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Lessons from AI-assisted surgery monitoring for improved healthcare outcomes #

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Teodor Grantcharov, a professor of surgery at Stanford, has developed AI-powered “black boxes” for operating theaters to reduce medical errors during surgeries. These devices record everything in the operating room and use artificial intelligence to help surgeons analyze the data. While these black boxes are in use in many institutions, there are concerns about privacy and staff acceptance. Grantcharov designed the system to protect the identities of patients and staff, but some hospitals have faced resistance from doctors and nurses who are suspicious of the new technology.

Lessons learned from this story include the importance of privacy in AI adoption, the need to win people over before implementing new technologies, and the fact that more data doesn’t always lead to solutions. Some hospitals have reported small improvements based on black-box data, but there is a lack of large, peer-reviewed studies showing the effectiveness of these devices in reducing patient complications and saving lives.

In a related development, physicist Sam Dillavou at the University of Pennsylvania has created a low-power alternative to energy-intensive GPUs for machine learning using a simple circuit. This innovation could offer a more sustainable solution for AI computing. Additionally, companies like OpenAI are reporting on influence operations that use AI tools, highlighting the need for transparency in AI applications.

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Source link: https://www.technologyreview.com/2024/06/11/1093484/what-using-artificial-intelligence-to-help-monitor-surgery-can-teach-us/amp/

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