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Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI) including Machine Learning (ML) have the controversial potential to replace healthcare professionals in their jobs. While AGI does not exist to date, ANI/ML technology already has the capacity to augment, educate and democratize expertise for medical front-line personnel, freeing up their time for difficult cases while making them better at their tasks in modern precision medicine. 

ANI/ML can make the quality of an individual’s work better, resulting in better decision making in healthcare, faster diagnosis, improved patient communication, more efficient  training and less mistakes. The Chair’s main aim  is to find optimal interfaces between humans and ANI/ML systems that lead to broad acceptance and massively improved patient journeys.

Members of the health data science lab are strong believers in the human-in-the-loop paradigm. Interactive feedback mechanisms between real-time computer algorithms and humans are essential. Eventually this discipline aims to augment human decision-making abilities far beyond our currently limited horizon, especially for patient-specific treatment planning and in critical domains where decisions can determine the difference between life and death.

Friedrich-Alexander-Universität Erlangen-Nürnberg