Rash Decision is a disease-management health and wellness tool that is an alternative to costly doctor visits. An individual simply takes a photo of their skin affliction in question and submits it to the system. The image is then compared to hundreds of thousands of confirmed ailments and instantly gives easy-to-interpret results. Rash Decision identifies photos with up to 99.99% accuracy.
When an individual uses the application, it remains confidential and unseen by anyone else. This is because our system autonomously compares it to other images and does not use human agency.
Rash Decision identifies skin cancer, rashes, allergic reactions, and dermis-level symptoms of illnesses/diseases. This coordination of functions ultimately saves money, as well as lives. By curbing needless ER/clinic visits, avoiding subsequent pharmacy trips, and preempting travel costs, Rash Decision prevents monetary dissipation. The money saving aspects also incentivize users to be more proactive about their health.
As the application matches the user’s picture with great accuracy, we are in no way giving out diagnoses or claiming to be doctors. Rather, we are scientists and technologists using clinically approved Machine Learning algorithms to provide accurate and immediate answers that will save users time and money.
When an individual uses the application, it remains confidential and unseen by anyone else. This is because our system autonomously compares it to other images and does not use human agency.
Rash Decision identifies skin cancer, rashes, allergic reactions, and dermis-level symptoms of illnesses/diseases. This coordination of functions ultimately saves money, as well as lives. By curbing needless ER/clinic visits, avoiding subsequent pharmacy trips, and preempting travel costs, Rash Decision prevents monetary dissipation. The money saving aspects also incentivize users to be more proactive about their health.
As the application matches the user’s picture with great accuracy, we are in no way giving out diagnoses or claiming to be doctors. Rather, we are scientists and technologists using clinically approved Machine Learning algorithms to provide accurate and immediate answers that will save users time and money.
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