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MedTech Outlook | Tuesday, July 27, 2021
Artificial Intelligence can help people identify skin problems to make better-informed decisions about their next step.
FREMONT, CA: Artificial intelligence (AI) can help clinicians provide better care for patients and manage the disease, from enhancing breast cancer screening to more effectively detecting tuberculosis. Companies can also discover new ways for people to stay better informed about their health by combining these improvements in AI with other technologies, such as smartphone cameras.
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How does the AI-powered dermatology tool work
Every year, about ten billion Google searches for skin, nail, and hair problems are conducted. Dermatological problems affect numerous people globally, but there is a worldwide scarcity of dermatologists. While many people's first move is to go to a Google Search bar, describing what they see on the skin with words alone can be challenging.
The AI-powered dermatology assist tool can be defined as a web-based application intended to offer help to people understand the problem with their skin. The users can use the phone's camera to capture three images of the skin, hair, or nail problem from different angles. The tool will then ask them questions about their skin type, how long they have been suffering from the problem, and other symptoms to help narrow down the options. The AI model analyzes this data and uses its knowledge of 288 conditions to generate a list of potential matches, which users can examine further.
The program will display dermatologist-reviewed material, answers to frequently asked questions for each matching condition, and similar matching photos from the web.
Developing an AI model that evaluated problems for all skin types
This dermatology software is the result of more than three years of machine learning research and development. The model accounts for age, sex, color, and skin types, like light skin that doesn't tan to brown skin that hardly burns to ensure that the developers are designing for everybody. The developers used de-identified data to construct and fine-tune the model, which included roughly 65,000 photos and case data of identified skin problems, numerous curated skin concern images, and several examples of healthy skin from various demographics.
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