Home-based patient care among AI applications being tested at next-gen 5G, 6G research centre

NUHS, Singtel and Ericsson have launched a research centre to connect clinical healthcare with advanced telecommunications. Its purpose is to test how AI, 5G-Advanced and future 6G networks can improve hospital services. The partners signed a memorandum of understanding on Nov 3.

The centre will examine home-based patient care, autonomous mobile robots and surgical support. It could test connected devices that send patient information to hospital teams, robots that handle logistics, and smart glasses that help surgeons view medical images. AI may also process large amounts of healthcare data in the cloud.

The work responds to expected growth in home recovery, patient data and AI-generated information. Current 5G can support some remote monitoring, but the newer networks are designed for more data, lower latency and more connected devices. The centre aims to turn these technical capabilities into practical healthcare applications.

5G-Advanced is an upgraded form of 5G, while 6G is a future generation of mobile networks. The article says 5G-Advanced networks launched in 2025, and 6G networks are slated for commercial launch in 2030. The new research centre is studying both for healthcare uses.

Their main advantages are speed, capacity and responsiveness. They are expected to handle greater volumes of data than current 5G and have significantly less latency, meaning they can respond more quickly. 6G is also predicted to support a much larger number of connected devices.

These differences matter because hospitals may collect more complex patient data, monitor more people at home and use more AI tools. More capable networks could move more processing into the cloud. That may support smaller wearables, slimmer smart glasses and more responsive robots.

These networks could help hospitals provide care remotely instead of keeping every recovering patient in a ward. Connected medical wearables would collect vital signs at home and send the information to hospital teams. AI could assess the readings and identify possible emergencies quickly.

For example, an AI-powered wearable might detect a sudden blood-pressure spike or abnormal heart rate. If the readings suggest serious danger, hospital staff caring for the patient remotely and emergency services could receive real-time alerts. Faster networks would help carry this data reliably and with less delay.

Current 5G already supports some remote monitoring, but future home care could involve more patients, richer data and more AI processing. The article presents this as a future in which hospital wards have no walls. It does not state that this model is already routine or give a launch date for home-based care.

The article does not quantify exactly how many times more data 5G-Advanced or 6G will handle than current 5G. It says they are expected to manage greater volumes of data. That increase is linked to more patients recovering at home, more complex patient information and greater use of online AI tools.

For connected devices, the article also gives no numerical total or multiplier. It says 6G is predicted to support a much larger number of connected devices than 5G. This could matter in hospitals, where wearables, tags, smartwatches, robots and surgical equipment may need network connections.

The clear comparison is qualitative, not numerical. Newer networks are forecast to be faster, have significantly less latency and support more devices. Therefore, the article supports the conclusion that capacity will rise substantially, but it does not provide a precise amount for either data or devices.

The centre could develop several healthcare applications beyond home monitoring. These include autonomous mobile robots for logistics and patient care, augmented-reality glasses for surgeons, and network-based tracking of patients’ locations and vital signs. AI could also predict patient surges and suggest where hospitals should free beds.

One surgical example uses a hologram of a patient’s heart, recreated from CT scans and placed over the patient’s chest. Future AI could use higher-resolution camera feeds from smart glasses to superimpose that image more accurately. Robots might receive central cloud-based control while delivering medicine along hospital corridors.

NUHS already uses Microsoft HoloLens headsets for some operations and plans to replace them with slimmer augmented-reality glasses. The article also describes low-powered tags and smartwatches for tracking. These possibilities depend on networks that can carry more data and support more connected devices with less delay.

Latency is the delay between sending information and receiving a network response. Lower latency matters in healthcare because monitoring, robotic movement and surgical imaging can depend on timely information. A shorter delay can help systems react sooner, although the article does not promise specific clinical outcomes.

For a medical robot, lower latency could support more responsive central control as it moves through a hospital. For remote monitoring, alerts about abnormal vital signs could reach hospital staff faster. For surgical AI, higher-resolution camera feeds from smart glasses could travel to the AI with less delay, helping it process views of the operation more promptly.

The new networks are expected to have significantly less latency than current 5G. The centre is investigating how that capability can support robots, home care and surgery. The article describes these as potential applications, not established services already operating everywhere.

Cloud computing means processing or storing data on remote networked computers instead of relying entirely on the device collecting it. AI refers here to computer systems that analyse information, recognise patterns or assess situations. The article links both ideas to future healthcare networks and cloud-based hospital tools.

A wearable could collect a patient’s vital signs while cloud-based AI assesses them. Smart glasses could send higher-resolution camera feeds to AI, which helps create or position a surgical hologram. Because the remote system handles more demanding computation, the glasses do not need as much processing hardware on the frame.

The article says newer networks can move more data processing into the cloud. As a result, augmented-reality headsets can become smaller and less bulky because they need less powerful chips and smaller batteries. This could support slimmer surgical glasses and less bulky patient tags or smartwatches, though the article presents these as expected benefits.