Literature DB >> 19155396

Workforce shortages in breast imaging: impact on mammography utilization.

Paul Wing1, Margaret H Langelier.   

Abstract

OBJECTIVE: The objective of this study was to develop reliable forecasts of the future supply of radiologists and radiologic technologists practicing mammography under different assumptions about future introduction of new practitioners. In addition, this article includes basic mammography workforce statistics to provide a context for the forecasts.
MATERIALS AND METHODS: The forecasts were developed using an age cohort flow model based on data provided by the American College of Radiology (ACR) on the numbers and age distribution of radiologists and on data provided by the American Society of Radiologic Technologists (ASRT) on radiologic technologists providing mammography services.
RESULTS: The forecasts show that the current rates of production of new mammography professionals will result in dramatic reductions in mammography professionals per woman age 40 years old and older over the next 15-20 years.
CONCLUSION: Unless the number of new mammography professionals entering practice every year increases beyond the current levels, there will be a growing gap between the supply of and demand for mammography professionals over the next two decades.

Entities:  

Mesh:

Year:  2009        PMID: 19155396     DOI: 10.2214/AJR.08.1665

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  12 in total

1.  Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Authors:  Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Tao Tan; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ritse M Mann; Ioannis Sechopoulos
Journal:  J Natl Cancer Inst       Date:  2019-09-01       Impact factor: 13.506

2.  External validation of AI algorithms in breast radiology: the last healthcare security checkpoint?

Authors:  Teodoro Martin-Noguerol; Antonio Luna
Journal:  Quant Imaging Med Surg       Date:  2021-06

3.  Breast cancer early detection: A phased approach to implementation.

Authors:  Ophira Ginsburg; Cheng-Har Yip; Ari Brooks; Anna Cabanes; Maira Caleffi; Jorge Antonio Dunstan Yataco; Bishal Gyawali; Valerie McCormack; Myrna McLaughlin de Anderson; Ravi Mehrotra; Alejandro Mohar; Raul Murillo; Lydia E Pace; Electra D Paskett; Anya Romanoff; Anne F Rositch; John R Scheel; Miriam Schneidman; Karla Unger-Saldaña; Verna Vanderpuye; Tsu-Yin Wu; Safina Yuma; Allison Dvaladze; Catherine Duggan; Benjamin O Anderson
Journal:  Cancer       Date:  2020-05-15       Impact factor: 6.860

4.  Variations in breast cancer detection rates during mammogram-reading sessions: does experience have an impact?

Authors:  Abdulaziz S Alshabibi; Moayyad E Suleiman; Salman M Albeshan; Robert Heard; Patrick C Brennan
Journal:  Br J Radiol       Date:  2021-11-04       Impact factor: 3.039

Review 5.  AI-enhanced breast imaging: Where are we and where are we heading?

Authors:  Almir Bitencourt; Isaac Daimiel Naranjo; Roberto Lo Gullo; Carolina Rossi Saccarelli; Katja Pinker
Journal:  Eur J Radiol       Date:  2021-07-30       Impact factor: 4.531

Review 6.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

Review 7.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2020-06-19

8.  DeepCAT: Deep Computer-Aided Triage of Screening Mammography.

Authors:  Paul H Yi; Dhananjay Singh; Susan C Harvey; Gregory D Hager; Lisa A Mullen
Journal:  J Digit Imaging       Date:  2021-01-11       Impact factor: 4.056

9.  A machine learning model based on readers' characteristics to predict their performances in reading screening mammograms.

Authors:  Ziba Gandomkar; Sarah J Lewis; Tong Li; Ernest U Ekpo; Patrick C Brennan
Journal:  Breast Cancer       Date:  2022-02-05       Impact factor: 3.307

10.  International evaluation of an AI system for breast cancer screening.

Authors:  Scott Mayer McKinney; Marcin Sieniek; Varun Godbole; Jonathan Godwin; Natasha Antropova; Hutan Ashrafian; Trevor Back; Mary Chesus; Greg S Corrado; Ara Darzi; Mozziyar Etemadi; Florencia Garcia-Vicente; Fiona J Gilbert; Mark Halling-Brown; Demis Hassabis; Sunny Jansen; Alan Karthikesalingam; Christopher J Kelly; Dominic King; Joseph R Ledsam; David Melnick; Hormuz Mostofi; Lily Peng; Joshua Jay Reicher; Bernardino Romera-Paredes; Richard Sidebottom; Mustafa Suleyman; Daniel Tse; Kenneth C Young; Jeffrey De Fauw; Shravya Shetty
Journal:  Nature       Date:  2020-01-01       Impact factor: 49.962

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