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Pragmatic Management of Patient Data and AI: A Position Paper for Medical Research

Transfer, AIH,

Artificial Intelligence (AI) offers various opportunities in medical research, but the use of patient data frequently raises privacy concerns. In a recent position paper, Dr. Carsten Marr, a scientist at Helmholtz Munich, and other authors address the challenges and opportunities in hematology and oncology, formulating policy recommendations.

The experts recommend the following measures to adjust legal frameworks and enhance technical infrastructure:

  1. Standardized digital health data documentation and interface provision.
  2. Incentives and resources for data collection, including medical primary caregivers.
  3. Powerful and secure technical infrastructure for processing large amounts of health data.
  4. Cross-border design of consent and data protection regulations that appropriately consider medical public welfare and facilitate access to data for research.

“Cutting-edge AI models hold the potential to predict diseases based on genetic data, classify cells, establish new correlations between illnesses and their potential causes, and lay the foundation for personalized therapies. It's crucial that policymakers prioritize the treatment of severe diseases and remove unnecessary barriers to data access for research. This is the key to unlocking the full potential of AI in medical research and improving healthcare,” says Dr. Carsten Marr, Director at the Institute of AI for Health at Helmholtz Munich.

In addition to Carsten Marr, Dr. Jan Moritz Middeke from the University Hospital Dresden, and Dr. Christian Pohlkamp from the Munich Leukemia Laboratory were the lead authors of the position paper. A total of 16 authors from various German research and healthcare institutions were involved.