Reconstructing strategies for precision diagnosis and treatment of liver cancer based on multi-modal data

Abstract

Liver cancer, particularly hepatocellular carcinoma (HCC), poses a severe global public health threat owing to its high incidence, frequent late-stage diagnosis, and poor 5-year survival rate. Conventional approaches to liver cancer diagnosis and treatment are limited by their reliance on subjective physician experience, uniform and undifferentiated treatment strategies, and imprecise prognostic assessment. This review synthesizes studies published between 2019 and 2025 on the application of multi-modal data in liver cancer care, including computed tomography (CT), magnetic resonance imaging (MRI), pathology, and multi-omics data. We explore the utility of single-modal data analysis including the role of CT or MRI in enhancing diagnostic accuracy and the application of pathological data. Subsequently, the review focuses on multi-modal data fusion strategies, including feature-level, decision-level, and modal-level fusion, which collectively support precision diagnosis, personalized treatment recommendation, and accurate prognosis prediction in clinical practice. Additionally, it addresses critical challenges such as data heterogeneity and low physician acceptance of integrated data-driven tools, while outlining future directions including the development of standardized multi-modal data ecosystems. This review highlights multi-modal data as a core driver of precision liver cancer care, with the objective of accelerating its translation into routine clinical practice.

Keywords

Liver cancer / precision diagnosis / multi-modal data / treatment / challenge

References

[1] Bray F,Sung H.Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin2024;74:229-63 PMCID:13048501

[2] Llovet JM,Villanueva A.Hepatocellular carcinoma.Nat Rev Dis Primers2021;7:6

[3] Singal AG,Llovet JM.Global trends in hepatocellular carcinoma epidemiology: implications for screening, prevention and therapy.Nat Rev Clin Oncol2023;20:864-84 PMCID:12996431

[4] Han B,Zeng H.Cancer incidence and mortality in China, 2022.J Natl Cancer Cent2024;4:47-53 PMCID:PMC11256708

[5] Zhan Z,Huang R.Long-term trends and future projections of liver cancer burden in China from 1990 to 2030.Sci Rep2025;15:13120 PMCID:PMC12003824

[6] Saborido B,Forner A.Diagnostic performance of contrast-enhanced US in small liver nodules not conclusively characterized after MRI in cirrhotic patients.Eur Radiol2025;35:5771-80

[7] Huang L,Sun L.Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.Nat Commun2023;14:48 PMCID:PMC9813224

[8] Sangro B,Hervás-Stubbs S.Advances in immunotherapy for hepatocellular carcinoma.Nat Rev Gastroenterol Hepatol2021;18:525-43 PMCID:PMC8042636

[9] Yang X,Zhang S.Precision treatment in advanced hepatocellular carcinoma.Cancer Cell2024;42:180-97

[10] Shi X,Yao W.Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives.Signal Transduct Target Ther2024;9:192 PMCID:PMC11294630

[11] Abdelhamed W.Hepatocellular carcinoma recurrence: predictors and management.Liver Res2023;7:321-32 PMCID:PMC11791921

[12] Hu R,Li S.Noninvasive prognostication of hepatocellular carcinoma based on cell-free DNA methylation.PLoS ONE2025;20:e0321736 PMCID:PMC12026916

[13] She S,Yang M.C-reactive protein is a biomarker of AFP-negative HBV-related hepatocellular carcinoma.Int J Oncol2015;47:543-54

[14] Steyaert S,Nagaraj D.Multimodal data fusion for cancer biomarker discovery with deep learning.Nat Mach Intell2023;5:351-62 PMCID:PMC10484010

[15] Saltz J,Gao Y.Towards generation, management, and exploration of combined radiomics and pathomics datasets for cancer research.AMIA Jt Summits Transl Sci Proc2017;2017:85-94 PMCID:PMC5543366

[16] Chen RJ,Williamson DFK.Pan-cancer integrative histology-genomic analysis via multimodal deep learning.Cancer Cell2022;40:865-78.e6 PMCID:PMC10397370

[17] Khan RA,Burbridge B,Wu FX.A multi-modal deep neural network for multi-class liver cancer diagnosis.Neural Netw2023;165:553-61

