Review Article on: AI-Driven Precision Medicine in Liver Disease: Microbiome and Nanotechnology Integration
DOI:
https://doi.org/10.64261/m76tpv25Keywords:
Liver disease, precision medicine, microbiome, nanotechnology, hepatocellular carcinoma, artificial intelligence, regenerative therapy, biomarkersAbstract
Liver diseases such as metabolic dysfunction–associated steatotic liver disease (MASLD), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC) remain global health challenges. Conventional therapies offer limited success due to interindividual variability in genetics, metabolism, and microbial composition. The emergence of artificial intelligence (AI), genomic medicine, microbiome modulation, and nanotechnology-based drug delivery has redefined precision hepatology. AI-driven models enhance diagnosis, prognostication, and therapeutic decision-making; genomic and epigenomic insights enable personalized pharmacotherapy; microbiome engineering and nanocarrier-based delivery systems improve therapeutic targeting and efficacy. This review integrates these multidimensional innovations highlighting AI applications, microbiome therapeutics, nanomedicine, regenerative strategies, and emerging biomarkers toward a unified model of precision medicine in liver disease management.
References
1 Gan, C., Yuan, Y., Shen, H., Gao, J., Kong, X., Che, Z., ... & Xiao, J. (2025). Liver diseases: epidemiology, causes, trends and predictions. Signal Transduction and Targeted Therapy, 10(1), 33. DOI: https://doi.org/10.1038/s41392-024-02072-z
2 Zhang, W., Shao, T., Wang, C., Wang, S., & An, L. (2026). Advances in pharmacological interventions for hepatic fibrosis: from pathogenic mechanisms to novel therapeutic targets. Annals of Medicine, 58(1), 2613502. DOI: https://doi.org/10.1080/07853890.2026.2613502
3 Colonna, G. (2025). Advancing Liver Cancer Treatment through Dynamic Genomics and Systems Biology: A Path Toward Personalized Oncology. DOI: https://doi.org/10.20944/preprints202509.1290.v1
4 Tilg, H., Petta, S., Stefan, N., & Targher, G. (2026). Metabolic dysfunction–associated steatotic liver disease in adults: a review. Jama, 335(2), 163-174. DOI: https://doi.org/10.1001/jama.2025.19615
5 Parvin, N., Joo, S. W., Jung, J. H., & Mandal, T. K. (2025). Multimodal AI in biomedicine: pioneering the future of biomaterials, diagnostics, and personalized healthcare. Nanomaterials, 15(12), 895. DOI: https://doi.org/10.3390/nano15120895
6 Li, Y., Wang, X., Zhang, J., Zhang, S., & Jiao, J. (2022). Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease (NAFLD): a systematic review. Reviews in Endocrine and Metabolic Disorders, 23(3), 387-400. DOI: https://doi.org/10.1007/s11154-021-09681-x
7 Debelee, T. G., Kebede, S. R., Schwenker, F., & Shewarega, Z. M. (2020). Deep learning in selected cancers’ image analysis—a survey. Journal of imaging, 6(11), 121. DOI: https://doi.org/10.3390/jimaging6110121
8 Rabindranath, M. (2023). Automated Diagnosis of Liver Allograft Fibrosis using Machine Learning Approaches (Master's thesis, University of Toronto (Canada)).
9 Sumitha, M. S., & Xavier, T. S. (2026). AI in Electrochemical Healthcare Devices. In AI-Driven Innovations in Electrochemical Technologies for Sustainable Energy Solutions (pp. 284-331). Bentham Science Publishers. DOI: https://doi.org/10.2174/9798898811860125010013
10 Wakabayashi, S. I., Kimura, T., Tamaki, N., Iwadare, T., Okumura, T., Kobayashi, H., Yamashita, Y., Tanaka, N., Kurosaki, M., & Umemura, T. (2025). AI-Based Platelet-Independent Noninvasive Test for Liver Fibrosis in MASLD Patients. JGH open : an open access journal of gastroenterology and hepatology, 9(4), e70150. https://doi.org/10.1002/jgh3.70150 DOI: https://doi.org/10.1002/jgh3.70150
11 Beeram, Ramesh & Jana, Gnaneswara & Kotta, Ravi & Paluri, Lakshmi & Kanakam, Siva. (2026). AI-Based Hepatocellular Carcinoma (HCC) Detection and Prediction System. International Journal For Multidisciplinary Research. 8. 10.36948/ijfmr.2026.v08i02.72674. DOI: https://doi.org/10.36948/ijfmr.2026.v08i02.72674
12 Vianna, Pedro & Kulbay, Merve & Boustros, Pamela & Calce, Sara-Ivana & Larocque-Rigney, Cassandra & Patry-Beaudoin, Laurent & Luo, Yi & Chaudary, Muawiz & Kadoury, Samuel & Nguyen, Bich & Montagnon, Emmanuel & Belilovsky, Eugene & Wolf, Guy & Chassé, Michaël & Tang, An & Cloutier, Guy. (2023). Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images. 1-4. 10.1109/IUS51837.2023.10307501. DOI: https://doi.org/10.1109/IUS51837.2023.10307501
13 H. Xu et al., "Vision Transformers for Computational Histopathology," in IEEE Reviews in Biomedical Engineering, vol. 17, pp. 63-79, 2024, doi: 10.1109/RBME.2023.3297604 DOI: https://doi.org/10.1109/RBME.2023.3297604
