REFERENCES
1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63.
2. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2021;7:6.
3. Singal AG, Kanwal F, Llovet JM. Global trends in hepatocellular carcinoma epidemiology: implications for screening, prevention and therapy. Nat Rev Clin Oncol 2023;20:864-84.
4. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent 2024;4:47-53.
5. Zhan Z, Chen B, Huang R, et al. Long-term trends and future projections of liver cancer burden in China from 1990 to 2030. Sci Rep 2025;15:13120.
6. Saborido B, Darnell A, Forner A, et al. Diagnostic performance of contrast-enhanced US in small liver nodules not conclusively characterized after MRI in cirrhotic patients. Eur Radiol 2025;35:5771-80.
7. Huang L, Sun H, Sun L, et al. Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning. Nat Commun 2023;14:48.
8. Sangro B, Sarobe P, Hervás-Stubbs S, Melero I. Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2021;18:525-43.
9. Yang X, Yang C, Zhang S, et al. Precision treatment in advanced hepatocellular carcinoma. Cancer Cell 2024;42:180-97.
10. Shi X, Wang X, Yao W, et al. Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives. Signal Transduct Target Ther 2024;9:192.
11. Abdelhamed W, El-Kassas M. Hepatocellular carcinoma recurrence: predictors and management. Liver Res 2023;7:321-32.
12. Hu R, Tran B, Li S, et al. Noninvasive prognostication of hepatocellular carcinoma based on cell-free DNA methylation. PLoS ONE 2025;20:e0321736.
13. She S, Xiang Y, Yang M, et al. C-reactive protein is a biomarker of AFP-negative HBV-related hepatocellular carcinoma. Int J Oncol 2015;47:543-54.
14. Steyaert S, Pizurica M, Nagaraj D, et al. Multimodal data fusion for cancer biomarker discovery with deep learning. Nat Mach Intell 2023;5:351-62.
15. Saltz J, Almeida J, Gao Y, et al. Towards generation, management, and exploration of combined radiomics and pathomics datasets for cancer research. AMIA Jt Summits Transl Sci Proc 2017;2017:85-94.
16. Chen RJ, Lu MY, Williamson DFK, et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 2022;40:865-78.e6.
17. Khan RA, Fu M, Burbridge B, Luo Y, Wu FX. A multi-modal deep neural network for multi-class liver cancer diagnosis. Neural Netw 2023;165:553-61.
18. Shi Y, Wang M, Liu H, Zhao F, Li A, Chen X. MIF: multi-shot interactive fusion model for cancer survival prediction using pathological image and genomic data. IEEE J Biomed Health Inform 2025;29:3247-58.
19. Mohsen F, Ali H, El Hajj N, Shah Z. Artificial intelligence-based methods for fusion of electronic health records and imaging data. Sci Rep 2022;12:17981.
20. Ying H, Liu X, Zhang M, et al. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun 2024;15:1131.
21. Shan R, Pei C, Fan Q, et al. Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study. BMC Cancer 2025;25:154.
22. Yin C, Zhang H, Du J, Zhu Y, Zhu H, Yue H. Artificial intelligence in imaging for liver disease diagnosis. Front Med 2025;12:1591523.
23. Kiani A, Uyumazturk B, Rajpurkar P, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit Med 2020;3:23.
24. Gao W, Wang W, Song D, et al. A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma. Radiol Med 2022;127:259-71.
25. Soenksen LR, Ma Y, Zeng C, et al. Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit Med 2022;5:149.
26. Erion G, Janizek JD, Hudelson C, et al. A cost-aware framework for the development of AI models for healthcare applications. Nat Biomed Eng 2022;6:1384-98.
27. Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019;25:672-82.
28. García-Figueiras R, Baleato-González S, Padhani AR, et al. How clinical imaging can assess cancer biology. Insights Imaging 2019;10:28.
