a transition away from compliance toward quality improvement, whereby the information and insights gained facilitate education and drive systematic improvements that reduce the frequency and impact of diagnostic error. The next generation of peer review requires significant improvements in IT functionality and integration, enabling features such as anonymization, adjudication by multiple specialists, categorization and analysis of errors, tracking, feedback, and easy export into teaching files and other media that require strong partnerships with vendors. In this article, the authors assess various peer review practices, with focused discussion on current limitations and future needs for meaningful peer review in radiology.


Assuntos
Erros de Diagnóstico/prevenção & controle , Revisão dos Cuidados de Saúde por Pares/normas , Garantia da Qualidade dos Cuidados de Saúde/normas , Radiologia/normas , Competência Clínica/normas , Previsões , Humanos , Melhoria de Qualidade
13.
J Am Coll Radiol ; 12(4): 396-402, 2015 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-25842018

RESUMO

Imaging 3.0 is a radiology community initiative to empower radiologists to create and demonstrate value for their patients, referring physicians, and health systems. In image-guided health care, radiologists contribute to the entire health care process, well before and after the actual examination, and out to the point at which they guide clinical decisions and affect patient outcome. Because imaging is so pervasive, radiologists who adopt Imaging 3.0 concepts in their practice can help their health care systems provide consistently high-quality care at reduced cost. By doing this, radiologists become more valuable in the new health care setting. The authors describe how informatics is critical to embracing Imaging 3.0 and present a scorecard that can be used to gauge a radiology group's informatics resources and capabilities.


Assuntos
Sistemas de Apoio a Decisões Clínicas/organização & administração , Diagnóstico por Imagem/métodos , Informática Médica/organização & administração , Sistemas de Informação em Radiologia/organização & administração , Estados Unidos
14.
AJR Am J Roentgenol ; 204(4): 716-20, 2015 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-25794061

RESUMO

OBJECTIVE: Informatics innovations of the past 30 years have improved radiology quality and efficiency immensely. Radiologists are groundbreaking leaders in clinical information technology (IT), and often radiologists and imaging informaticists created, specified, and implemented these technologies, while also carrying the ongoing burdens of training, maintenance, support, and operation of these IT solutions. Being pioneers of clinical IT had advantages of local radiology control and radiology-centric products and services. As health care businesses become more clinically IT savvy, however, they are standardizing IT products and procedures across the enterprise, resulting in the loss of radiologists' local control and flexibility. Although this inevitable consequence may provide new opportunities in the long run, several questions arise. CONCLUSION: What will happen to the informatics expertise within the radiology domain? Will radiology's current and future concerns be heard and their needs addressed? What should radiologists do to understand, obtain, and use informatics products to maximize efficiency and provide the most value and quality for patients and the greater health care community? This article will propose some insights and considerations as we rethink radiology informatics.


Assuntos
Diagnóstico por Imagem/tendências , Aplicações da Informática Médica , Difusão de Inovações , Eficiência Organizacional , Previsões , Humanos , Serviço Hospitalar de Radiologia/tendências , Sistemas de Informação em Radiologia/tendências
15.
J Am Coll Radiol ; 11(12 Pt B): 1197-204, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-25467895

RESUMO

Imaging 3.0 is a blueprint for the future of radiology modeled after the description of Web 3.0 as "more connected, more open, and more intelligent." Imaging 3.0 involves radiologists' using their expertise to manage all aspects of imaging care to improve patient safety and outcomes and to deliver high-value care. IT tools are critical elements and drivers of success as radiologists embrace the concepts of Imaging 3.0. Organized radiology, specifically the ACR, is the natural convener and resource for the development of this Imaging 3.0 toolkit. The ACR's new Imaging 3.0 Informatics Committee is actively working to develop the informatics tools radiologists need to improve efficiency, deliver more value, and provide quantitative ways to demonstrate their value in new health care delivery and payment systems. This article takes each step of the process of delivering high-value Imaging 3.0 care and outlines the tools available as well as additional resources available to support practicing radiologists. From the moment when imaging is considered through the delivery of a meaningful and actionable report that is communicated to the referring clinician and, when appropriate, to the patient, Imaging 3.0 IT tools will enable radiologists to position themselves as vital constituents in cost-effective, high-value health care.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Internet/organização & administração , Informática Médica/métodos , Sistemas de Informação em Radiologia/organização & administração , Software , Tecnologia Radiológica/métodos , Desenho de Programas de Computador
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