14 Rathee, S., MacMahon, M., Liu, A., Katritsis, N. M., Youssef, G., Hwang, W., ... & Han, N. (2022). DILI C: An AI-based classifier to search for drug-induced liver injury literature. Frontiers in genetics, 13, 867946. DOI: https://doi.org/10.3389/fgene.2022.867946
15 P. Yesankar, C. Puri and P. M. Gote, "AI-Powered Clinical Decision Support Systems (CDSS): Challenges, Benefits, Applications, and Future Directions," 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS), Prawet, Thailand, 2025, pp. 1192-1197, doi: 10.1109/ICMLAS64557.2025.10969014. DOI: https://doi.org/10.1109/ICMLAS64557.2025.10969014
16 Zhang, L., Huang, Y., Huang, M., Zhao, C. H., Zhang, Y. J., & Wang, Y. (2024). Development of cost-effective fatty liver disease prediction models in a Chinese population: statistical and machine learning approaches. JMIR Formative Research, 8, e53654 DOI: https://doi.org/10.2196/53654
17 Lakshmi Vara Prasad G, Murali A, Bhaskar Reddy Y V, Haribabu Marturi, and Valeru Vision Paul. 2025. Deep Learning in Medical Imaging: Image Processing - From Augmenting Accuracy to Enhancing Efficiency. In Proceedings of the 2024 7th International Conference on Digital Medicine and Image Processing (DMIP '24). Association for Computing Machinery, New York, NY, USA, 101–106. https://doi.org/10.1145/3705927.3705945. DOI: https://doi.org/10.1145/3705927.3705945
18 Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025). Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. European journal of medical research, 30(1), 848. https://doi.org/10.1186/s40001-025-03196-w DOI: https://doi.org/10.1186/s40001-025-03196-w
19 Tian, Y., & Wang, B. (2023). Unraveling the pathogenesis of non‐alcoholic fatty liver diseases through genome‐wide association studies. Journal of gastroenterology and hepatology, 38(11), 1877-1885. DOI: https://doi.org/10.1111/jgh.16330
20 Kitzmiller, J. P., Mikulik, E. B., Dauki, A. M., Murkherjee, C., & Luzum, J. A. (2016). Pharmacogenomics of statins: understanding susceptibility to adverse effects. Pharmacogenomics and personalized medicine, 97-106. DOI: https://doi.org/10.2147/PGPM.S86013
21 Lu, X., Zhang, M., Li, G., Zhang, S., Zhang, J., Fu, X., & Sun, F. (2023). Applications and research advances in the delivery of CRISPR/Cas9 systems for the treatment of inherited diseases. International journal of molecular sciences, 24(17), 13202. DOI: https://doi.org/10.3390/ijms241713202
22 Tao, H., Song, Z. Y., Ding, X. S., Yang, J. J., Shi, K. H., & Li, J. (2018). Epigenetic signatures in cardiac fibrosis, special emphasis on DNA methylation and histone modification. Heart Failure Reviews, 23(5), 789-799. DOI: https://doi.org/10.1007/s10741-018-9694-z
23 Petta, S., Armandi, A., & Bugianesi, E. (2025). Impact of PNPLA3 I148M on clinical outcomes in patients with MASLD. Liver International, 45(3), e16133. DOI: https://doi.org/10.1111/liv.16133
24 Bourganou, M. V., Chondrogianni, M. E., Kyrou, I., Flessa, C. M., Chatzigeorgiou, A., Oikonomou, E., ... & Kassi, E. (2025). Unraveling metabolic dysfunction-associated steatotic liver disease through the use of omics technologies. International Journal of Molecular Sciences, 26(4), 1589. DOI: https://doi.org/10.3390/ijms26041589
25 Demirtas, C. O., & Yilmaz, Y. (2024). Decoding 17-beta-hydroxysteroid dehydrogenase 13: a multifaceted perspective on its role in hepatic steatosis and associated disorders. Journal of Clinical and Translational Hepatology, 12(10), 857. DOI: https://doi.org/10.14218/JCTH.2024.00257
26 Samarasinghe, S. M., Hewage, A. S., Siriwardana, R. C., Tennekoon, K. H., Niriella, M. A., & De Silva, S. (2023). Genetic and metabolic aspects of non-alcoholic fatty liver disease (NAFLD) pathogenicity. Egyptian Journal of Medical Human Genetics, 24(1), 53. DOI: https://doi.org/10.1186/s43042-023-00433-x
27 Xu, K., Chen, P., Su, Y., Chen, Y., Song, X., Yu, B., & Wang, H. (2025). Significant Association Between Glucokinase Regulatory Protein Variants and Genetic and Metabolic Diseases. Current Issues in Molecular Biology, 47(10), 850. DOI: https://doi.org/10.3390/cimb47100850
28 Sebastiani, G., & Walker, A. P. (2007). HFE gene in primary and secondary hepatic iron overload. World journal of gastroenterology: WJG, 13(35), 4673. DOI: https://doi.org/10.3748/wjg.v13.i35.4673
29 Borel, F., Tang, Q., Gernoux, G., & Greer, C. (2017). eScholarship@ UMassChan. Molecular Therapy, 25(11). DOI: https://doi.org/10.1016/j.ymthe.2017.09.020
30 Hashemizadeh, Z., Malek-Hosseini, S. A., & Badiee, P. (2018). Prevalence of CYP2C19 Genetic Polymorphism among Normal People and Patients with Hepatic Diseases. International journal of organ transplantation medicine, 9(1), 27–33.