29. Wang Y, Ju X, Hua R, et al. Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer. Br J Cancer 2024;131:1833-45.
30. Woo EG, Burkhart MC, Alsentzer E, Beaulieu-Jones BK. Synthetic data distillation enables the extraction of clinical information at scale. NPJ Digit Med 2025;8:267.
31. Vithayathil M, Koku D, Campani C, et al. Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma. J Hepatol 2025;83:959-70.
32. Schön F, Kieslich A, Nebelung H, et al. Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma. Sci Rep 2024;14:590.
33. Wang Q, Li X, Qian B, Hu K, Liu B. Fluorescence imaging in the surgical management of liver cancers: current status and future perspectives. Asian J Surg 2022;45:1375-82.
34. Hennedige T, Venkatesh SK. Imaging of hepatocellular carcinoma: diagnosis, staging and treatment monitoring. Cancer Imaging 2013;12:530-47.
35. Tanaka H. Current role of ultrasound in the diagnosis of hepatocellular carcinoma. J Med Ultrason 2020;47:239-55.
36. Li L, Dioguardi Burgio M, Fetzer DT, et al. Contrast-enhanced ultrasound for hepatocellular carcinoma diagnosis-AJR expert panel narrative review. AJR Am J Roentgenol 2026;226:e2532813.
37. Lee PYC, Mohamed Afif A, Anthony A, Goodyear M, Lombardo P. Ambient light intensity affecting ultrasound operator detection of liver lesions in cine-clips. Radiography 2024;30:1151-7.
38. Davis DP, Campbell CJ, Poste JC, Ma G. The association between operator confidence and accuracy of ultrasonography performed by novice emergency physicians. J Emerg Med 2005;29:259-64.
39. Chaiteerakij R, Ariyaskul D, Kulkraisri K, et al. Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma. Sci Rep 2024;14:20617.
40. Wang L, Fatemi M, Alizad A. Artificial intelligence techniques in liver cancer. Front Oncol 2024;14:1415859.
41. Lu RF, She CY, He DN, et al. AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening. NPJ Digit Med 2025;8:500.
42. Hu HT, Wang W, Chen LD, et al. Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound. J Gastroenterol Hepatol 2021;36:2875-83.
43. Ding W, Meng Y, Ma J, et al. Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions. J Hepatol 2025;83:426-39.
44. Chartampilas E, Rafailidis V, Georgopoulou V, Kalarakis G, Hatzidakis A, Prassopoulos P. Current imaging diagnosis of hepatocellular carcinoma. Cancers 2022;14:3997.
45. Balaguer-Montero M, Marcos Morales A, Ligero M, et al. A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer. Cell Rep Med 2025;6:102032.
46. Shin H, Hur MH, Song BG, et al. AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B. J Hepatol 2025;82:1080-8.
47. Özcan F, Uçan ON, Karaçam S, Tunçman D. Fully automatic liver and tumor segmentation from CT image using an AIM-Unet. Bioengineering 2023;10:215.
48. Krishnan MS, Rajan Kd A, Park J, et al. Genomic analysis of vascular invasion in HCC reveals molecular drivers and predictive biomarkers. Hepatology 2021;73:2342-60.
49. Zhou Y, Sun SW, Liu QP, Xu X, Zhang Y, Zhang YD. TED: two-stage expert-guided interpretable diagnosis framework for microvascular invasion in hepatocellular carcinoma. Med Image Anal 2022;82:102575.
50. Xiao H, Guo Y, Zhou Q, et al. Prediction of microvascular invasion in hepatocellular carcinoma with expert-inspiration and skeleton sharing deep learning. Liver Int 2022;42:1423-31.
51. Hoffmann E, Masthoff M, Kunz WG, et al. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol 2024;21:428-48.
52. Melendez-Torres J, Singal AG. Early detection of hepatocellular carcinoma: roadmap for improvement. Expert Rev Anticancer Ther 2022;22:621-32.
