Managing medication after a liver or kidney transplant is a delicate balancing act. Doctors must ensure patients have enough of the drug tacrolimus to prevent organ rejection, while keeping levels low enough to avoid toxicity. Because every patient is unique, finding the right dose can be complex.
A review of current research shows that machine learning (ML) offers a way to make these doses more personal. These computer models can look at many different clinical details at once to help doctors choose initial doses and manage transitions between different forms of the medication. This helps move away from one-size-fits-all dosing.
While these tools show promise for individual treatment, they are not yet used widely in everyday clinics. Current models also struggle to adjust drug levels automatically over a long period of time. For now, these systems serve as a helpful tool for research and specialized care rather than a replacement for standard clinical practice.
