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Clin Imaging ; 81: 87-91, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34655997


OBJECTIVE: To investigate how patients experience a radiologist-patient consultation of imaging findings directly after neck ultrasonography (US), and how much time this consumes. MATERIALS AND METHODS: This prospective randomized study included 109 consecutive patients who underwent neck US, of whom 44 had a radiologist-patient consultation of US results directly after the examination, and 65 who had not. RESULTS: The median ratings of all healthcare quality metrics (friendliness of the radiologist, explanation of the radiologist, skill of the radiologist, radiologist's concern for comfort during the examination, radiologist's concern for patient questions/worries, overall rating of the examination, and likelihood of recommending the examination) were either good/high or very good/very high, without any significant differences between both patient groups. Patients who did not discuss the US results with the radiologist, were significantly more worried during the examination (P = 0.040) and had significantly higher anxiety levels after completion of the US examination (P = 0.027) than patients who discussed the US results with the radiologist. Fifty-one out of 55 responding patients (92.7%) indicated a radiologist-patient consultation of US results to be important. The median duration of US examinations that included a radiologist-patient consultation of US results was 7.57 min (range: 5.15-12.10 min), while the median duration of US examinations without a radiologist-patient consultation of US results was 7.34 min (range: 3.45-14.32 min), without any significant difference (P = 0.637). CONCLUSION: A radiologist-patient consultation of imaging findings after neck US decreases patient anxiety, is desired by most patients, and does not significantly prolong total examination time.

Radiologia , Humanos , Estudos Prospectivos , Radiologistas , Encaminhamento e Consulta , Ultrassonografia
Clin Imaging ; 81: 98-102, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34678654


Disparities exist in access to a multitude of screening and diagnostic imaging examinations and procedures. To address these disparities within radiology, emphasis so far has been placed upon diversifying the workforce and formally educating trainees on healthcare disparities. Currently, there is no organized and nationally accepted educational program or content for practicing radiologists specific to diversity and healthcare disparity. This void can be addressed by providing an educational curriculum framework for practicing radiologists based on three key factors: individual efforts, calling for institutional change, and national collaboration. Individual efforts should focus on acknowledging the existence of disparities, understanding the contribution of one's implicit bias in perpetuating disparities, understanding and highlighting issues related to insurance coverage of radiology examinations, and participating in radiology political action committees. These efforts can be facilitated by a consolidated web-based training program for practicing radiologists. To pave the way for meaningful systemic change, the implementation of institutional change like that initiated by the Culture of Safety movement in 2002 is needed. A national collaborative effort initiated by radiology organizations to empower radiologists and recognize positive changes would further provide support. SUMMARY: A three-pronged educational framework combining individual radiologist education, institutional change, and national collaboration will enable radiologists to play a role in addressing imaging-related disparities in healthcare.

Disparidades em Assistência à Saúde , Radiologia , Currículo , Humanos , Radiografia , Radiologistas , Radiologia/educação
Clin Imaging ; 81: 67-71, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34619566


PURPOSE: International student surveys have shown significant anxiety about pursuing radiology as a career due to artificial intelligence (AI). For a counterpart study in the US, we examined the impact of AI on US medical students' choice of radiology as a career, and how such impact is influenced by students' opinions on and exposures to AI and radiology. METHODS: Students across 32 US medical schools participated in an anonymous online survey. The respondents' radiology ranking with and without AI were compared. Among those considering radiology within their top 3 choices, change in radiology ranking due to AI was statistically examined for association with baseline characteristics, subjective opinions, and prior exposures. RESULTS: AI significantly lowered students' preference for ranking radiology (P < .001). One-sixth of students who would have chosen radiology as the first choice did not do so because of AI, and approximately half of those considering radiology within their top 3 choices remained concerned about AI. Ranking radiology lower due to AI was associated with greater concerns about AI (P < .001), less perceived understanding of radiology (P = .02), predicting a decrease in job opportunities (P < .001), and exposure to AI through medical students/family (P = .03) as well as through radiology attendings and residents (P = .03). Education on AI during radiology rotations, followed by pre-clinical lectures, was the most preferred way to learn about AI. CONCLUSION: AI has a significantly negative impact on US medical students' choice of radiology as a career, a phenomenon influenced by both individual concerns and exposure to AI from the medical community.

Radiologia , Estudantes de Medicina , Inteligência Artificial , Humanos , Radiografia , Inquéritos e Questionários
Rev. colomb. anestesiol ; 49(4): e400, Oct.-Dec. 2021. graf
Artigo em Inglês | LILACS, COLNAL | ID: biblio-1341243


The accompanying images demonstrate giant pulmonary artery aneurysms in a patient with idiopathic pulmonary arterial hypertension (Image 1). In addition to the main pulmonary artery, both the left and right pulmonary arteries are aneurysmal and are compressing the lung parenchyma (Image 2).

