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Austausch über die eigene Disziplin hinaus
Die engverwobenen Bereiche Künstliche Intelligenz und Medizin leben vom Austausch über die eigene Disziplin hinaus, vom Diskurs und der Zusammenarbeit der unterschiedlichen Fachbereiche. Diesen Austausch wollen wir mit dieser Veranstaltung anregen und anstoßen. Annäherungen an Fragestellungen, aktuelle Entwicklungen und Herausforderungen dieses interdisziplinären Themenbereichs sollen im Zentrum stehen und für Gesprächsstoff sorgen.
Themenabend: AI in Medicine
WANN
Mittwoch, 20.09.2023
17:30 - 19:30 Uhr
Im Anschluss gemütlicher Austausch bei Getränk & Fingerfood.
WAS
Keynote, Austausch, Netzwerken
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Themenabend: AI in Medicine
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Evangelia Christodoulou
German Cancer Research Center (DKFZ)
Post-doctoral fellowship as part of the AI Health Innovation Cluster, öffnet eine externe URL in einem neuen Fenster
Drawing on her educational background, Evangelia Christodoulou holds a BSc. in mathematics and an MSc. in Biostatistics. In 2021, she successfully completed her PhD in clinical prediction modelling at KU Leuven, Belgium, with guidance from Professor Ben Van Calster. Throughout her doctoral studies, she garnered valuable experience collaborating in an interdisciplinary setting that brought together oncologists and statisticians. Her contributions during this period were particularly impactful, concentrating on enhancing the methodological integrity and validation procedures for predictive algorithms utilizing clinical data.
In February 2021, she became a member of the German Cancer Research Center (DKFZ), securing a postdoctoral fellowship as part of the AI Health Innovation Cluster of the department led by Professor Lena Maier-Hein. At present, her research focuses primarily on validating AI algorithms in the realm of medical imaging analysis. Furthermore, she is delving into the potential of deep learning techniques to support clinical prediction utilizing tabular data.
©Christodoulou
AI in medical imaging analysis: Successes, caveats, and how to move forward
AI algorithms have the potential to reshape the landscape of healthcare. Recent achievements of AI models in the medical field, combined with the intrigue surrounding Large Language Models (LLM) such as chatGPT, Llama 2, and the like, introduce certain considerations from both medical and ethical standpoints.
This naturally gives rise to a fundamental question: To what extent can we place our confidence in modern AI algorithms to aid in patient management, and how can we navigate this terrain safely? During my presentation, I will predominantly delve into subjects related to validating AI algorithms as part of the sphere of medical imaging analysis. This will lay the foundation for discourse related to the measures necessary to establish reliable AI applications in the medical domain.
