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Division of Intelligent Medical Systems

Prof. Dr. Lena Maier-Hein

© dkfz.de

Artificial intelligence (AI) is set to revolutionize various areas of everyday life. In healthcare, however, the successful application and integration of AI is subject to a number of domain-specific challenges which can only be solved in a concerted effort that involves all relevant stakeholders, from clinicians to patients to health providers.
The hypothesis of our research at the Division of Intelligent Medical Systems is that by systematically addressing the domain-specific challenges AI faces in healthcare, AI applications can be developed that successfully elevate the clinical safety, efficiency, and quality in oncology. Committed to the goal of creating both immediate and long-lasting benefit for patients as well as clinical staff, the division closely collaborates with a number of clinical practicioners from different specialties.
A particular focus of our research lies in the surgical application domain. Currently, surgical success in oncology still strongly depends on the performing surgical team’s level of experience. Lesser experience is associated with higher rates of complication, a large proportion of which could be avoided. Our research seeks to consistently elevate and level the field through the development of AI-powered intelligent systems for interventional cancer care.

Research
Team Philosophy

Contact

Prof. Dr. Lena Maier-Hein
Intelligent Medical Systems (E130)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 223
69120 Heidelberg
Tel: +49 6221 42 2354

Selected Publications

  • Maier-Hein L / Reinke A, et al. Metrics reloaded: Recommendations for image analysis validation. Nature Methods 21, 2024
  • Ayala L / Adler T, et al., Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Science Advances 9(10), 2023
  • Maier-Hein L, et al. Surgical data science - from concepts toward clinical translation. Medical Image Analysis 76:102306, 2022
  • Ardizzone L, et al. Analyzing inverse problems with invertible neural networks. International Conference on Learning Representations (ICLR) 2019
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