A team of researchers has refined a method that improves the reliability of artificial intelligence (AI) in image-based medical diagnoses. This approach allows for more accurate distinction between inflammation and tumors, reducing false positives and negatives.

The advancement integrates deep learning with the patient's clinical data (medical history, blood markers, symptoms) to contextualize the images. By incorporating this metadata, the algorithm becomes more robust and reliable, and less prone to isolated errors.

It has been particularly applied in the detection of cancers and inflammatory diseases: areas where the margin of error must be minimal. This type of tool can reduce unnecessary biopsies and offer faster and more reliable diagnoses for patients.

From an aesthetic point of view, this advance can facilitate early diagnoses of melanoma, skin cancer or other dermal lesions, integrating with technologies such as dermatoscopy, reflectance or specialized AI.

This advance confirms that technology is not meant to replace doctors, but to empower them. With more reliable tools, professionals can make more informed, ethical, and safe decisions.