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Artificial intelligence (AI) tools trained to detect pneumonia on chest X-rays in a clinical trial in the United States. PLOS Medicine on machine learning and health care. These findings suggest that artificial intelligence in the medical field should be carefully tested for performance across a wide range of populations; otherwise, the deep learning models may not perform as accurately as expected.
The use of convolutional neural networks (CNN) to analyze computer aided diagnosis, has recently suggested that AI image classification may not be generalized to other commonly used portrayed data.
Researchers at the Icahn School of Medicine at Mount Sinai Assessed how AI models identified pneumonia in 158,000 chest X-rays across three medical institutions: the National Institutes of Health; The Mount Sinai Hospital; and Indiana University Hospital. Researchers to study the diagnosis of pneumonia on chest X-rays for its common occurrence, clinical significance, and prevalence in the research community.
In three out of five comparisons, CNNs' performance in diagnosing diseases on X-rays from hospitals outside of its own network was significantly lower than that of the original health system. However, CNNs were able to detect the hospital system when X-ray was acquired with a high-degree of accuracy, and cheated at their predictive task-based pneumonia at the training institution. Researchers found that the difficulty of using a large number of parameters in the treatment of cancer is difficult to predict, such as the types of CT scanners used at hospital and the resolution quality of imaging.
"Eric Oermann, MD, Instructor in Neurosurgery at the Icahn School, explains Eric Oermann, MD. of Medicine at Mount Sinai. "Deep learning models can be used to perform general medical diagnosis, but it can not be taken for granted since patient populations and imaging techniques differ significantly across institutions."
"If CNN systems are used for medical diagnosis, they must be tailored to suit the clinical questions," says John Zech, a medical student at the Icahn School of Medicine at Mount Sinai.
This research builds on papers published earlier this year in the journals Radiology and Nature Medicine, where the framework for applying computer vision and deep learning techniques, including natural language processing algorithms, for identifying clinical concepts in radiology reports for CT scans.
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Materials provided by The Mount Sinai Hospital / Mount Sinai School of Medicine. Note: Content can be edited for style and length.
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