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Artificial Intelligence

From innovation to impact: How AI elevates patient care

AI is transforming healthcare by empowering people. It’s giving clinicians time to focus on what matters most: their patients. By turning data into decisions and complexity into clarity, AI helps to make care faster, smarter, and more human.
Sophie Gräf
Published on October 24, 2025
Every hospital has its own rhythm — the pulse of patients arriving, clinicians moving, machines humming. But that rhythm has quickened. Around the world, healthcare systems are under strain: Aging populations, rising demand, and workforce shortages make it harder to give every patient the time and attention they deserve. The true challenge isn’t just doing more — it’s caring better. Artificial intelligence (AI) is stepping in, not to replace human compassion, but to amplify it. The technology is helping doctors see patterns in complexity, hospitals use resources more wisely , and patients receive care that’s as personal as it is precise.

Automating workflows

In radiology, every clinical image is a fragment of a patient’s story — and the number of cases  keeps rising. As demand grows, radiology teams are under increasing pressure to deliver precision at speed. “Without optimizing the way we operate, it’s not possible for us to meet this growing demand,” says Professor Fabian Bamberg, MD, clinical director of radiology at University Hospital Freiburg in Germany. That’s where AI is beginning to make a difference, quietly, in the background, helping imaging teams focus on what matters most.

“The defining step for the entire diagnostic pathway is the selection of the exam protocol. It must fit the patient, the clinical context, and the scanner that’s being used,” says Professor Christopher L. Schlett, MD, medical director of radiology at the University Heart Center in Bad Krozingen, Germany.

Prof. Christopher L. Schlett, MD, and a technologist acquiring a CT scan.

As imaging volumes rise, radiology teams must maintain quality and consistency while meeting growing demand. Standardized protocols achieve efficiency and reliability, but often at the cost of personalization — the ability to adapt each exam to the individual patient. Finding the right balance between efficiency and individualization has become one of the defining challenges within the diagnostic pathway. Large language models can automate this process with precision and personalization, giving radiology teams the freedom to focus on the nuances no algorithm can understand.

“AI is already adding enormous value by automating individual routine tasks. The next step will be to use AI to automate entire phases of the imaging process, and having them operate as autonomously as possible. That would allow me to devote more of my time and attention to complex cases, and most importantly, to my patients,” explains Bamberg.

What are large language models?

Large language models (LLMs) are a form of artificial intelligence trained to understand and generate human language. In healthcare, they can interpret clinical notes, referrals, and patient information to support decision-making. In radiology, this means an LLM can read a physician’s request, understand the clinical question, and recommend the most appropriate imaging protocol — matching the exam to the patient, the condition, and the scanner being used.

Patient during CT Scan
Prof. Fabian Bamberg, MD, Clinical Director Radiology at University Hospital Freiburg, Germany
CT Scan of thorax
Prof. Fabian Bamberg, MD and Prof. Christopher L. Schlett, MD looking at clinical images
Patient during CT Scan

Personalizing care

Thousands of kilometers away in Canada, the healthcare team at Cancer Care Alberta is aware of another kind of pressure — time itself. For cancer patients, waiting for a diagnosis or therapy can be agonizing. Every cancer journey is unique: Each patient requires an individualized treatment plan involving multiple departments, specialists, and resources that must align at exactly the right moment. For hospitals, anticipating those needs in real time is an enormous challenge.

Now, AI is helping replace unpredictability with preparation. By analyzing over 300,000 cancer diagnoses and millions of visits, imaging scans, and lab results, predictive models enable hospitals to forecast demand, plan staff, and allocate resources — shortening waiting times by increasing readiness.

Omar Khan, MD, Medical Oncologist at Cancer Care Alberta, Canada, pointing at a clinical image

“This means ensuring patients have access to state-of-the-art care when they need it and that we tailor it to meet the needs of individual patients,” says Brenda Hubley, chief program officer at Cancer Care Alberta.

Beyond planning resources, AI is also transforming how clinicians understand disease itself. “Cancer is a nonlinear disease. With artificial intelligence, we can overcome the inherent limitations of linear models,” says Omar Khan, MD, a medical oncologist at Cancer Care Alberta. The team has developed an AI model capable of extracting more than 5,000 features from CT images to predict survival outcomes in patients. “With our early data, the model is already a better predictor than staging,” says Khan. “It goes beyond looking at cancer alone, but rather at the overall condition of the patient.”

The true measure of progress isn’t how intelligent systems become — it’s how much more human healthcare can be. “To me, the best way that care teams and health systems can support the cancer patient is recognizing that the cancer patient and their families are the experts on them,” says Charlotte Kessler, patient and family advisor at Cancer Care Alberta, who was diagnosed with a rare form of brain cancer in 2013. “We know how we are feeling, we know what we are experiencing when we are listened to, heard, and engaged. We feel empowered, and by feeling empowered, we are able to face cancer from a much stronger stance.”

Brenda Hubley, Chief Program Officer at Cancer Care Alberta, entering the cancer care center
Omar Khan, MD, and Brenda Hubley walking through the cancer center
Omar Khan, MD, and colleagues discussing
Christina Kessler cancer patient
Brenda Hubley, Chief Program Officer at Cancer Care Alberta, entering the cancer care center

Siemens Healthineers Shape 25 Spotlight

Sophie Gräf
Sophie Gräf
By Sophie Gräf
Sophie Gräf is a digital editor and multimedia content creator at Siemens Healthineers.

  1. The product/feature and/or service offerings mentioned herein are not commercially available in all countries and/or for all modalities. Their future availability cannot be guaranteed.

  2. The statements by customers of Siemens Healthineers 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.