Using artificial intelligence, orthopaedic surgeons at Cleveland Clinic were able to identify the manufacturer and model of an arthroplasty implant with 99% accuracy with plain x-rays alone.
A new methodology developed at Cleveland Clinic combines electroencephalography (EEG) and artificial intelligence to diagnose pain accurately and objectively.
Researchers have developed the first machine-learning model that can predict with high accuracy MDS patient response or resistance to HMA treatment.
COVID-19 has catapulted nursing into the digital world, embracing a new generation of technology in healthcare. ACNO Dr. Nelita Iuppa shares insight on how nurses can prepare for digital nursing practice.
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Scientists and clinicians from Cleveland Clinic and Case Western Reserve University received a $1.15 million grant from the NCI to develop AI tools that assist in the differentiation of tumor recurrence versus scar tissue on postoperative MRI scans in patients with certain colorectal and brain cancers.
Learn more about recent advances in machine learning and hematological malignancies in this review coauthored by the Director of the Center for Clinical Artificial Intelligence
One driver of increased pharmaceutical spending is the high failure rate of expensive and time-consuming randomized control trials. deepDTnet, a network-based deep learning methodology for novel target identification and in silico drug repurposing, may aid in the development of novel, effective treatment strategies for complex diseases.
Technology-enabled data capture is accelerating progress in biomechanics and in the care of neurological disorders. A leading researcher shares insights on some of the advances in a new podcast episode.
Progress toward use of machine learning and deep learning techniques to inform epilepsy surgery decisions for individual patients is well underway. We share a status report on Cleveland Clinic’s experience in this space.
Cleveland Clinic researchers have trained an advanced computer network to find subtle radiation sensitivity features in the CT scans of individual lung cancer patients that can predict the likelihood of successful radiotherapy outcomes. The network can generate a personalized radiation dose plan that reduces the probability of treatment failure to less than 5%.