Decoding AI for transplantation: many models, one patient: why there is no single “AI for transplantation” – and what to ask instead

Consider a routine follow-up with a kidney transplant recipient: You are sitting with your patient presenting with an elevated serum creatinine.To assess potential allograft rejection or injury you do not reach for a single piece of information.You look at the trend rather than one value, you check the proteinuria, you recall the donor-specific antibodies from last year, you open the most recent biopsy results and you weigh all of it against everything you know about this particular patient.In a few minutes you have combined five different kinds of information into one judgement, almost without noticing.This ordinary act-holding many sources together and deciding what they mean as a whole-turns out to be the single hardest thing for artificial intelligence to do.The current landscape of AI tools in transplantation largely features fragmented, siloed architectures.Most of these tools read a single type of data-the biopsy, the transcriptomic signature, or the cfDNA value alone-and are built to answer one specific diagnostic or prognostic question.