5 recent studies exploring AI in healthcare

In the past decade, the medical research community has become increasingly interested in artificial intelligence's potential to transform healthcare for the better by reducing workflow inefficiencies, predicting health outcomes and speeding up diagnoses.

Below are five key AI studies that have been published recently:

  1. "Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model": Researchers from New Hyde Park, N.Y.-based Northwell Health's research arm developed an artificial intelligence tool to predict which patients will remain stable overnight and don't need to be awoken for vital monitoring. The tool cut in half the number of patients who were awoken during the night for vital sign checks, misclassifying less than two of 10,000 cases.

  2. "Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies": Researchers developed an artificial intelligence tool to improve pathologists' grading of prostate needle biopsies, finding significant increases in grading agreement.

  3. "Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study": The research team developed and validated a real-time deep convolutional neural networks system for the detection of early gastric cancer.

  4. "Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography": Researchers developed a deep learning-based artificial intelligence algorithm to help detect myocardial infarction using electrocardiography to speed up the diagnosis process. They found the algorithm could accurately detect cases of myocardial infarction faster than physicians.

  5. "Deep learning-based classification of primary bone tumors on radiographs: A preliminary study": The research team developed a deep learning model to classify primary bone tumors from preoperative radiographs and compared its performance to that of radiologists. They found it operated with a similar accuracy to subspecialists and better performance than junior radiologists.

More articles on artificial intelligence:
3 hospital execs: How to ensure medical AI is trained on sufficiently diverse patient data
Microsoft invests in AI virtual health platform: 4 details
Alphabet CEO pledges changes after exit of Google AI leader

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