Friedrich, Christoph M.

Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms

Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., et al. (2020) · JAMA Network Open

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 409

Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches

Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 233

Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning

Bloch, L., Friedrich, C. M., & Initiative, F. T. A. D. N. (2021) · Alzheimer's Research & Therapy

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 68

Client-Focused Security Assessment of mHealth Apps and Recommended Practices to Prevent or Mitigate Transport Security Issues

Müthing, J., Jäschke, T., & Friedrich, C. M. (2017) · JMIR mHealth and uHealth

Mobile Health and mHealth ApplicationsWeb Application Security Vulnerabilities

Cited by 36

Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

Pelka, O., Friedrich, C. M., Nensa, F., Mönninghoff, C., Bloch, L., Jöckel, K.-H., Schramm, S., Sanchez Hoffmann, S., Winkler, A., Weimar, C., et al. (2020) · PLoS ONE

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 22