AI-Assisted Dermatology

Skin cancer ranks as one of the most prevalent cancers worldwide, and its early detection is crucial for effective treatment. The current diagnostic process, however, faces significant challenges. It can be intricate, time-intensive, and occasionally leads to inaccurate diagnoses. With the growing need for dermatological services and the scarcity of specialized professionals in this field, there's an urgent demand for more efficient, precise, and broadly accessible diagnostic methods.

Machine Learning solutions are uniquely suited for the task due to their ability to analyze vast datasets, recognize complex patterns, and provide consistent, repeatable analyses. While fully replacing doctors with AI in such a sensitive and complex field is not advisable, AI can serve as an exceptional assistant. It can aid dermatologists by providing a preliminary analysis, which can then be reviewed and confirmed by the experts. This synergy between AI and medical professionals can enhance diagnostic accuracy, reduce the workload on dermatologists, and potentially speed up the treatment process, leading to better patient outcomes. Moreover, AI tools can be particularly beneficial in regions where access to specialized dermatological care is limited, thus democratizing health care access.

Fast Code AI's Contribution

At Fastcode AI, we are building reliable AI healthcare solutions aimed at making dermatological care more accurate, efficient, and accessible for everyone. Our main works include:

Data engine
  • Benign/Malignant Classification
    We've developed a robust ML model that accurately classifies skin lesions as benign or malignant. This distinction is crucial for determining the urgency and type of treatment required.

  • Lesion Type Detection and Segmentation
    The ML system's capabilities also extends to the intricate task of identifying the specific type of lesion. It can detect the specific type of lesion and segment it precisely, providing detailed information about the lesion's size, shape, and boundaries. This information is essential for treatment planning and monitoring.