Life sciences and artificial intelligence

Artificial intelligence is a ubiquitous term today.It is a subfield of computer science and deals with the ability of computer systems or machines to perform tasks that normally require human intelligence.This includes "simple" applications such as image processing and speech recognition, but also more complex tasks such as analyzing data, drawing conclusions, planning and making decisions, and even autonomous action.AI methods are also increasingly being used in the life sciences.In medicine, they analyze imaging procedures and thus enable the targeted identification of e.g.tumor structures in MRI images.The analysis of measurement data from blood samples allows the identification of biomarkers that can subsequently be used in the diagnosis and treatment of diseases.In drug development, AI systems simulate potential interactions between molecules and targets or optimize screening processes based on measurement data.AI operates largely autonomously; it uses the data provided by humans.Many AI systems, especially deep learning methods, operate as so-called black boxes.They provide highly precise data that humans can rarely reproduce.This raises the question of the extent to which we can and should trust such systems that we do not fully understand.This is not just a purely technical problem, but also an ethical one.Since life sciences are very closely linked to human life and health, the question of responsibility also arises.Who is liable if AI-based systems make mistakes?If, for example, they overlook serious illnesses?AI is not a natural person and therefore not itself liable.What we therefore need is a new understanding of responsibility in the age of AI, since the results and decisions of AI-based systems can directly affect people.We must ask ourselves cautiously which decisions we want to automate and which (should) remain in human hands.Besides liability, however, there is also the question of creative power.Every AI can only reflect and utilize what humans input into it.These primarily include data and structures, but also cultural assumptions and systemic biases.The selection of training data is pivotal for the results delivered.A lack of diversity in the data sets-e.g., due to