[18] Shi Y,Liu H,Li A.MIF: multi-shot interactive fusion model for cancer survival prediction using pathological image and genomic data.IEEE J Biomed Health Inform2025;29:3247-58

[19] Mohsen F,El Hajj N.Artificial intelligence-based methods for fusion of electronic health records and imaging data.Sci Rep2022;12:17981 PMCID:PMC9605975

[20] Ying H,Zhang M.A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.Nat Commun2024;15:1131 PMCID:PMC10850133

[21] Shan R,Fan Q.Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study.BMC Cancer2025;25:154 PMCID:PMC11773783

[22] Yin C,Du J,Zhu H.Artificial intelligence in imaging for liver disease diagnosis.Front Med2025;12:1591523 PMCID:PMC12062035

[23] Kiani A,Rajpurkar P.Impact of a deep learning assistant on the histopathologic classification of liver cancer.NPJ Digit Med2020;3:23 PMCID:PMC7044422

[24] Gao W,Song D.A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma.Radiol Med2022;127:259-71

[25] Soenksen LR,Zeng C.Integrated multimodal artificial intelligence framework for healthcare applications.NPJ Digit Med2022;5:149 PMCID:PMC9489871

[26] Erion G,Hudelson C.A cost-aware framework for the development of AI models for healthcare applications.Nat Biomed Eng2022;6:1384-98 PMCID:PMC9537352

[27] Zhou LQ,Yu SY.Artificial intelligence in medical imaging of the liver.World J Gastroenterol2019;25:672-82 PMCID:PMC6378542

[28] García-Figueiras R,Padhani AR.How clinical imaging can assess cancer biology.Insights Imaging2019;10:28 PMCID:PMC6399375

[29] Wang Y,Hua R.Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.Br J Cancer2024;131:1833-45 PMCID:PMC11589918

[30] Woo EG,Alsentzer E.Synthetic data distillation enables the extraction of clinical information at scale.NPJ Digit Med2025;8:267 PMCID:PMC12065832

[31] Vithayathil M,Campani C.Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma.J Hepatol2025;83:959-70

[32] Schön F,Nebelung H.Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma.Sci Rep2024;14:590 PMCID:PMC10770355

[33] Wang Q,Qian B,Liu B.Fluorescence imaging in the surgical management of liver cancers: current status and future perspectives.Asian J Surg2022;45:1375-82

[34] Hennedige T.Imaging of hepatocellular carcinoma: diagnosis, staging and treatment monitoring.Cancer Imaging2013;12:530-47 PMCID:PMC3666429

[35] Tanaka H.Current role of ultrasound in the diagnosis of hepatocellular carcinoma.J Med Ultrason2020;47:239-55 PMCID:PMC7181430

[36] Li L,Fetzer DT.Contrast-enhanced ultrasound for hepatocellular carcinoma diagnosis-AJR expert panel narrative review.AJR Am J Roentgenol2026;226:e2532813

[37] Lee PYC,Anthony A,Lombardo P.Ambient light intensity affecting ultrasound operator detection of liver lesions in cine-clips.Radiography2024;30:1151-7

[38] Davis DP,Poste JC.The association between operator confidence and accuracy of ultrasonography performed by novice emergency physicians.J Emerg Med2005;29:259-64

[39] Chaiteerakij R,Kulkraisri K.Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.Sci Rep2024;14:20617 PMCID:PMC11375009

[40] Wang L,Alizad A.Artificial intelligence techniques in liver cancer.Front Oncol2024;14:1415859 PMCID:PMC11405163

[41] Lu RF,He DN.AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening.NPJ Digit Med2025;8:500 PMCID:PMC12318110

[42] Hu HT,Chen LD.Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.J Gastroenterol Hepatol2021;36:2875-83 PMCID:8518504

[43] Ding W,Ma J.Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions.J Hepatol2025;83:426-39

[44] Chartampilas E,Georgopoulou V,Hatzidakis A.Current imaging diagnosis of hepatocellular carcinoma.Cancers2022;14:3997 PMCID:PMC9406360

