Samuel (Sandy) Aronson, ALM, MA, executive director of IT and AI Solutions for Mass General Brigham Personalized Medicine and senior director of IT and AI Solutions for the Accelerator for Clinical Transformation, is the corresponding author of a paper published in NEJM AI that looked at whether generative AI could hold promise for improving...
Mass General BrighamAug 13 2024 Samuel Aronson, ALM, MA, executive director of IT and AI Solutions for Mass General Brigham Personalized Medicine and senior director of IT and AI Solutions for the Accelerator for Clinical Transformation, is the corresponding author of a paper published in NEJM AI that looked at whether generative AI could hold promise for improving scientific literature review of variants in clinical genetic testing. Their findings could have a wide impact beyond this use case.
What question were you investigating? We investigated whether generative AI can be used to determine: 1) whether a scientific article contains evidence about a variant that could help a geneticist's assessment of a genetic variant and 2) whether any evidence found about the variant supports a benign, pathogenic, intermediate or inconclusive conclusion.
What did you find? Generative AI performed relatively well, but more improvement is needed for most use cases. However, as we ran our tests repeatedly, we observed a phenomenon we deemed important: running the same test dataset repeatedly produced different results. Through repeated running of the test set over time, we characterized the variability. We found that both drift and nondeterminism were present. We developed visualizations that demonstrate the nature of these problems.
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