Las imágenes adjuntas muestran aneurismas gigantes de la arteria pulmonar en un paciente con hipertensión arterial pulmonar idiopática (Imagen 1). Además de la arteria pulmonar principal, tanto la arteria pulmonar izquierda como la derecha son aneurismáticas y están comprimiendo el parénquima pulmonar (Imagen 2).

Humanos , Artéria Pulmonar , Radiologia , Aneurisma , Hipertensão Pulmonar Primária Familiar , Tecido Parenquimatoso , Cardiopatias Congênitas
BMC Med Inform Decis Mak ; 21(Suppl 9): 247, 2021 11 16.
Artigo em Inglês | MEDLINE | ID: mdl-34789213


BACKGROUND: Standardized coding of plays an important role in radiology reports' secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research. METHODS: We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese-English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms. RESULTS: The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup. CONCLUSIONS: The paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding.

Sistemas de Informação em Radiologia , Radiologia , Algoritmos , China , Humanos , Idioma , Processamento de Linguagem Natural
Rofo ; 193(11): 1343, 2021 Nov.
Artigo em Alemão | MEDLINE | ID: mdl-34710916
Rofo ; 193(11): 1352, 2021 Nov.
Artigo em Alemão | MEDLINE | ID: mdl-34710923

Radiologia , Radiografia
Rofo ; 193(11): 1356, 2021 Nov.
Artigo em Alemão | MEDLINE | ID: mdl-34710928
Rofo ; 193(11): 1358-1359, 2021 Nov.
Artigo em Alemão | MEDLINE | ID: mdl-34710931

Radiologia , Radiografia
Radiol Clin North Am ; 59(6): 1045-1052, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34689872


The radiology reporting process is beginning to incorporate structured, semantically labeled data. Tools based on artificial intelligence technologies using a structured reporting context can assist with internal report consistency and longitudinal tracking. To-do lists of relevant issues could be assembled by artificial intelligence tools, incorporating components of the patient's history. Radiologists will review and select artificial intelligence-generated and other data to be transmitted to the electronic health record and generate feedback for ongoing improvement of artificial intelligence tools. These technologies should make reports more valuable by making reports more accessible and better able to integrate into care pathways.

Inteligência Artificial , Diagnóstico por Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Radiologia/métodos , Humanos
Radiol Clin North Am ; 59(6): 1053-1062, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34689873


Artificial intelligence (AI) and informatics promise to improve the quality and efficiency of diagnostic radiology but will require substantially more standardization and operational coordination to realize and measure those improvements. As radiology steps into the AI-driven future we should work hard to identify the needs and desires of our customers and develop process controls to ensure we are meeting them. Rather than focusing on easy-to-measure turnaround times as surrogates for quality, AI and informatics can support more comprehensive quality metrics, such as ensuring that reports are accurate, readable, and useful to patients and health care providers.

Inteligência Artificial , Melhoria de Qualidade , Radiologia/normas , Humanos
Radiol Clin North Am ; 59(6): 1063-1074, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34689874


Although recent scientific studies suggest that artificial intelligence (AI) could provide value in many radiology applications, much of the hard engineering work required to consistently realize this value in practice remains to be done. In this article, we summarize the various ways in which AI can benefit radiology practice, identify key challenges that must be overcome for those benefits to be delivered, and discuss promising avenues by which these challenges can be addressed.

Inteligência Artificial/normas , Diagnóstico por Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Radiologia/métodos , Radiologia/normas , Diagnóstico por Imagem/normas , Humanos , Interpretação de Imagem Assistida por Computador/normas , Reprodutibilidade dos Testes , Software
Radiol Clin North Am ; 59(6): 1075-1083, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34689875


Artificial intelligence technology promises to redefine the practice of radiology. However, it exists in a nascent phase and remains largely untested in the clinical space. This nature is both a cause and consequence of the uncertain legal-regulatory environment it enters. This discussion aims to shed light on these challenges, tracing the various pathways toward approval by the US Food and Drug Administration, the future of government oversight, privacy issues, ethical dilemmas, and practical considerations related to implementation in radiologist practice.

Inteligência Artificial/legislação & jurisprudência , Diagnóstico por Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Radiologia/legislação & jurisprudência , Diagnóstico por Imagem/normas , Humanos , Interpretação de Imagem Assistida por Computador/normas , Estados Unidos , United States Food and Drug Administration
Radiol Clin North Am ; 59(6): 1085-1095, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34689876


No one knows what the paradigm shift of artificial intelligence will bring to medical imaging. In this article, we attempt to predict how artificial intelligence will impact radiology based on a critical review of current innovations. The best way to predict the future is to anticipate, prepare, and create it. We anticipate that radiology will need to enhance current infrastructure, collaborate with others, learn the challenges and pitfalls of the technology, and maintain a healthy skepticism about artificial intelligence while embracing its potential to allow us to become more productive, accurate, secure, and impactful in the care of our patients.

Inteligência Artificial/tendências , Diagnóstico por Imagem/métodos , Diagnóstico por Imagem/tendências , Interpretação de Imagem Assistida por Computador/métodos , Radiologia/métodos , Radiologia/tendências , Humanos