Edge AI has several potential applications in the healthcare sector, where real-time data processing, privacy, and security are critical considerations. The application of AI technology in healthcare has the potential to support several of the issues the healthcare sector is currently facing, from improving population health and the patient care experience, expediting diagnoses, and enabling preventive care through predictive analytics and point-of-care testing. By using edge-based AI, the healthcare sector can utilise these technologies while also protecting sensitive information like patient data by processing data locally.

Sensitive patient data is protected when processed on the edge, reducing the risk of data breaches or unauthorised access. This is particularly important in healthcare, where patient confidentiality and privacy regulations are stringent. UK healthcare providers are required to be compliant with data privacy acts such as the Data Protection Act 2018 and the General Data Protection Regulation (GDPR). Edge AI can also ensure that patient data is processed locally, reducing the risk of cyber disturbances compared to cloud-based AI. Likewise, Edge AI can monitor systems for potential cyberattacks or data breaches.

Edge AI can be used to analyse patient data collected from various secured sources, including electronic health records (EHRs), wearable devices, and medical sensors, to prevent the risk of developing certain diseases or medical complications. Healthcare providers can use this information to implement preventive measures and personalised treatment plans. Edge AI can support remote patient monitoring by enabling wearable devices equipped with sensors to monitor vital signs such as heart rate, blood pressure, and blood glucose levels in real-time. These decisions can analyse this data locally and alert healthcare providers or patients about any abnormalities, allowing for timely intervention and proactive healthcare management. Similarly, Edge AI can support point-of-care testing by enabling medical devices to perform rapid diagnostic tests, such as analysing blood samples for infectious diseases, detecting biomarkers for specific conditions, or assessing medication adherence. This allows for timely diagnosis and treatment decisions at the point of care, without the need for centralised laboratory facilities. In remote or underserved areas with limited access to healthcare facilities, edge AI-enabled devices can provide essential health monitoring and diagnostic capabilities. For example, portable ultrasound devices equipped with AI algorithms can assist healthcare workers in conducting prenatal screenings or diagnosing medical conditions in remote communities.

Similarly to Edge AI analysing data from wearable devices, Edge AI algorithms deployed on medical imaging devices can analyse images such as X-rays, MRI scans, and CT scans locally to detect abnormalities, tumours, fractures, or other medical conditions. This process reduces the need to send this data to centralised servers for analysis, leading to faster diagnosis and treatment. Edge AI can also assist emergency responders and healthcare providers in triaging patients, identifying medical emergencies, and allocating resources more efficiently during natural disasters, mass casualty incidents, or public health emergencies.