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Cleveland Clinic launches artificial intelligence centre

Cleveland Clinic’s Heart & Vascular Institute. Credit: HealthMonitor.

Cleveland Clinic has opened a new centre to improve the use of artificial intelligence (AI) in healthcare, according to a press release by the Clinic.

Launched by Cleveland Clinic Enterprise Analytics, the centre will focus on developing innovative clinical applications of AI and machine-learning technology to improve healthcare areas such as diagnostics, disease prediction and treatment planning.

The centre will also foster collaboration and communication between physicians, researchers and data scientists. It will offer technical support for AI initiatives at the Clinic and help to conduct research in several areas of medicine.

“Cleveland Clinic has formed the Center for Clinical Artificial Intelligence to translate AI-based concepts into clinical tools that will improve patient care and advance medical research,” said Dr Aziz Nazha, director of the new centre and associate medical director for AI, in a prepared statement.

According to the press release, the centre will also simplify collaboration among physicians, researchers, computer scientists and statisticians in the United States and around the world, as well as between academia and industry, to advance the application of AI in healthcare.

Machine learning technologies are already being deployed in several healthcare projects around the world. AI is used in diagnostics, prognosis, treatment, and patient outcomes.

The Clinic said some projects already are underway already at the new centre, using a cohort of more than one million patients admitted to. These include building models to identify patients with a high risk of death during admission and predict inpatient length of stay and readmission risk with an alleged higher degree of accuracy than existing models.

Many existing cancer-related projects, for instance, include developing models to provide personalised prediction of outcomes, improve cancer detection in pathology slides using computer vision, and predicting response or resistance to chemotherapy using multiple machine-learning algorithms.

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