Home | News

31.07.2026

Teaser image to Benedikt Wiestler: We Want to Build a Time Machine

Benedikt Wiestler: We Want to Build a Time Machine

Research Film

What if you could pick the best cancer treatment for a patient before therapy even begins?

«I want to take these models from the lab and bring them into clinical practice.»


Benedict Wiestler

MCML PI

MCML PI Benedikt Wiestler is tackling one of the hardest problems in neuro-oncology: how to target tumor cells that imaging cannot detect. Brain tumors don’t grow as a solid mass. They diffusely infiltrate surrounding brain tissue, and it’s this invisible spread that current radiotherapy can only approximate. In his research, AI models are used to visualize tumor infiltration and simulate individual treatment outcomes, with the goal of bringing personalized radiotherapy strategies into clinical trials.

In this video series, discover how MCML researchers are bridging theory and practice to build AI systems that prioritize accessibility, sovereignty, and impact.

The film was produced and edited by Nicole Huminski and Nikolai Huber.

#blog #research #wiestler

Related

Tiny logo
Link to MCML at IJCAI-ECAI 2026

14.08.2026

MCML at IJCAI-ECAI 2026

MCML researchers are represented with 1 paper at IJCAI-ECAI 2026.

Read more
Tiny logo
Link to MCML at UAI 2026

14.08.2026

MCML at UAI 2026

MCML researchers are represented with 9 papers at UAI 2026.

Read more
Link to Fabian Theis Receives 2026 BBAW Academy Award

13.08.2026

Fabian Theis Receives 2026 BBAW Academy Award

Fabian Theis receives the 2026 BBAW Academy Award for pioneering work at the intersection of AI, machine learning, and biomedicine.

Read more
Tiny logo
Link to MCML at KDD 2026

07.08.2026

MCML at KDD 2026

MCML researchers are represented with 5 papers at KDD 2026.

Read more
Link to Gaps in the Benchmark: Why Medical AI Fails in the Real World

28.07.2026

Gaps in the Benchmark: Why Medical AI Fails in the Real World

MCML researchers show in Nature Health how dynamic red teaming exposes hidden weaknesses in Medical AI beyond static benchmarks.

Read more
Back to Top