10 real-world examples of AI in healthcare

That starts with getting a patient in the right position for an exam. In CT, patient mispositioning is a common challenge, which can result in increased radiation dose to the patient or image noise¹. AI-enabled camera technology can automatically detect anatomical landmarks in a patient to enable fast, accurate and consistent patient positioning. In addition, AI-enabled image reconstruction can help to reduce radiation dose and improve CT image quality, thereby supporting diagnostic confidence.

Enter AI-based image reconstruction, which can accelerate MR exams to increase department productivity and reduce the cost per exam, while supporting diagnostic confidence with high-resolution images. On top of that, the technology increases MR accessibility for patients who are stressed, anxious or in pain. Because it shortens exams and provides first-time-right scans regardless of patient condition, patients spend less time in the scanner, thereby improving their experience.

With AI, we can take away some of that manual and repetitive labor; just like in some of the other AI in healthcare examples in this overview. AI-based automatic measurements can deliver fast and reproducible echo quantification, improving both the patient and staff experience. Healthcare professionals can accept or modify measurements based on their own clinical assessments, giving them a powerful tool to enhance their expertise while remaining in control of diagnostic decision-making.

For example, AI algorithms that scour MR images of the brain for subtle neurological changes over time have been shown to improve diagnostic accuracy in multiple sclerosis patients by 44%, while reducing reading times³. Similarly, AI-based lung nodule detection can perform nodule search 26% faster, detecting 29% of previously missed nodules compared to manual inspection4. For time-pressured radiologists who have to interpret more images than ever before, such AI-enabled support can make a meaningful difference, while also benefiting the patient.

In the future, the intelligent integration of data could give further insight into a patient’s prognosis, supporting selection of the best care pathway for that particular patient based on an analysis of treatment outcomes for similar patients.

For example, in stroke care, cloud-based AI can help analyze CT images to automatically detect large vessel occlusion – a major source of stroke – as well as its location. The software then shares this analysis with physicians at the primary stroke center and at the intervention center where the patient is eventually treated. Similarly, cloud-based AI can aid in planning and guiding endovascular surgery, helping interventional physicians to operate more accurately and efficiently.

By automatically monitoring vital signs and calculating early warning scores that signal possible patient deterioration, AI-enabled tools can help nurses and care teams identify early signs of events such as respiratory failure or cardiac arrest, allowing them to respond rapidly. In doing so, one hospital managed to reduce serious adverse events in the general ward by 35% and cardiac arrests by more than 86%⁶.

For example, through remote sensing, we can monitor and analyze over 500 parameters on an MR machine, allowing us to identify proactively when certain hardware parts may need maintenance or replacement. As a result, 30% of service cases can be resolved before downtime is caused – preventing avoidable interruptions to clinical practice and unnecessary patient delays⁷. In the future, having a full digital twin, or virtual representation of an entire imaging fleet, could allow for even more comprehensive predictive maintenance and continuous operational optimization.

Using the power of AI, we can extract relevant patterns from vast amounts of real-time and historical hospital data to forecast and manage patient flow. This generates actionable insights that can help answer questions such as: which patient should get an intensive care bed first? Or: which patient is ready to be transferred to a stepdown unit? Having such insights at their fingertips enables healthcare providers to get the most out of precious resources and manage transitions of care more effectively, from admission to discharge – ensuring that every patient gets the right care in the right place at the right time.

With AI-assisted analysis, cardiac care could eventually become more predictive and proactive. A recent study underscored this potential, showing how a deep learning model can predict the short-term risk of atrial fibrillation based on 24-hour Holter recordings [8]. It’s yet another example of how AI can help improve patients’ lives, by getting them the care they need sooner.

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