31 Lee, Y. S., & Chun, P. (2018). Effect of SLCO1B1 T521C on statin-induced myotoxicity: a systematic review and meta-analysis. Korean Journal of Clinical Pharmacy, 28(4), 320-330. DOI: https://doi.org/10.24304/kjcp.2018.28.4.320
32 Meza-Junco, J., Chu, Q. S. C., Christensen, O., Rajagopalan, P., Das, S., Stefanyschyn, R., & Sawyer, M. B. (2009). UGT1A1 polymorphism and hyperbilirubinemia in a patient who received sorafenib. Cancer chemotherapy and pharmacology, 65(1), 1-4. DOI: https://doi.org/10.1007/s00280-009-1096-4
33 Basytė-Bacevičė, V. (2021). The Role of TM6SF2, MBOAT7, Serpina1 and HSD17B13 Genetic Variants in Liver Fibrosis and Cirrhosis (Doctoral dissertation, Lithuanian University of Health Sciences (Lithuania)).
34 Marin, J. J., Serrano, M. A., Monte, M. J., Sanchez-Martin, A., Temprano, A. G., Briz, O., & Romero, M. R. (2020). Role of genetic variations in the hepatic handling of drugs. International Journal of Molecular Sciences, 21(8), 2884. DOI: https://doi.org/10.3390/ijms21082884
36 Sabha, S. U. (2025). Artificial Intelligence and Omics in Health and Diseases. In Artificial Intelligence in Human Health and Diseases (pp. 75-92). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-96-8176-1_5
37 Dhawan, V., Marwah, M., & Nagarsenker, M. (2018). Role of nanomedicine in hepatic targeting-deciphering strategies with molecular insights and preclinical prospects. Drug Delivery Letters, 8(2), 85-105. DOI: https://doi.org/10.2174/2210303108666171204160219
38 Vijayan, V., Unagolla, J. M., Panchal, D., John, J. E., Menon, S. S., & Menon, J. U. (2025). Biomimetic nanoparticles for targeted therapy of liver disease. RSC pharmaceutics, 2(4), 667-682. DOI: https://doi.org/10.1039/D5PM00044K
39 Mostafaei, F., Mahdinloo, S., Valizadeh, H., Hemmati, S., Abdi, M., Sarfraz, M., ... & Zakeri-Milani, P. (2023). An update review of smart nanotherapeutics and liver cancer: opportunities and challenges. Nanomedicine, 18(25), 1855-1873. DOI: https://doi.org/10.2217/nnm-2023-0196
40 Silva, B., & Bragança, J. (2025). Induced pluripotent stem cell-derived mesenchymal stem cells for modeling and treating metabolic associated fatty liver disease and metabolic associated steatohepatitis: Challenges and opportunities. World Journal of Stem Cells, 17(2), 99331. DOI: https://doi.org/10.4252/wjsc.v17.i2.99331
41 Cross-Najafi, A. A., Farag, K., Chen, A. M., Smith, L. J., Zhang, W., Li, P., & Ekser, B. (2024). The long road to develop custom-built livers: current status of 3D liver bioprinting. Transplantation, 108(2), 357-368. DOI: https://doi.org/10.1097/TP.0000000000004668
42 Guo, M., Zheng, B., Zeng, X., Wang, X., & Tzeng, C. M. (2025). Overview of cellular therapeutics clinical trials: advances, challenges, and future directions. International Journal of Molecular Sciences, 26(12), 5770. DOI: https://doi.org/10.3390/ijms26125770
43 Puengel, T., & Tacke, F. (2024). Pharmacotherapeutic options for metabolic dysfunction-associated steatotic liver disease: where are we today?. Expert Opinion on Pharmacotherapy, 25(9), 1249-1263. DOI: https://doi.org/10.1080/14656566.2024.2374463
44 Zafer, M., Tavaglione, F., Romero‐Gómez, M., & Loomba, R. (2025). GLP‐1 Receptor Agonists and Glucagon/GIP/GLP‐1 Receptor Dual or Triple Agonists—Mechanism of Action and Emerging Therapeutic Landscape in MASLD. Alimentary pharmacology & therapeutics, 61(12), 1872-1888. DOI: https://doi.org/10.1111/apt.70196