53. Zhong X, Guan T, Tang D, et al. Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm. BMC Gastroenterol 2021;21:155.
54. Liu Z, Yao B, Wen J, et al. Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions. Eur Radiol 2024;34:182-92.
55. Paudyal R, Shah AD, Akin O, et al. Artificial Intelligence in CT and MR imaging for oncological applications. Cancers 2023;15:2573.
56. Zhou J, Sun H, Wang Z, et al. Guidelines for the diagnosis and treatment of primary liver cancer (2022 Edition). Liver Cancer 2023;12:405-44.
57. Kiran N, Sapna F, Kiran F, et al. Digital pathology: transforming diagnosis in the digital age. Cureus 2023;15:e44620.
58. Kumar N, Gupta R, Gupta S. Whole slide imaging (WSI) in pathology: current perspectives and future directions. J Digit Imaging 2020;33:1034-40.
59. Yu J, Chen H, Hu L, et al. Exploring multi-instance learning in whole slide imaging: Current and future perspectives. Pathol Res Pract 2025;271:156006.
60. Ding GY, Shi JY, Wang XD, Yan B, Liu XY, Gao Q. Artificial intelligence-based pathological analysis of liver cancer: current advancements and interpretative strategies. ILIVER 2024;3:100082.
61. Khened M, Kori A, Rajkumar H, Krishnamurthi G, Srinivasan B. A generalized deep learning framework for whole-slide image segmentation and analysis. Sci Rep 2021;11:11579.
62. Yates J, Van Allen EM. New horizons at the interface of artificial intelligence and translational cancer research. Cancer Cell 2025;43:708-27.
64. Aatresh AA, Alabhya K, Lal S, Kini J, Saxena PUP. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. Int J Comput Assist Radiol Surg 2021;16:1549-63.
65. Zhang ZH, Jiang C, Qiang ZY, et al. Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: a literature review. Asian J Surg 2024;47:2138-43.
66. Liang L, Xu ZD, Lu WF, et al. Survival benefit from adjuvant TACE combined with lenvatinib for patients with hepatocellular carcinoma and microvascular invasion after curative hepatectomy. Asian J Surg 2024;47:5106-12.
67. Du JS, Hsu SH, Wang SN. The current and prospective adjuvant therapies for hepatocellular carcinoma. Cancers 2024;16:1422.
68. Zhang X, Yu X, Liang W, et al. Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images. Cancer Med 2024;13:e7104.
69. Safri F, Nguyen R, Zerehpooshnesfchi S, George J, Qiao L. Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications. Cancer Gene Ther 2024;31:1105-12.
70. Guo DZ, Zhang X, Zhang SQ, et al. Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts. Mol Cancer 2024;23:157.
71. Lim J, Park C, Kim M, Kim H, Kim J, Lee DS. Advances in single-cell omics and multiomics for high-resolution molecular profiling. Exp Mol Med 2024;56:515-26.
72. Moris D, Martinino A, Schiltz S, et al. Advances in the treatment of hepatocellular carcinoma: an overview of the current and evolving therapeutic landscape for clinicians. CA Cancer J Clin 2025;75:498-527.
73. Yang C, Zhang H, Zhang L, et al. Evolving therapeutic landscape of advanced hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2023;20:203-22.
74. Ramesh D, Manickavel P, Ghosh S, Bhat M. The integration of multi-omics with artificial intelligence in hepatology: a comprehensive review of personalized medicine, biomarker identification, and drug discovery. J Clin Exp Hepatol 2025;15:102611.
75. Nam Y, Kim J, Jung SH, et al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci 2024;7:225-50.
76. Bartolomucci A, Nobrega M, Ferrier T, et al. Circulating tumor DNA to monitor treatment response in solid tumors and advance precision oncology. NPJ Precis Oncol 2025;9:84.
77. Perera GS, Huang X, Bagherjeri FA, et al. Rapid and selective detection of TP53 mutations in cancer using a novel conductometric biosensor. Biosens Bioelectron 2025;276:117252.
