Google uses AI to decode common skin conditions, TB

The AI-powered tool is a web-based application that Google hopes to launch as a pilot later this year, to make it easier to figure out what might be going on with your skin.

"Once you launch the tool, simply use your phone's camera to take three images of the skin, hair or nail concern from different angles. You'll then be asked questions about your skin type, how long you've had the issue and other symptoms that help the tool narrow down the possibilities,"  said Peggy Bui, MD, Product Manager, Google Health.

The AI model analyses this information and draws from its knowledge of 288 conditions to give you a list of possible matching conditions that you can then research further.

For each matching condition, the tool will show dermatologist-reviewed information and answers to commonly asked questions, along with similar matching images from the web.

"The tool is not intended to provide a diagnosis nor be a substitute for medical advice as many conditions require clinician review, in-person examination, or additional testing like a biopsy. Rather we hope it gives you access to authoritative information so you can make a more informed decision about your next step," But said during the Google I/O Developer Conference late on Tuesday.

The company also shared new AI research that aims to improve screening for one of the top causes of death worldwide: tuberculosis (TB) that infects 10 million people per year.

To help catch the disease early and work toward eventually eradicating it, Google researchers have developed an AI-based tool that builds on its existing work in medical imaging to identify potential TB patients for follow-up testing.

In a new study released this week, the Google team found that the right deep learning system can be used to accurately identify patients who are likely to have active TB based on their chest X-ray.

"By using this screening tool as a preliminary step before ordering a more expensive diagnostic test, our study showed that effective AI-powered screening could save up to 80% of the cost per positive TB case detected," said Google.

The AI-based tool was able to accurately detect active pulmonary TB cases with false-negative and false-positive detection rates that were similar to 14 radiologists.

This accuracy was maintained even when examining patients who were HIV-positive, a population that is at higher risk of developing TB and is challenging to screen because their chest X-rays may differ from typical TB cases.

"Later this year, we plan to expand this work through two separate research studies with our partners, Apollo Hospitals in India and the Centre for Infectious Disease Research in Zambia (CIDRZ)," the company informed.