[45] Balaguer-Montero M,Ligero M.A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.Cell Rep Med2025;6:102032 PMCID:PMC12047525

[46] Shin H,Song BG.AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.J Hepatol2025;82:1080-8

[47] Özcan F,Karaçam S.Fully automatic liver and tumor segmentation from CT image using an AIM-Unet.Bioengineering2023;10:215 PMCID:PMC9951904

[48] Krishnan MS,Park J.Genomic analysis of vascular invasion in HCC reveals molecular drivers and predictive biomarkers.Hepatology2021;73:2342-60 PMCID:PMC8115767

[49] Zhou Y,Liu QP,Zhang Y.TED: two-stage expert-guided interpretable diagnosis framework for microvascular invasion in hepatocellular carcinoma.Med Image Anal2022;82:102575

[50] Xiao H,Zhou Q.Prediction of microvascular invasion in hepatocellular carcinoma with expert-inspiration and skeleton sharing deep learning.Liver Int2022;42:1423-31

[51] Hoffmann E,Kunz WG.Multiparametric MRI for characterization of the tumour microenvironment.Nat Rev Clin Oncol2024;21:428-48

[52] Melendez-Torres J.Early detection of hepatocellular carcinoma: roadmap for improvement.Expert Rev Anticancer Ther2022;22:621-32 PMCID:PMC9845108

[53] Zhong X,Tang D.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 Gastroenterol2021;21:155 PMCID:PMC8028813

[54] Liu Z,Wen J.Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.Eur Radiol2024;34:182-92

[55] Paudyal R,Akin O.Artificial Intelligence in CT and MR imaging for oncological applications.Cancers2023;15:2573 PMCID:PMC10177423

[56] Zhou J,Wang Z.Guidelines for the diagnosis and treatment of primary liver cancer (2022 Edition).Liver Cancer2023;12:405-44 PMCID:PMC10601883

[57] Kiran N,Kiran F.Digital pathology: transforming diagnosis in the digital age.Cureus2023;15:e44620 PMCID:PMC10547926

[58] Kumar N,Gupta S.Whole slide imaging (WSI) in pathology: current perspectives and future directions.J Digit Imaging2020;33:1034-40 PMCID:PMC7522141

[59] Yu J,Hu L.Exploring multi-instance learning in whole slide imaging: Current and future perspectives.Pathol Res Pract2025;271:156006

[60] Ding GY,Wang XD,Liu XY.Artificial intelligence-based pathological analysis of liver cancer: current advancements and interpretative strategies.ILIVER2024;3:100082 PMCID:PMC12212694

[61] Khened M,Rajkumar H,Srinivasan B.A generalized deep learning framework for whole-slide image segmentation and analysis.Sci Rep2021;11:11579 PMCID:PMC8172839

[62] Yates J.New horizons at the interface of artificial intelligence and translational cancer research.Cancer Cell2025;43:708-27 PMCID:PMC12007700

[63] Kwon D.How artificial intelligence is transforming pathology.Nature2025;641:1342-4

[64] Aatresh AA,Lal S,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 Surg2021;16:1549-63

[65] Zhang ZH,Qiang ZY.Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: a literature review.Asian J Surg2024;47:2138-43

[66] Liang L,Lu WF.Survival benefit from adjuvant TACE combined with lenvatinib for patients with hepatocellular carcinoma and microvascular invasion after curative hepatectomy.Asian J Surg2024;47:5106-12

[67] Du JS,Wang SN.The current and prospective adjuvant therapies for hepatocellular carcinoma.Cancers2024;16:1422 PMCID:PMC11011082

[68] Zhang X,Liang W.Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.Cancer Med2024;13:e7104 PMCID:10941532

[69] Safri F,Zerehpooshnesfchi S,Qiao L.Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications.Cancer Gene Ther2024;31:1105-12 PMCID:PMC11327108

[70] Guo DZ,Zhang SQ.Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts.Mol Cancer2024;23:157 PMCID:PMC11295380

[71] Lim J,Kim M,Kim J.Advances in single-cell omics and multiomics for high-resolution molecular profiling.Exp Mol Med2024;56:515-26 PMCID:PMC10984936

