| Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures A Imle, P Kumberger, ND Schnellbächer, J Fehr, P Carrillo-Bustamante, ... Nature communications 10 (1), 2144, 2019 | 73 | 2019 |
| Ml4h auditing: From paper to practice L Oala, J Fehr, L Gilli, P Balachandran, AW Leite, S Calderon-Ramirez, ... Machine learning for health, 280-317, 2020 | 54* | 2020 |
| Machine learning for health: algorithm auditing & quality control L Oala, AG Murchison, P Balachandran, S Choudhary, J Fehr, AW Leite, ... Journal of medical systems 45, 1-8, 2021 | 48 | 2021 |
| Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa J Fehr, S Konigorski, S Olivier, R Gunda, A Surujdeen, D Gareta, T Smit, ... NPJ digital medicine 4 (1), 106, 2021 | 42 | 2021 |
| Piloting a survey-based assessment of transparency and trustworthiness with three medical AI tools J Fehr, G Jaramillo-Gutierrez, L Oala, MI Gröschel, M Bierwirth, ... Healthcare 10 (10), 1923, 2022 | 7 | 2022 |
| CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities J Fehr, R Gunda, MJ Siedner, W Hanekom, A Grant, C Lippert, EB Wong The International Journal of Tuberculosis and Lung Disease 27 (2), 157-160, 2023 | 6 | 2023 |
| A causal framework for assessing the transportability of clinical prediction models J Fehr, M Piccininni, T Kurth, S Konigorski medRxiv, 2022.03. 01.22271617, 2022 | 4 | 2022 |
| Assessing the transportability of clinical prediction models for cognitive impairment using causal models J Fehr, M Piccininni, T Kurth, S Konigorski BMC Medical Research Methodology 23 (1), 187, 2023 | 3 | 2023 |
| A Trustworthy AI Reality-Check: The Lack of Transparency of Artificial Intelligence Products in Healthcare J Fehr, B Citro, R Malpani, C Lippert, VI Madai Frontiers in Digital Health 6, 1267290, 2024 | 2 | 2024 |
| The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation VK Bürger, J Amann, CKT Bui, J Fehr, VI Madai Frontiers in Digital Health 6, 1279629, 2024 | 1 | 2024 |
| Similar performances but markedly different triaging thresholds in three CAD4TB versions risk systematic errors in TB screening programs J Fehr, EB Wong medRxiv, 2022.04. 29.22274472, 2022 | 1 | 2022 |
| Simultaneous alleviation of verification and reference standard biases in a community-based tuberculosis screening study using Bayesian latent class analysis AK Keter, F Vanobberghen, L Lynen, A Van Heerden, J Fehr, S Olivier, ... Plos one 19 (6), e0305126, 2024 | | 2024 |
| Simulating rigid head motion artifacts on brain magnitude MRI data–Outcome on image quality and segmentation of the cerebral cortex H Olsson, JM Millward, L Starke, T Gladytz, T Klein, J Fehr, WC Lai, ... Plos one 19 (4), e0301132, 2024 | | 2024 |
| Heterogeneous Medical Data Integration with Multi-Source StyleGAN WC Lai, M Kirchler, H Yassin, J Fehr, A Rakowski, H Olsson, L Starke, ... Medical Imaging with Deep Learning, 2024 | | 2024 |
| 7 Data Science für Digitale Medizin J Fehr, S Konigorski, C Lippert Digitale Medizin: Kompendium für Studium und Praxis. Mit einem Geleitwort …, 2020 | | 2020 |
| Evaluating Age-Related Anatomical Consistency in Synthetic Brain MRI against Real-World Alzheimer's Disease Data. H Yassin, J Fehr, WC Lai, A Krichevsky, A Rakowski, C Lippert Medical Imaging with Deep Learning, 0 | | |
| Characterizing inflammation in sub-clinical TB disease L Ndlovu, J Fehr, T Zulu, S Olivier, D Gareta, K Baisley, T Ndung’u, ... | | |
