AI Application
Artificial intelligence (AI) applications are rapidly transforming various sectors, driven by advancements in model architectures like large language models (LLMs) and deep learning, and a focus on efficient data management and deployment. Current research emphasizes responsible AI development, including addressing ethical concerns, mitigating biases, and ensuring trustworthiness through standardized documentation and robust testing frameworks. This work holds significant implications for improving efficiency and decision-making across diverse fields, from healthcare and finance to manufacturing and autonomous systems, while simultaneously highlighting the need for careful consideration of societal impact and potential risks.
Papers
A Formal Model for Artificial Intelligence Applications in Automation Systems
Marvin Schieseck, Philip Topalis, Lasse Reinpold, Felix Gehlhoff, Alexander Fay
MedPix 2.0: A Comprehensive Multimodal Biomedical Dataset for Advanced AI Applications
Irene Siragusa, Salvatore Contino, Massimo La Ciura, Rosario Alicata, Roberto Pirrone
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Anja Thieme, Abhijith Rajamohan, Benjamin Cooper, Heather Groombridge, Robert Simister, Barney Wong, Nicholas Woznitza, Mark Ames Pinnock, Maria Teodora Wetscherek, Cecily Morrison, Hannah Richardson, Fernando Pérez-García, Stephanie L. Hyland, Shruthi Bannur, Daniel C. Castro, Kenza Bouzid, Anton Schwaighofer, Mercy Ranjit, Harshita Sharma, Matthew P. Lungren, Ozan Oktay, Javier Alvarez-Valle, Aditya Nori, Stephen Harris, Joseph Jacob
Developing trustworthy AI applications with foundation models
Michael Mock, Sebastian Schmidt, Felix Müller, Rebekka Görge, Anna Schmitz, Elena Haedecke, Angelika Voss, Dirk Hecker, Maximillian Poretschkin