Clemson-MUSC AI Hub

The Clemson-MUSC AI Hub, a joint effort by Clemson University and the Medical University of South Carolina, focuses on AI in biomedical science and healthcare research. This alliance, funded by both institutions, promotes AI technologies in clinical research, computational biology, and healthcare informatics. Our mission includes creating an industry portal for collaboration, developing ethical AI solutions in healthcare, catalyzing AI research for funding, and building strong partnerships to lead in biomedical AI research.  

What are the applications of AI in healthcare and how do they vary? 

Artificial Intelligence: This feature enables machines to learn tasks instead of just executing programmed computations, marking a significant advancement in technology. 

Machine Learning: This AI approach involves developing algorithms to analyze and predict outcomes based on input data, such as personalized news feeds or traffic forecasts. 

Neural Networks: Modeled after the brain, these algorithms process signals through interconnected nodes (artificial neurons), effectively recognizing and predicting patterns in neural signals crucial for brain functions. 

Deep Learning: This form of machine learning, using layered computations, creates deep neural networks capable of learning from complex, unstructured data, evident in applications like voice-controlled assistants and self-driving cars. 

How does AI contribute to enhancing healthcare and biomedical research? 

Radiology: AI aids in interpreting imaging results, potentially detecting subtle changes that may elude human observation. 

Imaging: AI is utilized in assessing post-surgery outcomes like facial reconstructions, enhancing precision in medical procedures. 

Telehealth: Wearable tech, integrated with AI, constantly monitors patients, detecting physiological changes and providing early alerts for conditions like asthma attacks. 

Clinical Decision Support Systems: AI is extensively used to develop systems assisting clinicians with treatment decisions by leveraging health data and case knowledge.