Magic Pills, Machine Learning, and the Future of Health

July 12, 2018.

Technology has always promised a better future … eventually. Somehow the real breakthroughs have always seemed to be just around the corner. But somehow, when we weren’t quite paying attention, the future actually arrived. Thanks to forward-thinking researchers calling on advances in genomics, artificial intelligence, food science, and drug hacking, a more resilient, enlightened, and cognitively-, physically-, and sexually-enhanced human already walks among us. (And her skin is amazing.) Here, eight exciting new health technologies — and where they’re heading next.

Machine-learning skincare
What it is: Global spending on skincare tops more than $50 billion a year — and dermatologists say much of that that goes to waste on products that are ineffective or incompatible. Launching in August 2018, Atolla Skin Lab uses an original, and proprietary database developed at MIT in conjunction with a machine-learning algorithm to connect combinations of ingredients to skin attributes, allowing for hyper-personalized product suggestions based on factors including skin hydration, oil content, sun damage, age, and skin concerns and goals.

What’s the sell: “Most people buy based on the category they think they fit in, and not the one they actually fit in,” says Boston dermatologist Ranella Hirsch, MD, FAAD, part of Atolla’s founding team. After an initial physical analysis (including high-res and UV photos and a short questionnaire) at one of Atolla’s retail pop-ups, people complete an online profile, reporting environmental and lifestyle data. Atolla’s proprietary algorithm then leverages the various data points to devise a custom product, down to the key, active ingredient combination, on the spot. The company declined to reveal investors, but Hirsch says there is interest “from the largest beauty brands.”

What’s next: Atolla’s in-development smartphone app calls on “computer vision” — the technology by which computers take in and analyze images — to track results, improving the algorithm and allowing the brand to make adjustments if necessary.

See the full article on Medium.