William F. Auffermann

William F. Auffermann, MD, PhD

Languages spoken: English

Clinical Locations

University of Utah Hospital

Radiology
Salt Lake City
801-581-7553

  • William Auffermann M.D., Ph.D., currently serves as the Interim Section Chief for the Thoracic Imaging Section in the Department of Radiology and Imaging Sciences at the University of Utah. He is dual board certified in Diagnostic Radiology and Clinical Informatics. His research focuses on 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 Interim Section Chief for the Thoracic Imaging Section in the Department of Radiology and Imaging Sciences at the University of Utah. He is dual board certified in Diagnostic Radiology and Clinical Informatics. His research focuses on 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 -Primary
    Board Certification
    American Board of Preventive Medicine (Clinical Informatics)
    American Board of Radiology (Diagnostic Radiology)

    Education history

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

    Selected Publications

    Journal Article

    1. Banerjee S, Pham T, Eastaway A, Auffermann WF, Quigley EP 3rd (2023). The Use of Virtual Reality in Teaching Three-Dimensional Anatomy and Pathology on CT. J Digit Imaging, 36(3), 1279-1284. (Read full article)
    2. 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, Bentez EC, Snchez-Valverde M, Jaques L, Panadero M, Vidal M, Culiaez-Casas M, Angulo-Gonzalez D, Langer SG, de la Iglesia-Vay M, Shih G (2022). The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs. J Digit Imaging, 36(1), 365-372. (Read full article)
    3. Banerjee S, Agarwal R, Auffermann WF (2023). RADHunters: gamification in radiology perceptual education. J Med Imaging (Bellingham), 10(Suppl 1), S11905. (Read full article)
    4. Auffermann WF, Mills MK (2023). Perceptual training and teaching medical students how to window and level chest radiographs. J Med Imaging (Bellingham), 10(Suppl 1), S11907. (Read full article)
    5. Zhu GG, Pham T, Banerjee S, Auffermann WF (2023). Taking a second look and zooming out: does this help with abnormality detection in chest radiography? J Med Imaging (Bellingham), 10(Suppl 1), S11914. (Read full article)
    6. Law N, Chan J, Kelly C, Auffermann WF, Dunn DP (2022). Incidence of pulmonary embolism in COVID-19 infection in the ED: ancestral, Delta, Omicron variants and vaccines. Emerg Radiol, 29(4), 625-629. (Read full article)
    7. Bigolin Lanfredi R, Zhang M, Auffermann WF, Chan J, Duong PT, Srikumar V, Drew T, Schroeder JD, Tasdizen T (2022). REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays. Sci Data, 9(1), 350. (Read full article)
    8. Banerjee S, Auffermann WF (2021). RadSimPE - a Radiology Workstation Simulator for Perceptual Education. J Digit Imaging, 34(4), 1059-1066. (Read full article)
    9. Williams LH, Carrigan AJ, Mills M, Auffermann WF, Rich AN, Drew T (2021). Characteristics of expert search behavior in volumetric medical image interpretation. J Med Imaging (Bellingham), 8(4), 041208. (Read full article)
    10. Fawver B, Thomas JL, Drew T, Mills MK, Auffermann WF, Lohse KR, Williams AM (2020). Seeing isn't necessarily believing: Misleading contextual information influences perceptual-cognitive bias in radiologists. J Exp Psychol Appl, 26(4), 579-592. (Read full article)
    11. Banerjee S, Drew T, Mills MK, Auffermann WF (2019). Perceptual training: learning versus attentional shift. J Med Imaging (Bellingham), 7(2), 022407. (Read full article)
    12. Auffermann WF, Drew T, Krupinski EA (2020). Special Section Guest Editorial: Medical Image Perception and Observer Performance. J Med Imaging (Bellingham), 7(2), 022401. (Read full article)
    13. Auffermann WF (2019). Automated Triaging of Adult Chest Radiographs. Radiology, 291(1), 203-204. (Read full article)

    Review

    1. Chan J, Auffermann WF (2022). Artificial Intelligence in the Imaging of Diffuse Lung Disease. [Review]. Radiol Clin North Am, 60(6), 1033-1040. (Read full article)
    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 I (2021). Review of Artificial Intelligence Training Tools and Courses for Radiologists. [Review]. Acad Radiol, 28(9), 1238-1252. (Read full article)
    3. Degnan AJ, Ghobadi EH, Hardy P, Krupinski E, Scali EP, Stratchko L, Ulano A, Walker E, Wasnik AP, Auffermann WF (2018). Perceptual and Interpretive Error in Diagnostic Radiology-Causes and Potential Solutions. [Review]. Acad Radiol, 26(6), 833-845. (Read full article)
    4. Auffermann WF, Gozansky EK, Tridandapani S (2019). Artificial Intelligence in Cardiothoracic Radiology. [Review]. AJR Am J Roentgenol, 212(5), 997-1001. (Read full article)

    Editorial

    1. Auffermann WF (2023). AI Nodule Detection on Chest Radiographs Using Randomized Controlled Data: The Effect on Clinical Practice. Radiology, 307(2), e223186. (Read full article)
    2. Auffermann WF (2021). Quantifying Pulmonary Edema on Chest Radiographs. Radiol Artif Intell, 3(2), e210004. (Read full article)

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