The models were developed using de-identified data from the Chennai, India-based Apollo Hospitals and the National Institutes of Health. Once trained using the datasets and radiologist input, the AI was able to identify pneumothorax, nodules and masses, fractures and airspace opacities in chest X-rays with the same or better accuracy as experts tasked with examining the same images.
In a post on the Google AI Blog, the study’s authors cautioned that their findings do not imply that AI should fully replace radiologists.
“The model often identified findings that were consistently missed by radiologists, and vice versa. As such, strategies that combine the unique ‘skills’ of both the deep learning systems and human experts are likely to hold the most promise for realizing the potential of AI applications in medical image interpretation,” they wrote.
More articles on AI:
VA launches national AI institute: 4 things to know
Beth Israel Deaconess team using AI to improve liver biopsies
DeepMind co-founder transitions to Google’s applied AI team
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.