Identify and diagnose various pulmonary and thoracic diseases. These diseases can include conditions like pneumonia, tuberculosis, lung cancer, COVID-19, pulmonary edema, and other abnormalities affecting the lungs, heart, or surrounding structures. Diseases are detected

A specialized imaging technique used to screen and diagnose breast cancer. Mammography utilizes low-dose X-rays to create detailed images of the breast tissue, helping radiologists identify early signs of cancer, often before any symptoms appear. It is considered one of the most effective tools for early detection of breast cancer, improving treatment outcomes and survival rates.

Involves the use of Computed Tomography (CT) scans and Deep Learning to diagnose and evaluate diseases and abnormalities in the liver and kidneys. CT scans provide detailed cross-sectional images of the body, allowing for the identification of structural changes, masses, lesions, and other abnormalities that might be difficult to detect through other imaging techniques

The application of deep learning techniques to analyze histopathology images for the diagnosis and classification of diseases, particularly cancer. Histopathology involves the microscopic examination of tissue samples to detect abnormalities at the cellular level, and deep learning has emerged as a powerful tool to automate and enhance the accuracy of this process.

Deep learning architecture used to analyze dermoscopic images. They automatically learn hierarchical features from the images, from low-level features like edges to high-level features such as lesion patterns, enabling them to classify skin lesions with high accuracy.

The use of advanced natural language processing (NLP) models like GPT (Generative Pre-trained Transformer) to aid in reducing and preventing stunting in children. Stunting is a form of chronic malnutrition that impairs physical and cognitive development, and it is typically caused by inadequate nutrition and poor health conditions, particularly in the first 1,000 days of life.

LLMs can play a significant role in stunting prevention by enhancing data analysis, improving communication strategies, supporting health interventions, and disseminating knowledge to various stakeholders involved in child health and nutrition.

Identify signs and symptoms of mental health conditions through the analysis of text, speech, or other forms of communication. LLMs, such as GPT, are trained on vast amounts of data and can understand human language, making them useful in detecting patterns associated with mental health issues like depression, anxiety, PTSD, and others.