Deep lear­ning – Living healthy with data?

7. September 2018

Health­care is one of the central areas for Machine Lear­ning (ML). Deep lear­ning is mainly used for the analy­sis of X‑ray images and in magne­tic reso­nance and compu­ter tomo­gra­phy. Anony­mous pati­ent data supports clini­cal diagnostics as well as appli­ca­ti­ons in radio­logy, patho­logy and dermatology.

Curr­ently, ML tech­ni­ques already allow the detec­tion of breast cancer, heart dise­ase, osteo­po­ro­sis and the first signs of skin cancer. It is expec­ted that in the near future such systems will be able to detect pande­mics at an early stage and take timely preven­tive measu­res. In addi­tion, the first service robots for nursing support are being developed.

The prere­qui­site and parti­cu­lar chal­lenge lies in compli­ance with data protec­tion regu­la­ti­ons, in parti­cu­lar in the use of pati­ent data, but also in the trans­pa­rency of the systems and not least in user accep­t­ance. More about this in today’s use case about the impor­t­ance of data for the phar­maceu­ti­cal indus­try and for clini­cal studies.

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