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AI system outperforms experts in spotting breast cancer

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An artificial intelligence program has been developed that is better at spotting breast cancer in mammograms than expert radiologists. The AI outperformed the specialists by detecting cancers that the radiologists missed in the images, while ignoring features they falsely flagged as possible tumours. If the program proves its worth in clinical trials, the software, developed by Google Health, could make breast screening more effective and ease the burden on health services such as the NHS where radiologists are in short supply. "This is a great demonstration of how these technologies can enable and augment the human expert," said Dominic King, the UK lead at Google Health. "The AI system is saying'I think there may be an issue here, do you want to check?'"


Artificial intelligence system spots lung cancer before radiologists

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CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


Artificial intelligence system spots lung cancer before radiologists

#artificialintelligence

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


How AI is improving cancer diagnostics

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When a young girl came to New York University (NYU) Langone Health for a routine follow-up, tests seemed to show that the medulloblastoma for which she had been treated a few years earlier had returned. The girl's recurrent cancer was found in the same part of brain as before, and the biopsy seemed to confirm medulloblastoma. With this diagnosis, the girl would begin a specific course of radiotherapy and chemotherapy. But just as neuropathologist Matija Snuderl was about to sign off on the diagnosis and set her on that treatment path, he hesitated. The biopsy was slightly unusual, he thought, and he remembered a previous case in which what was thought to be medulloblastoma turned out to be something else. So, to help him make up his mind, Snuderl turned to a computer.


Artificial intelligence beats doctors in breast cancer diagnosis

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AN ARTIFICIAL INTELLIGENCE programme has been developed which can detect breast cancer from mammograms better than experts, a study found. A new study has found that an AI system developed by Google Health can identify cancer in breast screening mammograms with fewer false positives, and fewer false negatives than radiologists. The programme was developed in collaboration with DeepMind, Cancer Research UK Imperial Centre, Northwestern University, and Royal Surrey County Hospital. Researchers said that the AI model was trained and tuned on anonymised mammograms from more than 76,000 women in the UK and more than 15,000 women in the US to see if it could learn to spot signs of breast cancer. It was then tested on a separate data selection of more than 25,000 women in the UK and over 3,000 women in the US.