A clinical trial tests a treatment in people, and its design determines what it can learn. Two decisions matter most: who is eligible to join, and how patients are assigned to treatment groups. AI contributes to both, but its most visible contribution is patient matching.
Cancer is not one disease. Two tumors that look identical under a microscope can be driven by different genetic changes, and a drug that works for one may do nothing for the other. Traditional trials often enroll broadly and then analyze the results afterward, which means a treatment that helps a small subgroup can be missed because the overall result looks unimpressive. AI can analyze tumor and patient data — genetic profiles, prior treatments, disease stage, and other clinical features — to identify which patients are most likely to respond, so a trial can be designed around that group from the start.
The matching works in both directions. On the trial side, AI helps define eligibility criteria that select the right patients without being so narrow that the trial cannot recruit. On the patient side, it helps identify which of several open trials a given patient is most likely to benefit from, which matters when options are limited and time is short.
The caution is that a model trained on past patients reflects the populations those patients came from. If certain groups were underrepresented in the training data, the model's predictions for them are less reliable, and using those predictions to decide who gets access to a trial can widen existing gaps rather than close them.