NEW YORK — Every day Bojana Milekic, a critical care doctor at Mount Sinai Hospital, scrolls through a computer screen of patient names, looking at the red numbers beside them — a score generated by artificial intelligence — to assess who might die.
Mount Sinai is among a group of elite hospitals pouring hundreds of millions of dollars into AI software and education, turning their institutions into laboratories for this technology.
They worry about the technology making wrong diagnoses, revealing sensitive patient data and becoming an excuse for insurance and hospital administrators to cut staff in the name of innovation and efficiency.
In the 1970s, Stanford University researchers created a rudimentary AI system that asked doctors questions about a patient’s symptoms and provided a diagnosis based on a database of known infections.
In the 1990s and early 2000s, AI algorithms began deciphering complex patterns in X-rays, CT scans and MRI images to spot abnormalities that the human eye might miss.
“While artificial intelligence (AI) undoubtedly holds tremendous potential to improve patient care and health outcomes, I worry that premature deployment of unproven technology could lead to the erosion of trust in our medical professionals and institutions,” he said in a statement.
The original article contains 1,500 words, the summary contains 201 words. Saved 87%. I’m a bot and I’m open source!
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NEW YORK — Every day Bojana Milekic, a critical care doctor at Mount Sinai Hospital, scrolls through a computer screen of patient names, looking at the red numbers beside them — a score generated by artificial intelligence — to assess who might die.
Mount Sinai is among a group of elite hospitals pouring hundreds of millions of dollars into AI software and education, turning their institutions into laboratories for this technology.
They worry about the technology making wrong diagnoses, revealing sensitive patient data and becoming an excuse for insurance and hospital administrators to cut staff in the name of innovation and efficiency.
In the 1970s, Stanford University researchers created a rudimentary AI system that asked doctors questions about a patient’s symptoms and provided a diagnosis based on a database of known infections.
In the 1990s and early 2000s, AI algorithms began deciphering complex patterns in X-rays, CT scans and MRI images to spot abnormalities that the human eye might miss.
“While artificial intelligence (AI) undoubtedly holds tremendous potential to improve patient care and health outcomes, I worry that premature deployment of unproven technology could lead to the erosion of trust in our medical professionals and institutions,” he said in a statement.
The original article contains 1,500 words, the summary contains 201 words. Saved 87%. I’m a bot and I’m open source!