Cargando…

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists

IMPORTANCE: Artificial intelligence (AI) can interpret abnormal signs in chest radiography (CXR) and generate captions, but a prospective study is needed to examine its practical value. OBJECTIVE: To prospectively compare natural language processing (NLP)-generated CXR captions and the diagnostic fi...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Yaping, Liu, Mingqian, Zhang, Lu, Wang, Lingyun, Zhao, Keke, Hu, Shundong, Chen, Xu, Xie, Xueqian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	American Medical Association 2023
Materias:	Original Investigation
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909497/ https://www.ncbi.nlm.nih.gov/pubmed/36753278 http://dx.doi.org/10.1001/jamanetworkopen.2022.55113

Ejemplares similares

Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing
por: Zhang, Yaping, et al.
Publicado: (2021)

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs
por: Li, Matthew D., et al.
Publicado: (2021)

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents
por: Wu, Joy T., et al.
Publicado: (2020)

Do pediatric intensivists and radiologists concur on the interpretation of chest radiographs?
por: Chambliss, C Robert, et al.
Publicado: (1998)

The impact of artificial intelligence on the reading times of radiologists for chest radiographs
por: Shin, Hyun Joo, et al.
Publicado: (2023)

Novel Method to Improve Radiologist Agreement in Interpretation of Serial Chest Radiographs in the ICU
por: Castro, Denise A, et al.
Publicado: (2015)

Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs
por: Gichoya, Judy W., et al.
Publicado: (2022)

Differentiation of Chest CT Findings Between Influenza Pneumonia and COVID-19: Interobserver Agreement Between Radiologists
por: Zarei, Fariba, et al.
Publicado: (2021)

Sign language in the CT suite: a new approach in communicating between radiologists and radiographers
por: Wang, Edward H., et al.
Publicado: (2016)

The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia
por: Rixe, Nancy, et al.
Publicado: (2023)

Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department
por: Huang, Jonathan, et al.
Publicado: (2023)

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
por: Ahn, Jong Seok, et al.
Publicado: (2022)

Deep Learning to Assess Long-term Mortality From Chest Radiographs
por: Lu, Michael T., et al.
Publicado: (2019)

The Radiologist as a Gatekeeper in Chest Pain
por: Pradella, Silvia, et al.
Publicado: (2021)

Understanding reasons for image rejection by radiologists and radiographers
por: Decoster, Robin, et al.
Publicado: (2022)

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
por: Rajpurkar, Pranav, et al.
Publicado: (2018)

Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs
por: Krishnamoorthy, Sabitha, et al.
Publicado: (2021)

Concordance Between Resident and Attending Radiologist in Reporting Pneumothorax on Intensive Care Unit and Emergency Room Chest Radiographs
por: Hafeez, Anam, et al.
Publicado: (2022)

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
por: Hillis, James M., et al.
Publicado: (2022)

Evaluation of the Occupational Doses of Interventional Radiologists
por: Kuipers, Gerritjan, et al.
Publicado: (2008)

Trends in Use of Daily Chest Radiographs Among US Adults Receiving Mechanical Ventilation
por: Gershengorn, Hayley B., et al.
Publicado: (2018)

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs
por: Yoo, Hyunsuk, et al.
Publicado: (2020)

Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network
por: Weiss, Jakob, et al.
Publicado: (2021)

Radiographers supporting radiologists in the interpretation of screening mammography: a viable strategy to meet the shortage in the number of radiologists
por: Torres-Mejía, Gabriela, et al.
Publicado: (2015)

Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists
por: Hong, Sungho, et al.
Publicado: (2023)

Anteroposterior chest radiograph vs. chest CT scan in early detection of pneumothorax in trauma patients
por: Omar, Hesham R, et al.
Publicado: (2011)

Imaging the Chest: The Chest Radiograph
por: Broder, Joshua
Publicado: (2011)

Comparison of the diagnostic performance of CT colonography interpreted by radiologists and radiographers
por: Lauridsen, Carsten, et al.
Publicado: (2013)

Postoperative radiograph of the hip arthroplasty: what the radiologist should know
por: Vanrusselt, Jan, et al.
Publicado: (2015)

Radiographic findings in COVID-19: Comparison between AI and radiologist
por: Sukhija, Arsh, et al.
Publicado: (2021)

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing
por: Crombé, Amandine, et al.
Publicado: (2022)

The Chest Radiograph
por: Kelly, Barry
Publicado: (2012)

Chest imaging using signs, symbols, and naturalistic images: a practical guide for radiologists and non-radiologists
por: Chiarenza, Alessandra, et al.
Publicado: (2019)

Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
por: Hwang, Eui Jin, et al.
Publicado: (2019)

Assessment of radiographers’ competences from the perspectives of radiographers and radiologists: a cross-sectional survey in Lithuania
por: Vanckavičienė, Aurika, et al.
Publicado: (2017)

Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists
por: Peters, Alan A., et al.
Publicado: (2021)

Chest radiograph classification and severity of suspected COVID-19 by different radiologist groups and attending clinicians: multi-reader, multi-case study
por: Nair, Arjun, et al.
Publicado: (2022)

Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs
por: Kim, Hwiyoung, et al.
Publicado: (2023)

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
por: Schaffter, Thomas, et al.
Publicado: (2020)

Chest CT Images for COVID-19: Radiologists and Computer-Based Detection
por: Dou, Qingli, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_s91s0gbcko61s17he07g0032gv