The emerging role of artificial intelligence in drug-induced liver injury research: Current innovations

Authors’ contributions

Thanathip Suenghataiphorn: Writing - review & editing, Writing - original draft, Conceptualization. Kanachai Boonpiraks: Writing - original draft, Investigation. Vitchapong Prasitsumrit: Investigation. Narathorn Kulthamrongsri: Writing - original draft. Pojsakorn Danpanichkul: Writing - review & editing, Conceptualization.

Declaration of competing interest

The authors declare that there is no conflicts of interest.

Acknowledgements

This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

[1] Hosack T, Damry D, Biswas S. Drug-induced liver injury: a comprehensive re-view. Therap Adv Gastroenterol. 2023; 16:17562848231163410. https://doi.org/10.1177/17562848231163410.

[2] Sezgin E, Hussain SA, Rust S, Huang Y. Extracting medical information from free-text and unstructured patient-generated health data using natural lan-guage processing methods: feasibility study with real-world data. JMIR Form Res. 2023; 7:e43014. https://doi.org/10.2196/43014.

[3] Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med. 2024; 30:1134-1142. https://doi.org/10.1038/s41591-024-02855-5.

[4] Hayashi PH, Lucena MI, Fontana RJ. RECAM: a new and improved, computer-ized causality assessment tool for DILI diagnosis. Am J Gastroenterol. 2022; 117: 1387-1389. https://doi.org/10.14309/ajg.0000000000001836.

[5] Wu L, Fang H, Qu Y, Xu J, Tong W. Leveraging FDA labeling documents and large language model to enhance annotation, profiling, and classification of drug adverse events with AskFDALabel. Drug Saf. 2025; 48:655-665. https://doi.org/10.1007/s40264-025-01520-1.

[6] Wibowo AS, Chong KT, Tayara H. Enhancing DILI toxicity prediction through integrated graph attention (GATNN) and dense neural networks (DNN). Toxi-cology. 2025; 514:154108. https://doi.org/10.1016/j.tox.2025.154108.

[7] Niu H, Alvarez-Alvarez I, Chen M. Artificial intelligence: an emerging tool for studying drug-induced liver injury. Liver Int. 2025; 45:e70038. https://doi.org/10.1111/liv.70038.

[8] Ashby K, Zhuang W, González-Jimenez A, et al. Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged re-covery from DILI. J Hepatol. 2021; 75:333-341. https://doi.org/10.1016/j.jhep.2021.03.021.

[9] Ma C, Wolfinger RD. Toward an explainable large language model for the auto-matic identification of the drug-induced liver injury literature. Chem Res Tox-icol. 2024; 37:1524-1534. https://doi.org/10.1021/acs.chemrestox.4c00134.

[10] Suenghataiphorn T, Tribuddharat N, Danpanichkul P, Kulthamrongsri N. Bias in large language models across clinical applications: a systematic review. arXiv [csCL]. 2025. https://doi.org/10.48550/ARXIV.2504.02917.