Project C05: Creating explanations in collaborative human–machine knowledge exploration

When it comes to decision-making in medicine, a great deal of information must be considered and evaluated. Both prior medical knowledge as well as the patient’s previous test results or health history are needed. Based on this information, doctors make a diagnosis and decide on what treatment the patient should receive. In Project C05, researchers in computer science at Bielefeld University are innovating an intelligent system that assists medical professionals in evaluating all of the available treatment options and potential consequences of these therapies. For this, the system does not just answer questions – it also asks active questions and gives recommendations. Through this interaction, medical professionals arrive at an informed treatment decision and an explanation of why the chosen therapy is the most appropriate compared to other possible options.

 

Research areas: Computer science

Project leaders

Prof. Dr. Philipp Cimiano

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Prof. Dr.-Ing. Stefan Kopp

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Staff

Dominik Battefeld, M.Sc.

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Support Staff

Lukas Kachel, Bielefeld University

Mike Uhrich, Bielefeld University

Daniel Prib, Bielefeld University

Pub­lic­a­tions

Formalizing cognitive biases in medical diagnostic reasoning

D. Battefeld, S. Kopp, in: Proceedings of the 8th Workshop on Formal and Cognitive Reasoning, 2022.




Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive Process Trajectories

D. Battefeld, S. Mues, T. Wehner, P. House, C. Kellinghaus, J. Wellmer, S. Kopp, in: Proceedings of the 46th Annual Conference of the Cognitive Science Society, 2024.


A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support

F. Liedeker, O. Sanchez-Graillet, M. Seidler, C. Brandt, J. Wellmer, P. Cimiano, in: n.d.



ASCODI: An XAI-based interactive reasoning support system for justifiable medical diagnosing

D. Battefeld, F. Liedeker, P. Cimiano, S. Kopp, in: Proceedings of the 1st Workshop on Multimodal, Affective and Interactive EXplainable AI (MAI-XAI), 2024.


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