Cancer: Bringing clarity to complexity
Enhancing the patient experience
Behind every cancer diagnosis is a patient striving to retain their identity. Patient-centric solutions bring relief, hope, and satisfaction. They help clinicians deliver more compassionate care that connects with the patient, not just the diagnosis.

AI in cancer care: Transforming the patient journey
In Alberta, Canada, care teams are using artificial intelligence to make cancer care faster, fairer, and more personalized. See how innovation is empowering patients and care teams to improve outcomes and transform the cancer journey.
By feeling empowered, we are able to face cancer on a much stronger stance.
Charlotte Kessler, patient and family advisor, Cancer Care Alberta, Canada
Enabling personalized care
The single most important reason for innovation in radiotherapy is improving the patient's outcome.
Valery Lemmens, PhD, Member of the Board of Directors, Maastro Radiotherapy Clinic, Maastricht, The Netherlands
Creator of the Dutch Cancer Atlas

Streamlining the clinical workflow
Generative AI will influence every part of the patient journey.
Johannes Haubold, MD, Senior Physician for Clinical AI Integration, Universitätsklinikum Essen, Germany
Making innovation accessible
Solutions and services designed to deliver and sustain high-quality cancer care mean that exceptional care is always within reach for patients, clinicians, and cancer centers everywhere.

Scaling healthcare access


Our role is to work with the real agents of change, the ones that are actually driving change every day.
Isabel Mestres, CEO of the City Cancer Challenge Foundation (C/Can), Geneva, Switzerland

A holistic view
More information at your fingertips
The products/features mentioned herein are not commercially available in all countries. Their future availability cannot be guaranteed.
The statements by customers of Siemens Healthineers and their patients described herein are based on results that were achieved in the customer's unique setting. Because there is no “typical” hospital or laboratory and many variables exist (e.g., hospital size, samples mix, case mix, level of IT and/or automation adoption) there can be no guarantee that other customers will achieve the same results.



















