3 posts tagged with "dermatology"

In a small town in the interior of the Northeast, a primary-care physician suspects melanoma in a 62-year-old patient. She writes a referral. The referral describes the lesion in words: there is no standardised photograph, no objective severity measurement, no structured baseline. The patient joins the queue. The queue moves at the speed of a continent: before telemedicine programmes began to compress it, the average wait for a dermatology appointment in the SUS reached 294 days. [^1]

Skin cancer does not wait 294 days.

This is the access deficit that defines Brazilian dermatology today, and it is a different deficit from the one other countries face. Brazil is a continental country with a dual health system, and the dermatology bottleneck is not one queue: it is two parallel queues, both under pressure, each for slightly different reasons. A clinically validated layer of artificial intelligence (AI) that covers the full spectrum of dermatology (skin cancer, chronic disease and longitudinal follow-up) and is certified by ANVISA, with CE marking and MHRA registration, is the missing piece in both queues.

Dermatology encompasses a broad spectrum of diseases, yet there remains a disproportionate focus on malignant conditions such as melanoma and non-melanoma skin cancers. This narrow approach overlooks the significant global burden of other dermatological conditions, which vastly outnumber malignant diseases.

Melanoma is a very important disease to consider. No one is denying that. It is a malignant disease with a high mortality rate. However, understanding the actual prevalence of dermatological conditions is crucial.

Introduction​

Clinical trials are the engine that pushes forward medical science. What comes out as obvious is that the tools at the disposal of those at the forefront of progress should be as advanced and cutting-edge as the research that is been worked on.

The research that will propel us to the future shouldn't take place using tools of the past, especially if there are new tools that increase the reliability of outcome measures.

Legit.Health takes up the challenge of providing researchers with a modern, useful and reliable tool for their clinical trials by incorporating deep learning algorithms and image analysis technology into an easy-to-use and well-designed interface.