78. Zhang S, Xiao X, Yi Y, et al. Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets. Signal Transduct Target Ther 2024;9:149.
79. Abdelwahab O, Torkamaneh D. Artificial intelligence in variant calling: a review. Front Bioinform 2025;5:1574359.
80. Tohme S, Yazdani HO, Rahman A, et al. The use of machine learning to create a risk score to predict survival in patients with hepatocellular carcinoma: a TCGA cohort analysis. Can J Gastroenterol Hepatol 2021;2021:5212953.
81. Shen J, Qi L, Zou Z, et al. Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases. Sci Rep 2020;10:4435.
82. Pantel K, Alix-Panabières C. Minimal residual disease as a target for liquid biopsy in patients with solid tumours. Nat Rev Clin Oncol 2025;22:65-77.
83. Huang A, Guo DZ, Zhang X, et al. Serial circulating tumor DNA profiling predicts tumor recurrence after liver transplantation for liver cancer. Hepatol Int 2024;18:254-64.
84. Li C, Hu J, Li M, Mao Y, Mao Y. Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma. Hereditas 2025;162:61.
85. Chen S, Li Y, Hu J, et al. Integrating bulk RNA-seq, scRNA-seq, and spatial transcriptomics data to identify novel post-translational modification-related molecular subtypes and therapeutic responses in hepatocellular carcinoma. Cancer Cell Int 2025;25:330.
86. Santorelli L, Caterino M, Costanzo M. Proteomics and metabolomics in biomedicine. Int J Mol Sci 2023;24:16913.
87. Garg M, Karpinski M, Matelska D, et al. Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank. Nat Genet 2024;56:1821-31.
88. Xing X, Cai L, Ouyang J, et al. Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma. Nat Commun 2023;14:8392.
89. Bussi Y, Keren L. Multiplexed image analysis: what have we achieved and where are we headed? Nat Methods 2024;21:2212-5.
90. Heindl A, Nawaz S, Yuan Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab Invest 2015;95:377-84.
91. Zhu Q, Zhao X, Zhang Y, et al. Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines. Nat Commun 2023;14:8170.
92. Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2022;19:132-46.
93. Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023;24:494-515.
94. Zhang S, Zhang J, Tian B, Lukasiewicz T, Xu Z. Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation. Med Image Anal 2023;83:102656.
95. De Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell 2023;41:374-403.
96. Du T, Li W, Wang Z, et al. Overcoming the challenges of multi-modal medical image sharing: a novel data distillation strategy via contrastive learning. Neurocomputing 2025;617:129043.
97. Azam MA, Khan KB, Salahuddin S, et al. A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Comput Biol Med 2022;144:105253.
98. Lewis SM, Asselin-labat M, Nguyen Q, et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat Methods 2021;18:997-1012.
99. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 2019;25:1054-6.
100. Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007;25:675-80.
101. Harding‐theobald E, Louissaint J, Maraj B, et al. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901.
102. Ji G, Zhu F, Xu Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: a multi-institutional study. EBioMedicine 2019;50:156-65.
103. Mokrane F, Lu L, Vavasseur A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol 2019;30:558-70.
104. Peng J, Kang S, Ning Z, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol 2019;30:413-24.
105. Taouli B, Hoshida Y, Kakite S, et al. Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results. Eur Radiol 2017;27:4472-81.
106. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett 2020;471:61-71.
107. Hectors SJ, Lewis S, Besa C, et al. MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma. Eur Radiol 2020;30:3759-69.
108. Lv X, Zhang P, Zhang E, Yang S. Predictive factors and prognostic models for Hepatic arterial infusion chemotherapy in Hepatocellular carcinoma: a comprehensive review. World J Surg Oncol 2025;23:166.
109. Yu T, Zhan Z, Lin Q. Computed tomography radiomics prediction of survival in hepatocellular carcinoma and is associated with ADH1A expression of the retinol metabolism pathway. Medicine 2025;104:e42792.
