A computational patient is a digital representation of a person or patient profile used to simulate how they may respond to a drug, therapy, or medical intervention. It may combine lab results, imaging, genetics, clinical history, and other biological data to estimate likely outcomes before treatment is given.
In cyber security and healthcare AI, this matters because the model’s value depends on data integrity and trust. If training data are poisoned, inputs are altered, or patient records are incomplete, the simulated response can be misleading. Defenders therefore need strong access controls, audit logs, validation against real-world outcomes, and checks for bias and drift. In practice, computational patients are used to test treatment options, but only as decision support: they help compare scenarios, not replace clinical validation.