With the advent of artificial intelligence (AI), predictive medicine is becoming an important part of healthcare, especially in cancer treatment. Predictive medicine uses algorithms and data to help doctors understand how a cancer might continue to grow or react to specific drugs making it easier to target precision treatment for individual patients. While AI is important in this work, researchers from University of Maryland School of Medicine (UMSOM) say that it should not be relied on exclusively.

Instead, AI should be combined with other methods, such as traditional mathematical modeling, for the best outcomes. All health computational models need three key components to work: datasets, equations, and software. Then, after generating data comes leveraging it to improve early diagnoses, discover new treatments, and aid understanding of the diseases. 

Taken together, the two commentaries set a foundational approach to generating, analyzing, and ethically sharing data to benefit both patients and science. Explaining the argument of the Nature Biotechnology commentary Dr. Fertig said,  AI and mathematical models differ dramatically in how they arrive at an outcome.AI models first must be trained with existing data to make an outcome prediction, while mathematical models are directed to answer a specific question using both data and biological knowledge.

That means that when data is sparse as it often is in newer cancer treatments such as immunotherapy AI can over generalize, resulting in biased or inaccurate outcomes that cannot be reproduced by other scientists. Mathematical modeling, on the other hand, uses known biological mechanisms, learned from scientific experiments, to explain how it arrived at an outcome.

Source: https://www.medschool.umaryland.edu/news/2025/while-ai-could-be-the-game-changer-in-predicting-health-outcomes-it-should-not-be-the-only-method.html