Description
Chair: Jun.-Prof. Dr. Anne-Christin Hauschild (UMG), Dr. Nicolas Spicher (UMG), Dr. Zully Ritter (UMG)
Content / Abstract:
Nowadays, AI applications in healthcare and in general for clinical practice have proved to be successful in specific tasks like diagnosis prediction, risk estimation (e.g., heart failure risks), alarm and order automation, or cancer differential diagnosis; on the other hand, these achievements are confronted to endurance challenges related mainly to patient security (data privacy and data ownership) and bias additionally to ethical issues. Even without considering the regulation of AI and the coming AI Act, AI solutions to be implemented in clinical practice have been developed in academia and industry. In our session, we will provide insights into already proven solutions and those being tested to be efficiently implemented in clinical setups. We will present and delve into machine-learning approaches using mainly clinical, image, and biosignal data. Concerning solutions to handle data privacy properly, among other patient security issues, we will present in this session how, for example, federated learning is gaining in importance as an appropriate technique, allowing not only to solve the problems related to data sharing (both ethical and technical) but also even improving the performance of AI solutions in the case of not having enough or representative data to find a good performing AI solution.
Aiming to share and discuss the lessons learned during designing and testing AI solutions for clinical practice, we invite you to participate and be part of it.
Topics of interest include:
- Lessons learned from AI projects in clinical practice
- Federated Learning as a novel modality enabling AI in clinical practice
- Translation of AI methods for biosignal processing towards clinical practice
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