Keynote Lectures

Prof. Dr.

Peter J. Wild

Donnerstag, 26. März 2019, 08.00 - 08.45 Uhr im Raum MZF 2 (CCL, Ebene 0)

Lernende Systeme zur Bilderkennung in der Pathologie

 

Foto: ©Bruederli_9251

 

 

Prof. Dr.

Guy Joos

Freitag, 27. März 2020, 08.00 - 08.45 Uhr im Raum MZF 2 (CCL, Ebene 0)

From P0 to P4 medicine:
the development of respiratory medicine towards a personalized, precise, preventive and participatory approach

We are facing an explosion of knowledge on the genetics and the biology of diseases. In addition, the generation of “big data” originating from the application of systems biology, and those that result from large epidemiological studies are providing us new insights into the pathogenic mechanisms of many chronic  diseases. So, it is now possible to move more and more from reactive disease care to care that is Predictive, Preventive, Personalized and Participatory (P4 medicine). This (r)evolution is also happening in respiratory medicine and is exemplified by the major advances made in treatment of common diseases such as lung cancer and obstructive airway diseases, and rare diseases such as cystic fibrosis.

Both asthma and COPD are now being considered as showing  a vast complexity (i.e. comprising several components) and heterogeneity. Phenotypes have been well characterized, as well as some of the underlying biological mechanisms (endotypes)  and treatable traits. However, in both asthma and COPD, and despite optimal maintenance therapy and adequate treatment adherence, too many patients still remain symptomatic and sub-optimally controlled. In the case of severe asthma the unravelling of important endotypes, such as T2 inflammation, and a series of landmark clinical trials, have led to the introduction of targeted treatments (monoclonal antibodies against IgE, IL5 and IL4/13).  Precision medicine has arrived in our respiratory clinic, and it is already affecting the daily life our patients.

Dr.

Oliver Hinz

Samstag, 28. März 2020, 08.00 - 08.45 Uhr im Raum MZF 2 (CCL, Ebene 0)

Von Machine Learning zum Machine Teaching – Wie wir von künstlicher Intelligenz lernen können

Der Vortrag von Prof. Dr. Oliver Hinz diskutiert die Möglichkeiten und Beschränkungen von Künstlicher Intelligenz im medizinischen Umfeld. Es geht dabei sowohl um die Automatisierung von Entscheidungen als auch um die Generierung von Wissen mithilfe von Methoden des maschinellen Lernens. Strategische und operative Probleme werden aufgezeigt und es werden Vorgehensmodelle vorgeschlagen, die dafür sorgen sollen, dass Mensch und Maschine zusammen bessere Entscheidungen treffen. Illustriert wird das Vorgehen mit einer Studie im Bereich medizinischer Diagnosen.