Artificial intelligence is transforming healthcare from individual devices to entire hospitals. At a conference, GE Healthcare's manager for Central and Eastern Europe summed it up, emphasizing that the goal of these technologies is more human-centered care. He added survey results from healthcare professionals and patients, as well as concrete examples from the field.
Why healthcare needs to be more human
Demographic shifts are increasing demand for care, yet there is a shortage of people: already today there is a global shortfall of around 7 million healthcare professionals, with the deficit potentially growing by another 10 million by 2033 and even to 14 million by 2035. The survey showed that healthcare professionals are on the verge of burnout, do not feel respected, and are considering leaving the profession. Patients, for their part, are losing trust in the system. But both groups agree on one thing: the transformation must make healthcare more about people, not about paperwork and waiting rooms.
For context, the speaker also introduced GE Healthcare as a standalone company with more than 50,000 employees and an installed base of over 4 million systems on which more than a billion patients are examined annually. In Central and Eastern Europe, the company has over 1,500 people and two key locations, including Budapest with approximately 500 software engineers. They work on digital solutions, including AI, that are designed to ease the workload of teams and improve treatment outcomes.
Six trends and a data avalanche
According to the survey, the future of healthcare is forming around six trends: good conditions for teams, partnerships between patients and providers, smart and connected technologies, working with big data, decentralized (distributed) care, and precision medicine. The huge growth of data is double-edged: it brings new insights but also noise. The speaker noted that healthcare data today doubles globally roughly every 70 days. The key will be to distinguish signal from noise and get the right information to the right people at the right time.
AI is entering the system at multiple levels. At the device level, algorithms and image processing help highlight critical findings and reduce errors. At the department level, it accelerates processes, shortens waiting times, and reduces administrative work that takes too much of healthcare professionals' time. At the level of the entire organization, it enables analytics on big data, improves the patient journey, prevention, and the overall care experience.
Where AI helps today: examples from the field
Computer vision with machine learning and deep learning techniques is already bringing portable ultrasounds right to the bedside so that even less experienced professionals can perform the exam. Large language models help process the flood of publications that a single doctor could not otherwise keep up with. In radiology the workload is growing at a breakneck pace: while ten years ago it was on the order of hundreds of images per shift, today it is tens of thousands. Without intelligent tools, the pace would be unsustainable.
Specific examples were also given from magnetic resonance imaging, where GE Healthcare's AIR Recon DL solution uses deep learning to deliver higher-quality images in less time. In radiation oncology, a platform approach helps by gathering data from multiple modalities into a single overview so the team doesn't have to spend time searching for them and switching systems. The common denominator is giving time back to healthcare professionals for work with the patient and building trust, without which the transformation will not succeed.
