Journal of Hepatology / 2023

Artificial intelligence, machine learning, and deep learning in liver transplantation.

M. Bhat, Madhumitha Rabindranath, B. Chara, Douglas A. Simonetto

Computer VisionFoundation ModelsReinforcement Learning

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

169 citations5 influential

Full paper

Read the original paper

Source page

A direct open-access PDF is not available in the database yet. Use the source page or learning resources below to open the complete paper from the publisher or index.

Learning resources

Google Scholar referencesGoogle Scholar Papers with Code searchPapers with Code Semantic Scholar paper pageSemantic Scholar YouTube explanationsYouTube

Reading state

Discuss in ChatGPT

Uses your own ChatGPT account. The paper context is copied into a tutor prompt before ChatGPT opens.

Preview prompt

You are my AI/ML research paper instructor. I want to deeply understand the paper below.

First, teach it in layers:
1. One-paragraph intuition.
2. Problem statement and why it mattered.
3. Key method, architecture, or algorithm.
4. Important equations or mechanisms, explained intuitively.
5. Experiments and evidence.
6. Limitations, assumptions, and failure modes.
7. How this paper influenced later AI/ML/Deep Learning/GenAI work.
8. A 30-minute study plan with checkpoints.
9. Quiz me with 5 questions and wait for my answers.

When something is not available in the attached context, say what is missing and infer carefully.

### Paper attached as context
Title: Artificial intelligence, machine learning, and deep learning in liver transplantation.
Authors: M. Bhat, Madhumitha Rabindranath, B. Chara, Douglas A. Simonetto
Year: 2023
Venue: Journal of Hepatology
Categories: Computer Vision, Foundation Models, Reinforcement Learning
Citations: 169
Paper URL: https://www.semanticscholar.org/paper/f6f98d7b0815dad170b269bd8e65ebca3e153e1c
Open PDF: Not available

Abstract:
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Artificial%20intelligence%2C%20machine%20learning%2C%20and%20deep%20learning%20in%20liver%20transplantation.)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Artificial%20intelligence%2C%20machine%20learning%2C%20and%20deep%20learning%20in%20liver%20transplantation.)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/f6f98d7b0815dad170b269bd8e65ebca3e153e1c)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Artificial%20intelligence%2C%20machine%20learning%2C%20and%20deep%20learning%20in%20liver%20transplantation.+paper+explained)