This field of research is one of the most promising areas of health innovation since AI has the potential to increase levels of accuracy in detecting, diagnosing, and treating diseases.
Why did you choose to focus your research on the application of artificial intelligence to medical imaging?
Focusing on artificial intelligence (AI) in medical imaging allows me to use my knowledge from healthcare and combine it with my passion for computer science. This field of research is one of the most promising areas of health innovation since AI has the potential to increase levels of accuracy in detecting, diagnosing, and treating diseases. In this context, pursuing a career as a physician-scientist allows me to build on my experience as a clinical radiologist by transforming empirical observations into testable hypotheses and using state-of-the-art technology, such as AI, to translate research findings into actionable scientific progress. My main research focus is the application of AI in neuroradiology, the medical specialty that uses neuroimaging to study and diagnose brain and spine disorders, especially in patients with brain tumors. Focusing on brain tumors is of considerable interest, since recent advances in molecular characterization have led to a fundamental shift in the understanding of this disease. Consequently, numerous clinical trials are currently investigating the safety and effectiveness of new molecular targeted drugs. The assessment of these cost-intensive drugs is often based on biomarkers obtained from medical imaging, specifically from magnetic resonance imaging (MRI). But radiologists are still relying on manual visual interpretation to assess their MRI findings, which leads to a low validity and reproducibility for these biomarkers. Which is why my lab will keep directing its research efforts towards developing novel AI-based computational approaches for automated and quantitative high-throughput image analysis, to enable accurate and reproducible assessment of drug efficacy in brain tumor treatment and to help gear clinical practice towards precision oncology.
Is there a certain goal that you have set yourself in your research? What will the future of your research look like?
Our lab’s main goal is to establish an AI-based platform for next-generation imaging biomarker discovery in brain tumors. For this purpose, we will collaborate with national and international partners to apply, prospectively validate and refine existing AI solutions in clinical trials with brain tumor patients, to assess the safety and efficacy of novel molecular guided treatments. Successful deployment is particularly important because it could ultimately reduce the cost of conducting clinical trials on brain tumors and facilitate the availability of new effective therapies. On a more general note, regarding the future role of AI in medical imaging, I think it is important to recognize that the goal of AI is to take care of tedious and repetitive tasks, allowing radiologists to focus on more high-value tasks and patient-oriented roles.
What do you do to unwind from work? How do you relax?
I try to spend as much time as I can with my family. Moreover, as a native of Austria I particularity enjoy road cycling and skiing in the alps.
What do you like about your research environment?
Heidelberg University Hospital and the partner institutes on the campus such as the German Cancer Research Center (DKFZ) and the National Center for Tumor Diseases (NCT) offer a unique infrastructure when it comes to the development of innovative technologies and their clinical application. This environment has been of vital importance for my research and professional development as a physician-scientist.
How could the research conditions be improved for young scientists?
Physician-scientists who practice medicine while engaging in scientific research are often struggling to juggle both their clinical and research responsibilities. In this context it is of key importance to have dedicated third-party funding opportunities for physician-scientists, both during their training and the early stages of their career, which allows them to have protected research time where they can entirely focus on their research activities.
© Tobias Schwerdt