45 Tacke, F., & Weiskirchen, R. (2021). Non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH)-related liver fibrosis: mechanisms, treatment and prevention. Annals of Translational Medicine, 9(8), 729. DOI: https://doi.org/10.21037/atm-20-4354
46 Perkins, K. J., & Davies, K. E. (2012). Recent advances in Duchenne muscular dystrophy. Degenerative Neurological and Neuromuscular Disease, 141-164. DOI: https://doi.org/10.2147/DNND.S26637
47 Kokkorakis, M., Boutari, C., Hill, M. A., Kotsis, V., Loomba, R., Sanyal, A. J., & Mantzoros, C. S. (2024). Resmetirom, the first approved drug for the management of metabolic dysfunction-associated steatohepatitis: trials, opportunities, and challenges. Metabolism-Clinical and Experimental, 154. DOI: https://doi.org/10.1016/j.metabol.2024.155835
48 Schneider L. Wegovy Now Approved for Treatment of Severe Liver Disease. JAMA. 2025;334(13):1135. doi:10.1001/jama.2025.13358 DOI: https://doi.org/10.1001/jama.2025.13358
49 Abdeljawad, M. M., Hasan, M. T., Fareed, A., Abouelmagd, K., Saed, S. A. A., Ali, S., ... & Taha, A. M. (2025). Efficacy and Safety of Fibroblast Growth Factor 21 (FGF21) Analogs in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and Metabolic Dysfunction-Associated Steatohepatitis (MASH): A Systematic Review and Network Meta-Analysis. The Journal of Pharmacology and Experimental Therapeutics, 103786. DOI: https://doi.org/10.1016/j.jpet.2025.103786
50 Tiwari, A., Sharma, A., Kumar, H., Gupta, V., Deshpande, V., Mupparaju, J. S., ... & Jain, V. (2025). Resmetirom for MASH: A Comprehensive Review of a Novel Therapeutic Frontier. Biomedicines, 13(9), 2079. DOI: https://doi.org/10.3390/biomedicines13092079
51 Lin, R. T., Sun, Q. M., Xin, X., Ng, C. H., Valenti, L., Hu, Y. Y., ... & Feng, Q. (2024). Comparative efficacy of THR-β agonists, FGF-21 analogues, GLP-1R agonists, GLP-1-based polyagonists, and Pan-PPAR agonists for MASLD: A systematic review and network meta-analysis. Metabolism, 161, 156043. DOI: https://doi.org/10.1016/j.metabol.2024.156043
52 Zeng, X., Huang, D., Zhu, Z., Cai, Q., Yang, Y., Lu, H., & Chen, J. (2025). Mechanism-guided drug development and treatment for liver fibrosis: a clinical perspective. Frontiers in Pharmacology, 16, 1574385. DOI: https://doi.org/10.3389/fphar.2025.1574385
53 Johnson, P., Zhou, Q., Dao, D. Y., & Lo, Y. D. (2022). Circulating biomarkers in the diagnosis and management of hepatocellular carcinoma. Nature reviews Gastroenterology & hepatology, 19(10), 670-681. DOI: https://doi.org/10.1038/s41575-022-00620-y
54 Chang, M. L., & Yang, S. S. (2019). Metabolic signature of hepatic fibrosis: from individual pathways to systems biology. Cells, 8(11), 1423. DOI: https://doi.org/10.3390/cells8111423
55 Rama, K., Jahagirdar, V., Idalsoaga, F., Blaney, H., Rhoads, S. F., Díaz, L. A., ... & Arab, J. P. (2025). Novel Biomarkers for Alcohol-Associated Liver Disease and Their Implications Across Clinical Settings. Clin. Mol. Hepatol. DOI: https://doi.org/10.3350/cmh.2025.0921
56 Righetti, R., Cinque, F., Patel, K., & Sebastiani, G. (2025). The role of noninvasive biomarkers for monitoring cell injury in advanced liver fibrosis. Expert Review of Gastroenterology & Hepatology, 19(1), 65-80. DOI: https://doi.org/10.1080/17474124.2025.2450717
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