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 […]
Healthcare Resource Shortage As Driver of AI Adoption in Healthcare
As the figure below shows, there is an increasing imbalance between health workforce and demand for clinical services. This imbalance is due to a number of factors such as aging workforce within healthcare; competition from other sectors of the economy; aging population with higher healthcare needs; high burnout rates amongst clinical workers and more. This […]
Reimbursement As a Driver of AI in Healthcare II
Another AI solution received favorable reimbursement decision recently. Viz.ai received FDA clearance in early 2018 for a deep learning system that can detect blockages in the large blood vessels that supply the brain, on CT scans. This system was an interesting break from the dozens of pure diagnostic systems that startups were producing at the time, […]
Reimbursement As a Driver of AI in Healthcare I
If you want adoption of any technology in a meaningful way in the long-term, you need the public and private entities to pay for it. Most modern technologies are out of the reach of the majority of the population in terms of cost. Paying for healthcare is a major headaches for most governments in advanced […]