Siemens Healthineers is one of the largest medical technology companies in the world, with solutions that cover the entire patient journey from diagnosis to treatment and recovery. When speaking with Virginia Chan, Head of Digitalization at Siemens Healthineers, there were many examples of how artificial intelligence can address challenges in the healthcare industry, for example lack of skilled healthcare professionals, vulnerability to burnout among healthcare professionals, and even aging populations which raise the cost burden for governments and individuals.

Virginia talked about alignment with national health policies like Singapore’s and Malaysia’s that emphasize preventive healthcare, improved access for patients, and also improved affordability of healthcare. “So, it’s very much our focus to use technology to prevent, predict, as well as treat and let patients recover well so they can live a long, healthy, and happy life.”

She said, “Our new ambition is to bring healthcare to everyone, everywhere sustainably. This is really what we are passionate about, especially now that we have digital and AI components and can do patient training and precision medicine.”

AI takes a co-pilot role when it comes to diagnostics via imaging. Reporting time can be reduced by 50% while the scanning time for patients goes down by as much as 70%

Innovation to Lessen Healthcare Burdens

Virginia cited 18 million as the number of healthcare professionals that are currently lacking, globally. Also, there are 30-percent fewer radiologists than what we currently need around the world. The COVID situation has also actually accelerated burnout, and many healthcare professionals have left the industry leading to a stark shortage of skills in the healthcare sector.

“So what Siemens Healthineers sees is that digital technologies and AI can actually do more with the limited resources we have,” Virginia pointed out.

In short, there is increasing demand for healthcare but not enough manpower, but AI can help to relieve the strain of burden upon existing healthcare resources so they are better able to provide human-centric care for patients.

AI-driven Reporting and Measurements

According to Virginia, there is an AI-based radiology assistance that does automatic measurements and reporting after imaging diagnostic tests. This is thanks to more automated processes when it comes to determining the area of the anatomy to be scanned, for example. These are significant radiologist tasks which AI is able to help with, and in fact, one major healthcare institution in Singapore already uses this AI-based radiology capability in chest CT scanning to screen for lung cancers.

This AI capability has been deployed to more accurately screen for and treat cancer, as well as scan the brain. “We can accelerate brain scans by 70-percent and this is very important in a stroke patient’s case, because the longer it takes to diagnose and treat the stroke the higher the risk to the patient.

“Ultimately, AI suggests the outcome, but you as the healthcare professional will have the final say,” Virginia said, explaining that the AI takes a co-pilot role when it comes to diagnostics via imaging.

In this way, reporting time can be reduced by 50% while the scanning time for patients goes down by as much as 70%.

Remote monitoring is another area with exciting possibilities.  “We use AI to provide monitoring when the patient goes home, as well as prioritize the tasks list for the caregiver. They will get automated alerts when it is time to do measurements, or take medication, and so on.”

The outcome is promising, as the largest heart and diabetes center in Europe has a successful case study of 53% death rate reduction in heart surgery patients who were discharged and monitored remotely by AI.  

This bodes well for the whole patient-care continuum because now higher quality care is possible for a patient in the more familiar and comfortable environment called home.

Digital twins and simulations

Thanks to advances in technology, upgrades to computing power now also means better imaging processing. “Now, we have something called patient training whereby we create a simulation, or a digital twin of a patient using the patient’s health data.”

This has intriguing possibilities for preventive and predictive care, but Virginia wanted to call out the personalization capability that digital twin technology can have. For example, having a surgery simulation as a way of training how to approach a patient’s unique health, physiological, and physical condition.

This personalization also extends to communication when it comes to providing better quality patient care.  Here, Virginia was excited about the promise of generative AI in consolidating all of a patient’s information and presenting it for easier viewing and comprehension for the physician.

“It can take a lot of time to review all the information in a patient’s medical data history. We see the next level of AI as being able to consolidate all of that into one page so as to reduce consultation time by as much as 70%, and allow the physician to express their human side by providing more personalized care.”

Virginia identified close collaboration with governments as crucial to be able to balance benefits of AI with its uncertainties and risk. She shared how Siemens Healthineers works with health ministries of Southeast Asia countries to create a SEA region-level health data hub and underline it with data governance and consultancy to create AI in healthcare use cases. 

AI has undeniable potential to improve healthcare organizational efficiencies, patient experiences, and clinical outcomes when used to assist healthcare professionals.  For effective, positive and more predictable outcomes, responsible, ethical, and safe usage of AI can’t be emphasized enough.