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2.
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.

3.
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
4.
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.

7.
J Thorac Imaging ; 30(2): 115-29, 2015 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-25658476

RESUMO

The purpose of this article is to review clinical computed tomography (CT) lung screening program elements essential to safely and effectively manage the millions of Americans at high risk for lung cancer expected to enroll in lung cancer screening programs over the next 3 to 5 years. To optimize the potential net benefit of CT lung screening and facilitate medical audits benchmarked to national quality standards, radiologists should interpret these examinations using a validated structured reporting system such as Lung-RADS. Patient and physician educational outreach should be enacted to support an informed and shared decision-making process without creating barriers to screening access. Programs must integrate smoking cessation interventions to maximize the clinical efficacy and cost-effectiveness of screening. At an institutional level, budgets should account for the necessary expense of hiring and/or training qualified support staff and equipping them with information technology resources adequate to enroll and track patients accurately over decades of future screening evaluation. At a national level, planning should begin on ways to accommodate the upcoming increased demand for physician services in fields critical to the success of CT lung screening such as diagnostic radiology and thoracic surgery. Institutions with programs that follow these specifications will be well equipped to meet the significant oncoming demand for CT lung screening services and bestow clinical benefits on their patients equal to or beyond what was observed in the National Lung Screening Trial.


Assuntos
Neoplasias Pulmonares/diagnóstico por imagem , Doses de Radiação , Tomografia Computadorizada por Raios X/métodos , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Detecção Precoce de Câncer/métodos , Detecção Precoce de Câncer/estatística & dados numéricos , Humanos , Programas de Rastreamento/métodos , Programas de Rastreamento/estatística & dados numéricos
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