Hasso-Plattner-Institut
Prof. Dr. Christoph Lippert
 

Jana Fehr

Research Assistant, PhD Candidate

Phone: +49-(0)331 5509-4875


Room: G-2.1.34


Email: jana.fehr(at)hpi.de

Research Interest

Auditing medical AI algorithms in real-world healthcare application settings under the the lens of medical applicability, bias & fairness, external validity, transparency and trustworthiness

 

Publications

  • Fehr, J., Konigorski, S., Olivier, S., Gunda, R., Surujdeen, A., et al. Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa. npj Digit. Med.4, 106 (2021). https://doi.org/10.1038/s41746-021-00471-y   and medRxiv, doi: https://doi.org/10.1101/2020.09.04.20188045.

  • Fehr, J., Jaramillo-Gutierrez, G., Oala, L., Gröschel, M. I., Bierwirth, M., Balachandran, P., Werneck-Leite, A., & Lippert, C. Piloting A Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools. Healthcare, 10(10) (2022). https://doi.org/10.3390/healthcare10101923

  • Fehr, J., Piccinnini, M., Kurth, T., Konigorski, S. A causal framework for assessing the transportability of clinical prediction models. medRxiv, doi: https://doi.org/10.1101/2022.03.01.22271617 (2022)

  • Oala, L., Fehr, J., Gilli, L., Calderon-ramirez, S., Li, D. X., et al. ML4H Auditing : From Paper to Practice. Proc. Mach. Learn. Res.136, 281–317, (2020).

  • Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., et al. Machine Learning for Health : Algorithm Auditing & Quality Control. J. Med. Syst.45, 1–8, doi: 10.1007/s10916-021-01783-y (2021).

  • Fehr, J., Konigorski, S., and Lippert, C.. Data Science für Digitale Medizin - Buchkapitel in: Digitale Medizin – Kompendium für Studium und Praxis, Medizinisch Wissenschaftliche Verlagsgesellschaft Berlin, (2020); ISBN-10 3-95466-538-7, ISBN-13 978-3-95466-538-9

Research Projects

  • Analysing the applicability of automated chest x-ray reading with CAD4TB to detect tuberculosis in a population screening program in rual South Africa. (Fehr et al. 2021 in npJ Digital Medicine)
  • Assessing the degree of compliance to transparent reporting and trustworthy AI guidelines (Fehr et al. 2022 in Healthcare)
  • Assessing the transportability of machine learning algorithms
    • using a causal framework (Fehr et al. 2022, medRxiv, currently under review)
    • using synthetic data

Project Management

  • 'Syreal' a BMBF-funded consortium project with 7 project partners. Aims to synthesize realistic medical images to mitigate shortcomings in medical AI applications.
  • 'Empower', a citizen-centered App to display personal health risks: MSc Project SS2019
  • 'Model Zoo', to store and retrieve trained deep learning models for medical applications: MSc Project SS2020