William F. Auffermann
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William F. Auffermann, MD, PhD

Languages spoken: English

Clinical Locations

Primary Location

University of Utah Hospital

Radiology
50 N Medical Dr
Salt Lake City , UT 84132
801-581-7553
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William Auffermann M.D., Ph.D., currently serves as the Vice Chair for IT and Informatics, Section Chief for the Cardiothoracic Imaging Section, and is a Professor in the Department of Radiology and Imaging Sciences at the University of Utah.

He is dual board certified in Diagnostic Radiology and Clinical Informatics. The major goals as Vice Chair for IT and Informatics are to improve informatics in the Department of Radiology and Imaging Sciences and University of Utah as a whole. He completed his fellowship in cardiothoracic imaging at Duke University and interprets chest and cardiac XRs, CTs, and MRs. He is also a certified NIOSH B-Reader for interpreting radiographs evaluating for occupational lung disease.

His research focuses on human factors engineering using our knowledge of medical image perception and perceptual errors to develop computer simulation based educational tools for medical image interpretation. This ongoing research project focuses on integrating human factors research/engineering with new simulation based methods for educating healthcare trainees and practitioners on the evaluation of diagnostic imaging studies. Human factors known to correlate with improved image interpretation and reduced diagnostic errors are incorporated into training algorithms using computer simulation. Subject performance improved as a function of training, with fewer diagnostic/medical errors. Using this paradigm, subjects were able to attain a higher level of proficiency at image interpretation, with fewer diagnostic/interpretive errors, in less training time that would be required for conventional educational methods. Additional research interests include: biomedical informatics, machine learning, structured reporting, and clinical decision support.

Specialties

Board Certification

American Board of Preventive Medicine (Clinical Informatics)
American Board of Radiology (Diagnostic Radiology)

William Auffermann M.D., Ph.D., currently serves as the Vice Chair for IT and Informatics, Section Chief for the Cardiothoracic Imaging Section, and is a Professor in the Department of Radiology and Imaging Sciences at the University of Utah.

He is dual board certified in Diagnostic Radiology and Clinical Informatics. The major goals as Vice Chair for IT and Informatics are to improve informatics in the Department of Radiology and Imaging Sciences and University of Utah as a whole. He completed his fellowship in cardiothoracic imaging at Duke University and interprets chest and cardiac XRs, CTs, and MRs. He is also a certified NIOSH B-Reader for interpreting radiographs evaluating for occupational lung disease.

His research focuses on human factors engineering using our knowledge of medical image perception and perceptual errors to develop computer simulation based educational tools for medical image interpretation. This ongoing research project focuses on integrating human factors research/engineering with new simulation based methods for educating healthcare trainees and practitioners on the evaluation of diagnostic imaging studies. Human factors known to correlate with improved image interpretation and reduced diagnostic errors are incorporated into training algorithms using computer simulation. Subject performance improved as a function of training, with fewer diagnostic/medical errors. Using this paradigm, subjects were able to attain a higher level of proficiency at image interpretation, with fewer diagnostic/interpretive errors, in less training time that would be required for conventional educational methods. Additional research interests include: biomedical informatics, machine learning, structured reporting, and clinical decision support.

Board Certification and Academic Information

Academic Departments Radiology & Imaging Sciences -Professor (Clinical)
Board Certification
American Board of Preventive Medicine (Clinical Informatics)
American Board of Radiology (Diagnostic Radiology)

Education history

Undergraduate Chemistry; Minor in Physics - Polytechnic University B.S.
Professional Medical Medicine; Biomedical Engineering, Minor in Statistics - University of Minnesota Medical School M.D., Ph.D.
Internship Transitional Year - Hennepin County Medical Center Intern
Residency Radiology - University of Minnesota Medical School Resident
Fellowship Cardiothoracic Radiology - Duke University School of Medicine Fellow

