Get our free email newsletter

MIT Develops Robot Nurse to Aid in the Maternity Ward

The team at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed an artificial intelligence (AI) system capable of learning human behavior and interpreting the data to perform basic scheduling tasks and provide decision making support to nurses and doctors in the labor ward and delivery room. By using the NAO robot, developed by Softbanks Robotics, CSAIL demonstrated the AI capabilities at the Beth Israel Deaconess Hospital in Boston, Massachusetts where 90% of recommendations were accepted by nurses and physicians.

In a two-year study described in the paper, Robotic Assistance in Coordination of Patient Care, a robotic nurse named Ginger was put to task in the role of ‘resource nurse’ to learn, understand, and make complex decisions in a fast-paced environment that requires the coordination of multiple actions and resource allocations. The robot used learning behavior demonstration techniques and pre-programmed computer algorithms to evaluate the needs of the maternity ward, including room assignments and staff requirements, and provided and received feedback via speech recognition.

The aim of the work was to develop artificial intelligence that can learn from people about how the labor and delivery unit works, so that robots can better anticipate how to be helpful or when to stay out of the way — and maybe even help by collaborating in making challenging decisions.

- Partner Content -

Mastering High Voltage: The Importance of Accurate Test Equipment

This whitepaper underscores that precise calibration of high-voltage test gear — especially when measuring 1 kV–150 kV systems — is essential for safety, reliability, and regulatory compliance. It details measurement techniques (voltage dividers, step-down transformers, etc.), the impact of environmental and connection factors on accuracy, and why traceable calibration (e.g. to NIST / A2LA) is a must to ensure consistent, reliable results.
Julie Shah, Professor and Senior Author, MIT

Now that CSAIL’s AI has been proven to schedule tasks and coordinate resources in the labor ward, it is also being put to task in military applications as described in a recent study, Apprenticeship Scheduling: Learning to Schedule from Human Experts. The studies prove that the technology can serve as an effective training tool or decision making tool without impacting the need for human oversight.

 

References: MIT

Related Articles

Digital Sponsors

Become a Sponsor

Discover new products, review technical whitepapers, read the latest compliance news, and check out trending engineering news.

Get our email updates

What's New

- From Our Sponsors -

Don't Let Regulations

Derail Your Designs

Get free access to:

Close the CTA
  • Expert analysis of emerging standards
  • EMC and product safety technical guidance
  • Real-world compliance solutions

Trusted by 30,000+ engineering professionals

Sign up for the In Compliance Email Newsletter

Discover new products, review technical whitepapers, read the latest compliance news, and trending engineering news.

Close the CTA