AI in Healthcare Blogs

Consistent with radiology and pathology, dermatology is another specialty where a lot of the diagnosis comes down to a visual inspection of the skin and doing pattern recognition. This is good fit for AI to assist. Assist is the right … Read More

In 2021, the FDA granted its first clearance for a cancer diagnosis  AI program to Paige, a New York-based company launched in 2018 with data and digital pathology tech from Memorial Sloan Kettering Cancer Center.  The product, Paige Prostate, analyzes … Read More

Four factors came together to make digital pathology a must-have instead of a nice-to-have. First, COVID-19 sent pathologists home and challenged them to figure out new remote workflows. Second, cloud storage got cheaper and more robust, allowing for the sharing … Read More

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 … Read More

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 … Read More

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 … Read More

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 … Read More

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 … Read More

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 … Read More

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 … Read More