Prof. Dr. Christoph Lippert

Evaluation of medical AI applications

Evaluation of machine learning approaches for personalized medicine

Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.

Wilkinson J, Arnold K, Murray EJ, van Smeden M, Carr K, Sippy R, de Kamps M, Beam A, Konigorski S, Lippert C, Gilthorpe M, Tennant P (2020). It is time to reality check the promises of machine learning-powered precision medicine. The Lancet Digital Health 2: e677–80.https://doi.org/10.1016/S2589-7500(20)30200-4.


Computer-aided digital chest radiograph (CXR) interpretation

Computer-aided digital chest radiograph (CXR) interpretation can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based screening has been limited. We applied an automated image interpretation algorithm, CAD4TBv5, prospectively in an HIV-endemic area. We estimated the performance of CAD4TBv5 for triaging (identifying lung field abnormality as a criteria for sputum examination) and diagnosis (detection of active TB as defined by microbiologic (M+) or radiologic (R+) gold standards).

Fehr J, Konigorski S, Olivier S, et al. (2021). Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa. npj Digital Medicine  4: 106.https://doi.org/10.1038/s41746-021-00471-y.