[72] Moris D,Schiltz S.Advances in the treatment of hepatocellular carcinoma: an overview of the current and evolving therapeutic landscape for clinicians.CA Cancer J Clin2025;75:498-527 PMCID:PMC12593328

[73] Yang C,Zhang L.Evolving therapeutic landscape of advanced hepatocellular carcinoma.Nat Rev Gastroenterol Hepatol2023;20:203-22

[74] Ramesh D,Ghosh S.The integration of multi-omics with artificial intelligence in hepatology: a comprehensive review of personalized medicine, biomarker identification, and drug discovery.J Clin Exp Hepatol2025;15:102611 PMCID:PMC12272764

[75] Nam Y,Jung SH.Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine.Annu Rev Biomed Data Sci2024;7:225-50 PMCID:PMC11972123

[76] Bartolomucci A,Ferrier T.Circulating tumor DNA to monitor treatment response in solid tumors and advance precision oncology.NPJ Precis Oncol2025;9:84 PMCID:PMC11930993

[77] Perera GS,Bagherjeri FA.Rapid and selective detection of TP53 mutations in cancer using a novel conductometric biosensor.Biosens Bioelectron2025;276:117252

[78] Zhang S,Yi Y.Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets.Signal Transduct Target Ther2024;9:149 PMCID:PMC11189549

[79] Abdelwahab O.Artificial intelligence in variant calling: a review.Front Bioinform2025;5:1574359 PMCID:PMC12055765

[80] Tohme S,Rahman A.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 Hepatol2021;2021:5212953 PMCID:PMC8651371

[81] Shen J,Zou Z.Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases.Sci Rep2020;10:4435 PMCID:PMC7064516

[82] Pantel K.Minimal residual disease as a target for liquid biopsy in patients with solid tumours.Nat Rev Clin Oncol2025;22:65-77

[83] Huang A,Zhang X.Serial circulating tumor DNA profiling predicts tumor recurrence after liver transplantation for liver cancer.Hepatol Int2024;18:254-64

[84] Li C,Li M,Mao Y.Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma.Hereditas2025;162:61 PMCID:PMC11992824

[85] Chen S,Hu J.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 Int2025;25:330 PMCID:PMC12495838

[86] Santorelli L,Costanzo M.Proteomics and metabolomics in biomedicine.Int J Mol Sci2023;24:16913 PMCID:PMC10706996

[87] Garg M,Matelska D.Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.Nat Genet2024;56:1821-31 PMCID:11390475

[88] Xing X,Ouyang J.Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma.Nat Commun2023;14:8392 PMCID:PMC10728065

[89] Bussi Y.Multiplexed image analysis: what have we achieved and where are we headed?.Nat Methods2024;21:2212-5

[90] Heindl A,Yuan Y.Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology.Lab Invest2015;95:377-84

[91] Zhu Q,Zhang Y.Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines.Nat Commun2023;14:8170 PMCID:PMC10710513

[92] Bera K,Gupta A,Madabhushi A.Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nat Rev Clin Oncol2022;19:132-46 PMCID:PMC9034765

[93] Vandereyken K,Thienpont B.Methods and applications for single-cell and spatial multi-omics.Nat Rev Genet2023;24:494-515 PMCID:PMC9979144

[94] Zhang S,Tian B,Xu Z.Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation.Med Image Anal2023;83:102656

[95] De Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth.Cancer Cell2023;41:374-403

[96] Du T,Wang Z.Overcoming the challenges of multi-modal medical image sharing: a novel data distillation strategy via contrastive learning.Neurocomputing2025;617:129043

[97] Azam MA,Salahuddin S.A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics.Comput Biol Med2022;144:105253

[98] Lewis SM,Nguyen Q.Spatial omics and multiplexed imaging to explore cancer biology.Nat Methods2021;18:997-1012

[99] Kather JN,Halama N.Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.Nat Med2019;25:1054-6 PMCID:PMC7423299

[100] Segal E,Ooi C.Decoding global gene expression programs in liver cancer by noninvasive imaging.Nat Biotechnol2007;25:675-80

