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Researchers from New Zealand’s University of Auckland have identified new artificial intelligence (AI) use cases in healthcare designed to streamline medical processes for patients and health practitioners.
The new strides in AI are supported by computer vision, a subdivision that uses machine learning to teach systems how to analyze and understand images. A study published in Nature Medicine highlighted the upsides of AI-enabled systems that can spot abnormalities during surgical procedures, matching leading surgeons’ abilities.
The team, led by Chris Varghese, confirmed that initial studies have yielded promising results with test models demonstrating proficiency in identifying surgical tools and complex body parts under different circumstances.
“We are seeing a lot of exciting research looking at what we call computer vision, where AI is trying to learn what surgeons see, what the surgical instruments look like, what the different organs look like, and the potential there is to identify abnormal anatomy,” said Varghese.
Varghese says the health sector is on course to get autonomous robotic surgeons by the end of the decade.
Several utilities in surgical procedures have come to light, including using AI models to ascertain the best route to take in preparing a patient for surgery. This capability could play a key role in removing cancers and tumors while being extended to other benign medical procedures.
Outside of the operating table, the report noted a rising trend of health institutions turning to AI for various administrative processes. Hospitals are using AI to make patient waitlists fairer by prioritizing individuals in dire need of medical attention, while others are using it to summarize patient health histories.
“We are using automated algorithms to triage really long waiting lists,” said Varghese. “So, getting people prioritized and into clinics ahead of time, based on need, so the right patients are seen at the right time.”
Risks could ruin the party
For all the attendant benefits associated with AI in medicine, critics are highlighting risks that could dent the industry’s plans for a mass pivot to large language models (LLMs).
Privacy issues currently top the list for critics with the loudest voices claiming that a data breach of AI systems could have dire consequences on patients. Others allege that LLMs used in healthcare are not fool-proof and may be prone to errors or demonstrate traits of sycophancy.
To mitigate the risk, a number of researchers are pining for stricter rules for the deployment of AI in healthcare by regulatory authorities, while others are seeking an integration with blockchain technology for transparency.
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