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NHS doctors want to use ChatGPT to free up time and have begun experimenting already
Free artificial intelligence program ChatGPT could save NHS doctors time to see more patients, according to NHS heart experts.
Currently, ChatGPT pulls on existing data to formulate answers and write copy, which has seen a multitude of professionals and students use the software for a variety of uses.
If the software were to be used in parts of the NHS it would have to be trained to interpret and write notes on scans, according to Dr Samer Alabed, a cardiac radiologist at Sheffield Teaching Hospitals NHS Foundation Trust.
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Dr. Alabed told The Daily Mail: “It takes radiologists 45 minutes to analyse these scans and then write a report. We estimate that NHS clinicians spend 115,000 hours putting heart scan reports together each year.
“If we were able to use software like ChatGPT to do this work, we could free up an incredible amount of time which could be used to treat more patients.”
The software also holds the potential to translate technical medical jargon into non-medical language to improve communication for patients in hospitals.
As a result, a team of cardiologists including Dr. Alabed are currently experimenting with the program with the hopes of running a clinical trial which could lead to an increased usage of AI in hospitals.
So far, it’s been discovered by clinicians at Sheffield Teaching Hospital that a specially designed AI program can carry out complicated heart measurements and identify signs of disease.
This amounts to an average time-saving of 20 minutes, but doctors are still required to write up reports.
Enabling doctors to have their reports written up by AI would be the next step.
However, there is scepticism around the use of AI given that results are based on the quality of data available, as well as the fact that the source data would require more regular updates to be completely current.
It is a problem that Dr. Alabed and his team have acknowledged.
“When we input measurements into Chat GPT we’re finding that it sometimes adds in extra details which are made-up or not accurate,” he said.
“The next step is to work out how to train it to be accurate every time, so patients get the correct diagnosis.”
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