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A large language model for electronic health records npj Digital Medicine

Besides fracture detection, the CNN was also trained to localize the fracture and highlight it with a heatmap. The authors were able to demonstrate that the accuracy in fracture detection by emergency medicine clinicians could be significantly improved with the aid of mentioned CNN [17]. The group also presented their results with a subset of 36′408 wrist radiographs. The network had an AUC of 0.97, a specificity of 93% and sensitivity of 95%. After application of the inclusion/exclusion criteria and addition of supplementary search, 235 articles were included in the final review.

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This can be particularly difficult for generative AI, which produces a wide variety of outputs and is frequently updated by its developers. Consequently, designing a robust study to evaluate its accuracy might be problematic. As a general practitioner with a background in developing AI tools, I’ve observed a range of reactions within the medical community—from excitement to scepticism—regarding the adoption of AI in clinical practice. I believe that AI should not be viewed as a threat, but as a collaborator with potential to enhance patient care and make our lives easier. The AI paraphrasing tool will be invaluable to copywriters like me who need to rework content into new phrasing without losing the original meaning, allowing me to efficiently create fresh copy from existing materials. Regular updates often come with algorithm improvements, expanded language models, and bug fixes.

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