PURPOSE OF REVIEW: Machine learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for cli...
PURPOSE OF REVIEW: Classifiers based on artificial intelligence have emerged in all areas of medicine. Although very subtle, many decisions in organ transplantation can now be addressed in a more concisely manner with the support of these classifiers...
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning...
Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data wil...
The aim of this work is to estimate the area-under the blood concentration curve of tacrolimus (TAC) following b.i.d. or q.d. dosing in organ transplant patients, using Xgboost machine learning (ML) models. A total of 4,997 and 1,452 TAC interdose ar...
Therapeutic drug monitoring of mycophenolic acid (MPA) based on area under the curve (AUC) is well-established and machine learning (ML) approaches could help to estimate AUC. The aim of this work is to estimate the AUC of MPA in organ transplant pat...
Transplant international : official journal of the European Society for Organ Transplantation
35859667
Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolvin...
The international journal of medical robotics + computer assisted surgery : MRCAS
37190677
BACKGROUND: Robotic transplant surgery has garnered worldwide attention since 2002. Discussions on this issue have led to more publications over the past decade. This study assessed global robotic organ transplantation studies using bibliometric anal...