Artificial Neural Networks and Deep Learning in Solid Organ Transplantation

Artificial neural networks are now used across many fields, from medicine to chatbots. However, they are often complicated and give "black-box" predictions. To better understand these models, we outline some of the terminology, provide example applications in solid organ transplantation, and offer recommendations for those interested in starting to use such models. Examples and definitions are sourced from PubMed searches as well as a review of several applied texts in machine and deep learning. Neural networks have been applied in transplantation since the 1990s, but more recent applications involve their use to process nontabular data, such as images and text. Although interpretations of model results should take context into account, the power of these models holds great potential for the future in transplantation.

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Publication Details

Journal
Transplantation
Published
2025-07-24
DOI
https://doi.org/10.1097/tp.0000000000005500
Primary Topic
Organ Transplantation Techniques and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Neural Networks and Deep Learning in Solid Organ Transplantation

Aleksandar Đenić, Byron H. Smith
1 links
Transplantation
Organ Transplantation Techniques and Outcomes
article

Artificial Neural Networks and Deep Learning in Solid Organ Transplantation

Aleksandar Đenić, Byron H. Smith
article en

Abstract

Artificial neural networks are now used across many fields, from medicine to chatbots. However, they are often complicated and give "black-box" predictions. To better understand these models, we outline some of the terminology, provide example applications in solid organ transplantation, and offer recommendations for those interested in starting to use such models. Examples and definitions are sourced from PubMed searches as well as a review of several applied texts in machine and deep learning. Neural networks have been applied in transplantation since the 1990s, but more recent applications involve their use to process nontabular data, such as images and text. Although interpretations of model results should take context into account, the power of these models holds great potential for the future in transplantation.

TransplantationVol. 109(12)
Mayo Clinic (US)
Openalex Percentile: Top 69%
Organ Transplantation Techniques and Outcomes
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Artificial Neural Networks and Deep Learning in Solid Organ Transplantation — Aleksandar Đenić, Byron H. Smith · Transplantation (2025) | TGRS Research Map | TGRS