Inhaltsverzeichnis
Machine-Learning is becoming increasingly important in medical research. Computer science is thus becoming the central driver of progress in medicine. Gundler is a developer of the first generation who Machine-Learning of modernity as a student.
Edited Kubernetik “Machine Learning in Medicine”
The How Question
The concrete Kubernetik How-question, which Christopher treats is:
“How do I use machine learning in medicine for the benefit of the patient?”
Response phrase 1
The most important thing to employ machine learning in medicine is a checked correctness of the algorithm and the trust of the patient in these.
Response phrase 2
I should begin with the production of absolute transparency for the patient who leaves none of his questions unanswered.
Derive ToDos
In order to progress through action, to learn and to be able to comprehend a topic even better, every created Kubernetik needs a personal commitment to implement three concrete ToDos in the next two weeks . These must be in your own control area and must not depend on other circumstances. Gundler derives the following tasks for himself and as a recommendation for those interested:
- I document relentlessly
- I consult technical experts
- I work on validability
Conclusion
As always, the responses of a canvas created with the canvas Kubernetik seem simple after mature, networked consideration. Gundler about it:
“Compared to other expert Kubernetiks on this website, it doesn’t seem very complex. But it still offers exciting insights. It alone shows that, according to this scheme, the performance of the AI model has to take a back seat to its transparency and explainability. This is a finding that will certainly be shared by the vast majority of medical computer scientists. This speaks for the functionality of Kubernetiks as a method for understanding and overcoming complexities!”
More about Christopher Gundler
On LinkedIN – https://www.linkedin.com/in/christopher-gundler/
