2023-08-20 18:04:27
Bojana Milekic checks on a patient with a kinked chest tube in the ICU. Milekic is the director of the Mount Sinai Rapid Response Medical Team. (Hilary Swift for The Washington Post)
Every day, Bojana Milekic, an intensive care doctor at Mount Sinai Hospital, scrolls through a computer screen with patient names, looking at the red numbers that appear next to them – an artificial intelligence-generated score – to assess who might die.
Pranshu Verma // The Washington Post// INFOBAE
One morning in May, the tool scored a 74-year-old lung patient with a score of 0.81, well above the 0.65 score at which doctors start to worry. He didn’t seem to suffer, but he grabbed his daughter’s hand as Milekic started to work. She came around the bed and soon spotted the problem: A kinked chest tube was retaining fluid from her lungs, causing her blood oxygen levels to plummet.
After changing the position of the tube, his breathing stabilized: a “simple intervention,” says Milekic, that might not have happened without the help of the computer program.
Milekic’s morning might be a herald of AI’s potential to transform healthcare. Mount Sinai is part of an elite group of hospitals that invest hundreds of millions of dollars in AI software and training, turning their institutions into AI laboratories. They are encouraged by a growing body of scientific literature, including a recent study showing that AI mammography readings detect 20% more breast cancer cases than radiologists, along with a conviction that AI is the future of medicine.
The researchers are also working to bring generative AI, which supports tools capable of creating words, sounds and text, to the hospital setting. Mount Sinai has deployed a group of AI specialists to internally develop medical tools that doctors and nurses are testing in clinical care. Transcription software completes billing paperwork; chatbots help build patient summaries.
But the advances are causing tensions among frontline workers, many of whom fear the technology will come at a high cost to humans. They worry that the technology will misdiagnose, reveal sensitive patient data and become an excuse for insurers and hospital administrators to cut staff in the name of innovation and efficiency.
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