AI decoder: Unlocking cancer’s hidden patterns
With her expertise in machine learning, Alexis Battle is working to sift through vast amounts of genetic and clinical data, revealing hidden patterns that can inform treatment strategies and improve patient care.
To Alexis Battle, artificial intelligence is not the medicine of tomorrow, but a powerful tool already at work today, beginning to help doctors and researchers decode hidden clues about cancer and other diseases that would be impossible for any human to detect alone.
“Every patient’s disease is a moving target,” says Battle, a professor of biomedical engineering and computer science; the director of the Malone Center for Engineering in Healthcare; and the director for research, strategy, and partnerships for the Johns Hopkins Data Science and AI Institute (DSAI). “It changes over time—from diagnosis to treatment to remission or sometimes recurrence. Add in imaging, lab tests, genomics, and clinical notes, each coming from different parts of the body over time, and it becomes clear that the sheer volume and complexity of this data makes it impossible for any single person to see all of the patterns and piece it all together. This is where AI can help.”