110. Tschandl P, Rinner C, Apalla Z, et al. Human-computer collaboration for skin cancer recognition. Nat Med 2020;26:1229-34.
111. Mckinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature 2020;577:89-94.
112. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17:195.
113. Tang Z, Tang Z, Wu J. Anomaly detection in medical via multimodal foundation models. Front Bioeng Biotechnol 2025;13:1644697.
114. Liang W, Tadesse GA, Ho D, et al. Author correction: advances, challenges and opportunities in creating data for trustworthy AI. Nat Mach Intell 2022;4:904.
115. Rehm HL, Page AJ, Smith L, et al. GA4GH: international policies and standards for data sharing across genomic research and healthcare. Cell Genomics 2021;1:100029.
116. Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M. Federated learning: overview, strategies, applications, tools and future directions. Heliyon 2024;10:e38137.
117. Karako K, Tang W. Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI. BST 2024;18:497-504.
118. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 2019;1:206-15.
119. Liu G, Zhang J, Chan AB, Hsiao JH. Human attention guided explainable artificial intelligence for computer vision models. Neural Netw 2024;177:106392.
120. Satapathy SM, Paul MG, Garg A, Bhatnagar S. GAN-enhanced hybrid deep learning with explainable AI for automated cataract diagnosis. J Med Syst 2025;49:123.
121. Oncu E, Ciftci F. Multimodal AI framework for lung cancer diagnosis: integrating CNN and ANN models for imaging and clinical data analysis. Comput Biol Med 2025;193:110488.
122. Lee S, Kim DW, Oh N, et al. External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study. Sci Rep 2025;15:1100.
123. Cheung CY, Ran AR, Wang S, et al. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. The Lancet Digital Health 2022;4:e806-15.
124. Colak C, Yagin FH, Algarni A, Algarni A, Al-hashem F, Ardigò LP. Proposed comprehensive methodology integrated with explainable artificial intelligence for prediction of possible biomarkers in metabolomics panel of plasma samples for breast cancer detection. Medicina 2025;61:581.
125. Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023;189:106706.
126. Lee C, Lien JJ, Chain K, Huang L, Hsu Z. Federated learning-based CT liver tumor detection using a teacher-student SANet with semisupervised learning. BMC Med Imaging 2025;25:250.
127. Sadée C, Testa S, Barba T, et al. Medical digital twins: enabling precision medicine and medical artificial intelligence. The Lancet Digital Health 2025;7:100864.
128. Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. Nat Comput Sci 2024;4:184-91.
129. Feldman K, Johnson RA, Chawla NV. The state of data in healthcare: path towards standardization. J Healthc Inform Res 2018;2:248-71.
130. Brancato V, Esposito G, Coppola L, et al. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024;22:136.
131. Al-antari MA. Artificial intelligence for medical diagnostics - existing and future AI technology! Diagnostics 2023;13:688.
132. Parvin N, Joo SW, Jung JH, Mandal TK. Multimodal AI in biomedicine: pioneering the future of biomaterials, diagnostics, and personalized healthcare. Nanomaterials 2025;15:895.
133. Zubair M, Hussain M, Albashrawi MA, Bendechache M, Owais M. A comprehensive review of techniques, algorithms, advancements, challenges, and clinical applications of multi-modal medical image fusion for improved diagnosis. Comput Methods Programs Biomed 2025;272:109014.
134. Chatzipanagiotou OP, Loukas C, Vailas M, et al. Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature. J Gastroenterol Hepatol 2024;39:1994-2005.
135. Chen X, Wang X, Zhang K, et al. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal 2022;79:102444.
136. Van De Sande D, Chung EFF, Oosterhoff J, Van Bommel J, Gommers D, Van Genderen ME. To warrant clinical adoption AI models require a multi-faceted implementation evaluation. npj Digit Med 2024;7:58.





