Artificial intelligence in liver transplantation: Opportunities and challenges

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.