AI in Pathology I
If a radiology scan suggests a mass that can be concerning for something serious (of even if not,) what comes next is taking tissue from that mass and examining it. When a cancer is suspected by a radiologist, a biopsy is taken, and that lump of tissue is sent to a pathology lab. There, a […]
AI in Radiology Imaging Acquisition
Whilst X-rays accounted for most of the scans performed, it is estimated that they accounted for less than 20% of total radiologist reading time, due to faster reading times per scan. The net effect of the changing complexion of scan types forecast over the next five years is, not only are diagnostic procedures increasing, but […]
AI in Radiology IV
A series of studies have described the use of deep learning algorithms to detect abnormalities in radiology, yielding promising results. ‘qXR’, Qure.ai’s chest X-ray interpretation tool, is able to automatically detect and localize up to 29 abnormalities, including those indicative of possible lung cancer. There are several features of chest radiographs (such as sharply circumscribed […]
AI in Radiology III
While medical imaging is very well suited for the use of machine learning-based pattern recognition, adoption within healthcare providers is a notoriously slow process due to a lack of trust in AI amongst clinical staff, an unclear economic value proposition which has not been fully proven yet, complex data integration due to siloed and proprietary […]
AI in Radiology II
Initial benefits of AI in this realm include providing earlier detection of a potentially life-threatening event and ensuring higher accuracy in reading these studies. If a patient presents with a stroke or a collapsed lung, an algorithm that can immediately look for abnormalities upon completion of the scan and alert the radiologist if it finds […]
AI in Radiology I
This is the initial frontier of AI in healthcare. Why? images are for the most part digital files with structured data that can be used to develop and validate a model to perform a narrow task such as finding tumor on a CT scan or a fracture on an X-ray. The infrastructure that has been […]
AI in Medical Diagnostics
Diagnostics is probably the first frontier for AI in healthcare. Much of what happens in healthcare is about collecting data (symptoms, exam data, labs, genetics, etc) and interpreting it to make determinations about a patient’s health or medical issues. We have developed great capabilities in the last century in diagnostic testing. Often, a piece of […]
Partnerships Are Needed to Make the Promise of AI in Healthcare A Reality
More than a dozen major health systems, with millions of patients in 40 states, are banding together to launch Truveta, a new data-driven organization focused on collaborative approaches to precision medicine and population health. The goal is to innovate care delivery and spur development of new therapies by leveraging billions of clinical data points with […]
AI to Address Clinical Mistakes, Inefficient Care Pathways, and Non-Personalized Care II
In a survey by KPMG, many healthcare executives expressed interest and optimism about the potential impact of AI in their businesses. 89 percent of respondents said that AI is already creating efficiencies in their systems, and 91 percent say AI is increasing patient access to care. Executives are particularly optimistic about AI’s ability to accelerate […]
AI to Address Clinical Mistakes, Inefficient Care Pathways, and Non-Personalized Care I
It is not to be underestimated that many of the issues that are slowing down the adoption of AI in healthcare such as fragmented data, misaligned economic incentives, legacy IT systems, and more are currently leading to less than ideal practice of medicine. That means patient outcomes that are far from optimal, waste of resources […]