Artificial Intelligence (AI) is playing a transformative role in the field of healthcare by aiding in the diagnosis of diseases. The application of AI in medical diagnosis has the potential to enhance accuracy, speed up processes, and provide valuable insights to healthcare professionals. This topic came to us from UT Health in San Antonio in their release, “UT Health San Antonio: Using AI to diagnose brain disease in seconds.”

AI algorithms are being developed to analyze medical images, such as X-rays, CT scans, MRIs, and mammograms. These algorithms can detect abnormalities, highlight potential areas of concern, and assist radiologists in making more accurate interpretations.

AI is also being applied in digital pathology to analyze tissue samples. AI algorithms can identify patterns in images of biopsied tissues, helping pathologists detect diseases like cancer more efficiently.

AI’s ability to analyze complex medical data, detect patterns, and provide rapid insights is revolutionizing disease diagnosis. By augmenting healthcare professionals’ expertise with advanced algorithms, AI is advancing the accuracy, efficiency, and effectiveness of medical diagnoses across a wide range of conditions.

Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.