Artificial intelligence in liver transplantation: Opportunities and challenges

Table 1 Types of machine learning

Type of machine learning	Methods & data used	Applications
Supervised learning	Labeled data	Predicts known outcome, e.g., SVM
Unsupervised learning	Unlabeled data	Identifies hidden pattern in the data, e.g., K-means, clustering
Semi supervised learning	Hybrid-less labeled & more unlabeled data	Useful when obtaining labeled data is expensive or time consuming
Reinforcement learning	Learning through interaction and feedback (based on evolutionary concept of human behavior of reward and punishment)	Useful in robotics and natural language processing
Ensemble learning	Multiple base models combined to develop more accurate predictive model	Enhance accuracy and reduce overfitting
Deep learning	Based on Artificial Neuronal Network inspired by the human brain neural networks	Useful in handling unlabeled data, e.g., image recognition, natural language processing

SVM: Support vector machines.

Table 2 Comparison between supervised and unsupervised machine learning

Characteristics	Unsupervised	Supervised
Definition	Machine tries to find hidden pattern in the data by itself without human interference	Machines identify the pattern in the new data based on labeled input data with human interference
Input data	Unlabeled	Labeled
When to use	You do not know what you are looking for in the data	You know what you are looking for in the data
Typical tasks	Clustering and association problems	Classification and regression problems
Accuracy of result	May provide less accurate result	Provide more accurate result
Common algorithm	k-means clustering, hierarchical clustering, principal component analysis	Support vector machine
		Decision tree
		Random forest
Example of use	Anomaly detection	Spam filters
	Customer segmentation	Price prediction
	Preparing data for supervised learning	Image identification

Table 3 Commonly used machine learning model in liver transplant

Random forest	Can predict post-transplant outcomes
Decision tree	Useful for classification and regression tasks
Logistic regression	Useful in predicting binary outcomes like survival or graft failure
Support vector machines	Useful in classification, regression, and outlier detection
	can predict patient outcomes from medical records and diagnose diseases
K-Nearest neighbors	Classifies data points based on their proximity to other data points assuming they share similar traits
	Can predict patient outcomes by comparing new data to historical cases
Artificial neural networks	Based on human brain. Consists of multiple layers of neurons
	Useful in image, speech recognition and predictive models
Gradient boosting machines	Ensemble learning method
	Useful in regression and classification problems
	Can handle large scale data
XGBoost	Advanced form of GBM
	Less overfitting as compared to GBM
AdaBoost	Ensemble learning method
	Useful in image classification, sentiment analysis, and fraud detection
RuleFit	Ensemble learning method
	combines decision trees and linear models to form interpretable rules that reveal data patterns
	useful for understanding decisions, especially in regulatory contexts
Tab transformers	Useful to handle tabular data

GBM: Gradient boosting machine; XGBoost: eXtreme Gradient Boosting.

Table 4 Limitation of artificial intelligence and its potential solutions

Limitations of AI	Potential solutions
Data and model drift
Data availability and data quality; (b) Complexity of medical database; (c) Missing data points; (d) Inconsistent reporting of data class; (e) Imbalance in training data; and (f) Perpetuation of socioeconomic factors that affect outcomes	(a) Curated datasheets; (b) Adaptation and development of new model; (c) Multimodal AI model; (d) Inclusion of diverse population in data; and (e) Ranking of AI model based on fairness matrices
Ethical concern
(a) Data privacy and security; (b) Equitable access; and (c) Integration with clinical practice	(a) Transparency of AI models; (b) Adress biases; (c) Ensure data privacy and security; and (d) Establish accountability with regular audit and monitoring
Spectrum bias and overfitting	(a) Diversified and representative training data; (b) Identifying and mitigating bias; (c) Cross validation; and (d) Ensemble learning
Hallucinations due to insufficient or dirty training data	(a) High quality training data; (b) Fine tuning of AI model; (c) Inclusion of fact checking mechanism; and (d) Retrieval-augmented generation
Generalizability (difficulty in achieving the similar level of accuracy in different geography or populations)	(a) To use curated training data set; and (b) Population based validation of AI model
Interpretability (due to Blackbox design of AI model)	(a) Shapley analysis; (b) Explainable AI model; (c) Saliency maps; and (d) Surrogate model

AI: Artificial intelligence.