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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.
Conf Proc IEEE Eng Med Biol Soc ; 2017: 2430-2433, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-29060389

RESUMO

Patients are increasingly presented with their health data through patient portals in an attempt to engage patients in their own care. Due to the large amounts of data generated during a patient visit, the medical information when shared with patients can be overwhelming and cause anxiety due to lack of understanding. Health care organizations are attempting to improve transparency by providing patients with access to visit information. In this paper, we present our findings from a research study to evaluate patient understanding of medical images. We used cognitive fit theory to evaluate existing tools and images that are shared with patients and analyzed the relevance of such sharing. We discover that medical images need a lot of customization before they can be shared with patients. We suggest that new tools for medical imaging should be developed to fit the cognitive abilities of patients.


Assuntos
Cognição , Humanos
5.
J Digit Imaging ; 30(5): 602-608, 2017 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-28623557

RESUMO

Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry. This results in a fragmented view of the patient, and increases overhead to maintain such single purpose registry system by requiring an alternative data entry workflow and additional infrastructure to host and maintain multiple registries for different clinical needs. This complexity is magnified in the health care enterprise whereby radiology systems usually are run parallel to other clinical systems due to the different clinical workflow for radiologists. In the new era of value based care where data needs are increasing with demand for a shorter turnaround time to provide data that can be used for information and decision making, there is a critical gap to develop registries that are more adapt to the radiology workflow with minimal overhead on resources for maintenance and setup. We share our experience of developing and implementing an open source registry system for quality improvement and research in our academic institution that is driven by our radiology workflow.


Assuntos
Melhoria de Qualidade , Sistemas de Informação em Radiologia , Radiologia , Sistema de Registros , Fluxo de Trabalho , Humanos
6.
J Am Coll Radiol ; 14(2): 217-223, 2017 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-27475970

RESUMO

BACKGROUND: An increasing number of technologies allow providers to access the results of imaging studies. This study examined differences in access of radiology images compared with text-only reports through a health information exchange system by health care professionals. METHODS: The study sample included 157,256 historical sessions from a health information exchange system that enabled 1,670 physicians and non-physicians to access text-based reports and imaging over the period 2013 to 2014. The primary outcome was an indicator of access of an imaging study instead of access of a text-only report. Multilevel mixed-effects regression models were used to estimate the association between provider and session characteristics and access of images compared with text-only reports. RESULTS: Compared with primary care physicians, specialists had an 18% higher probability of accessing actual images instead of text-only reports (ß = 0.18; P < .001). Compared with primary care practice settings, the probability of accessing images was 4% higher for specialty care practices (P < .05) and 8% lower for emergency departments (P < .05). Radiologists, orthopedists, and neurologists accounted for 79% of all the sessions with actual images accessed. Orthopedists, radiologists, surgeons, and pulmonary disease specialists accessed imaging more often than text-based reports only. CONCLUSIONS: Consideration for differences in the need to access images compared with text-only reports based on the type of provider and setting of care are needed to maximize the benefits of image sharing for patient care.


Assuntos
Documentação/estatística & dados numéricos , Registros Eletrônicos de Saúde/estatística & dados numéricos , Acesso aos Serviços de Saúde/estatística & dados numéricos , Armazenamento e Recuperação da Informação/métodos , Radiologistas/estatística & dados numéricos , Sistemas de Informação em Radiologia/estatística & dados numéricos , Troca de Informação em Saúde/estatística & dados numéricos , Disseminação de Informação/métodos , New York , Revisão da Utilização de Recursos de Saúde
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