Selected Publications

Journal Article

  1. Banerjee S, Pham T, Eastaway A, Auffermann WF, Quigley EP 3r (2023). The Use of Virtual Reality in Teaching Three-Dimensional Anatomy and Pathology on CT. Journal of digital imaging, 36(3), 1279-1284.
  2. Zhu GG, Pham T, Banerjee S, Auffermann W (2023). Taking a second look and zooming out: does this help with abnormality detection in chest radiography?. Journal of medical imaging (Bellingham, Wash.), 10(Suppl 1), S11914.
  3. Auffermann WF, Mills M (2023). Perceptual training and teaching medical students how to window and level chest radiographs. Journal of medical imaging (Bellingham, Wash.), 10(Suppl 1), S11907.
  4. Banerjee S, Agarwal R, Auffermann W (2023). RADHunters: gamification in radiology perceptual education. Journal of medical imaging (Bellingham, Wash.), 10(Suppl 1), S11905.
  5. Lakhani P, Mongan J, Singhal C, Zhou Q, Andriole KP, Auffermann WF, Prasanna PM, Pham TX, Peterson M, Bergquist PJ, Cook TS, Ferraciolli SF, Corradi GCA, Takahashi MS, Workman CS, Parekh M, Kamel SI, Galant J, Mas-Sanchez A, Benítez EC, Sánchez-Valverde M, Jaques L, Panadero M, Vidal M, Culiañez-Casas M, Angulo-Gonzalez D, Langer SG, de la Iglesia-Vayá M, Shih (2023). The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs. Journal of digital imaging, 36(1), 365-372.
  6. Law N, Chan J, Kelly C, Auffermann WF, Dunn D (2022). Incidence of pulmonary embolism in COVID-19 infection in the ED: ancestral, Delta, Omicron variants and vaccines. Emergency radiology, 29(4), 625-629.
  7. Fawver B, Thomas JL, Drew T, Mills MK, Auffermann WF, Lohse KR, Williams A (2020). Seeing isn't necessarily believing: Misleading contextual information influences perceptual-cognitive bias in radiologists. Journal of experimental psychology. Applied, 26(4), 579-592.
  8. Bigolin Lanfredi R, Zhang M, Auffermann WF, Chan J, Duong PT, Srikumar V, Drew T, Schroeder JD, Tasdizen (2022). REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays. Scientific data, 9(1), 350.
  9. Banerjee S, Auffermann W (2021). RadSimPE - a Radiology Workstation Simulator for Perceptual Education. Journal of digital imaging, 34(4), 1059-1066.
  10. Williams LH, Carrigan AJ, Mills M, Auffermann WF, Rich AN, Drew (2021). Characteristics of expert search behavior in volumetric medical image interpretation. Journal of medical imaging (Bellingham, Wash.), 8(4), 041208.
  11. Auffermann WF, Drew T, Krupinski E (2020). Special Section Guest Editorial: Medical Image Perception and Observer Performance. Journal of medical imaging (Bellingham, Wash.), 7(2), 022401.
  12. Banerjee S, Drew T, Mills MK, Auffermann W (2020). Perceptual training: learning versus attentional shift. Journal of medical imaging (Bellingham, Wash.), 7(2), 022407.
  13. Auffermann W (2019). Automated Triaging of Adult Chest Radiographs. Radiology, 291(1), 203-204.
  14. Banerjee S, Agarwal R, Auffermann W (2023). RADHunters: gamification in radiology perceptual education. Journal of medical imaging (Bellingham, Wash.),
  15. Auffermann WF, Mills M (2023). Perceptual training and teaching medical students how to window and level chest radiographs. Journal of medical imaging (Bellingham, Wash.),
  16. Grace G. Zhu, Theresa Pham, Soham Banerjee, William F. Aufferman (2023). Taking a second look and zooming out: does this help with abnormality detection in chest radiography?. Journal of medical imaging (Bellingham, Wash.),
  17. Morris MF, Henry TS, Raptis CA, Amin AN, Auffermann WF, Hatten BW, Kelly AM, Lai AR, Martin MD, Sandler KL, Sirajuddin A, Surasi DS, Chung, J (2024). Workup of Pleural Effusion or Pleural Disease. Journal of the American College of Radiology, 21(6S),
  18. Kok EM, Niehorster DC, van der Gijp, Rutgers DR, Auffermann WF, van der Schaaf M, Kester L, van Gog (2024). The Effects of Gaze-display Feedback on Medical Students’ Self-monitoring and Learning in Radiology. Advances in Health Sciences Education - Theory and Practice,
  19. Zenger SK, Agarwal R, Auffermann W (2025). Using a limited field of view to improve training for pulmonary nodule detection on radiographs. Journal of medical imaging (Bellingham, Wash.), 12(5), 051804.
  20. Zahergivar A, Golagha M, Stoddard G, Anderson PS, Woods L, Newman A, Carter MR, Wang L, Ibrahim M, Chamberlin J, Auffermann WF, Kabakus I, Burt J (2024). Prognostic value of coronary artery calcium scoring in patients with non-small cell lung cancer using initial staging computed tomography. BMC medical imaging, 24(1), 350.
  21. Expert Panel on Thoracic Imaging, Morris MF, Henry TS, Raptis CA, Amin AN, Auffermann WF, Hatten BW, Kelly AM, Lai AR, Martin MD, Sandler KL, Sirajuddin A, Surasi DS, Chung J (2024). ACR Appropriateness Criteria® Workup of Pleural Effusion or Pleural Disease. Journal of the American College of Radiology, 21(6S), S343-S352.
  22. Kok EM, Niehorster DC, van der Gijp A, Rutgers DR, Auffermann WF, van der Schaaf M, Kester L, van Gog (2024). The effects of gaze-display feedback on medical students' self-monitoring and learning in radiology. Advances in health sciences education, 29(5), 1689-1710.

Review

  1. Chan J, Auffermann W (2022). Artificial Intelligence in the Imaging of Diffuse Lung Disease. Radiologic clinics of North America, 60(6), 1033-1040.
  2. Richardson ML, Adams SJ, Agarwal A, Auffermann WF, Bhattacharya AK, Consul N, Fotos JS, Kelahan LC, Lin C, Lo HS, Nguyen XV, Salkowski LR, Sin JM, Thomas RC, Wassef S, Ikuta (2021). Review of Artificial Intelligence Training Tools and Courses for Radiologists. Academic radiology, 28(9), 1238-1252.
  3. Degnan AJ, Ghobadi EH, Hardy P, Krupinski E, Scali EP, Stratchko L, Ulano A, Walker E, Wasnik AP, Auffermann W (2019). Perceptual and Interpretive Error in Diagnostic Radiology-Causes and Potential Solutions. Academic radiology, 26(6), 833-845.
  4. Auffermann WF, Gozansky EK, Tridandapani (2019). Artificial Intelligence in Cardiothoracic Radiology. AJR. American journal of roentgenology, 212(5), 997-1001.

Editorial

  1. Auffermann W (2023). AI Nodule Detection on Chest Radiographs Using Randomized Controlled Data: The Effect on Clinical Practice. Radiology, 307(2), e223186.
  2. Auffermann W (2021). Quantifying Pulmonary Edema on Chest Radiographs. Radiology. Artificial intelligence, 3(2), e210004.