5 myths about medical AI, debunked

In 2015, a small research team at Google brainstormed how AI could help improve people’s health. We met doctors like Dr. Kim Ramasamy at Aravind Eye Hospital, who had a lifelong vision of improving access to eyesight-saving patient care. There are not enough specialists in many parts of the world, and having an AI-powered screening system could help doctors reach more patients. We set out to explore whether we could train AI to identify diabetic retinopathy (DR), a leading and growing cause of preventable blindness, and in 2016, we built an AI model that performed on par with eyecare doctors.

In 2018, our partner team at Verily received CE mark for this tool, Automated Retinal Disease Assessment (ARDA). As the prevalence of diabetes rises in low- and middle-income countries, we felt it was most critical to assist with the rising demand and the first patient was screened with ARDA in Madurai, India.

Today, ARDA has screened over 200,000 patients in clinics around the world, from urban cities in the EU to rural communities in India. However, the path to bringing medical AI into a real clinical environment was not easy. To help others who may be embarking on a similar journey, we are sharing our key lessons learned in an article published in Nature Medicine.

Below are 5 key lessons we learned from our work: