An unfavorable revenue mix and higher R&D expenses contributed to a 13% drop in profit for Siemens Healthcare in its 2015 fiscal first quarter. The company also announced that the Siemens Healthcare division is getting a new CEO.
For the period (end-December 31), the company posted 2.851 billion euros ($3.25 billion U.S.) in revenue, up 6% on an actual basis and 2% on a comparable basis from the 2.694 billion euros ($3.07 billion U.S.) reported in the first quarter of fiscal 2014.
Order growth was driven by recovery in Europe and the U.S. markets, while the Asian sector showed weakness, according to the vendor. Revenue growth was driven by replacement demand in Europe, Siemens said.
Quarterly profit dipped to 413 million euros ($470.3 million U.S.) from 473 million euros ($538.6 million U.S.) in the first quarter of fiscal 2014. Siemens said the segment's profit was limited by an unfavorable revenue mix as well as increased R&D expenses targeted at future growth. The firm also noted that currency tailwinds were not yet evident in its profit due to hedging.
In summarizing the results, Siemens AG President and CEO Joe Kaeser said in the company's earnings release statement that "healthcare needs to step up its efforts to quickly resume to its outstanding performance."
Siemens also announced that longtime Healthcare CEO Hermann Requardt is stepping down "to enable a generation change" as Siemens moves to separate the Healthcare division into an independent legal entity. He will remain available in an advisory capacity. Requardt has been CEO of Siemens Healthcare since 2008.
The new CEO will be Bernd Montag, who is currently CEO of Imaging and Therapy Systems. Siemens has also appointed Michael Reitermann as a member of executive management and Michael Sen as chief financial officer. Reitermann is currently CEO of the vendor's Diagnostics business. All three appointments are effective February 1.
In other moves, Siemens AG has named Siegfried Russwurm as the board-level partner for the healthcare business.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