[101] Harding‐theobald E,Maraj B.Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.Aliment Pharmacol Ther2021;54:890-901 PMCID:PMC8435007

[102] Ji G,Xu Q.Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: a multi-institutional study.EBioMedicine2019;50:156-65 PMCID:PMC6923482

[103] Mokrane F,Vavasseur A.Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.Eur Radiol2019;30:558-70 PMCID:11631000

[104] Peng J,Ning Z.Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.Eur Radiol2019;30:413-24 PMCID:PMC6890698

[105] Taouli B,Kakite S.Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results.Eur Radiol2017;27:4472-81 PMCID:PMC5654702

[106] Huang S,Fong S.Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges.Cancer Lett2020;471:61-71 PMCID:12521688

[107] Hectors SJ,Besa C.MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma.Eur Radiol2020;30:3759-69 PMCID:PMC7869026

[108] Lv X,Zhang E.Predictive factors and prognostic models for Hepatic arterial infusion chemotherapy in Hepatocellular carcinoma: a comprehensive review.World J Surg Oncol2025;23:166 PMCID:PMC12034129

[109] Yu T,Lin Q.Computed tomography radiomics prediction of survival in hepatocellular carcinoma and is associated with ADH1A expression of the retinol metabolism pathway.Medicine2025;104:e42792 PMCID:12173319

[110] Tschandl P,Apalla Z.Human-computer collaboration for skin cancer recognition.Nat Med2020;26:1229-34 PMCID:7341032

[111] Mckinney SM,Godbole V.International evaluation of an AI system for breast cancer screening.Nature2020;577:89-94

[112] Kelly CJ,Suleyman M,King D.Key challenges for delivering clinical impact with artificial intelligence.BMC Med2019;17:195 PMCID:PMC6821018

[113] Tang Z,Wu J.Anomaly detection in medical via multimodal foundation models.Front Bioeng Biotechnol2025;13:1644697 PMCID:PMC12378924

[114] Liang W,Ho D.Author correction: advances, challenges and opportunities in creating data for trustworthy AI.Nat Mach Intell2022;4:904

[115] Rehm HL,Smith L.GA4GH: international policies and standards for data sharing across genomic research and healthcare.Cell Genomics2021;1:100029 PMCID:PMC8774288

[116] Yurdem B,Gullu MK,Tabassum M.Federated learning: overview, strategies, applications, tools and future directions.Heliyon2024;10:e38137 PMCID:PMC11466570

[117] Karako K.Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI.BST2024;18:497-504

[118] Rudin C.Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat Mach Intell2019;1:206-15 PMCID:PMC9122117

[119] Liu G,Chan AB.Human attention guided explainable artificial intelligence for computer vision models.Neural Netw2024;177:106392

[120] Satapathy SM,Garg A.GAN-enhanced hybrid deep learning with explainable AI for automated cataract diagnosis.J Med Syst2025;49:123

[121] Oncu E.Multimodal AI framework for lung cancer diagnosis: integrating CNN and ANN models for imaging and clinical data analysis.Comput Biol Med2025;193:110488

[122] Lee S,Oh N.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 Rep2025;15:1100 PMCID:PMC11707174

[123] Cheung CY,Wang S.A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study.The Lancet Digital Health2022;4:e806-15

[124] Colak C,Algarni A,Al-hashem F.Proposed comprehensive methodology integrated with explainable artificial intelligence for prediction of possible biomarkers in metabolomics panel of plasma samples for breast cancer detection.Medicina2025;61:581 PMCID:PMC12029019

[125] Bakrania A,Zhao X,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 Res2023;189:106706

[126] Lee C,Chain K,Hsu Z.Federated learning-based CT liver tumor detection using a teacher-student SANet with semisupervised learning.BMC Med Imaging2025;25:250 PMCID:PMC12217386

[127] Sadée C,Barba T.Medical digital twins: enabling precision medicine and medical artificial intelligence.The Lancet Digital Health2025;7:100864 PMCID:PMC12412312

[128] Laubenbacher R,Shmulevich I.Digital twins in medicine.Nat Comput Sci2024;4:184-91 PMCID:PMC11102043

[129] Feldman K,Chawla NV.The state of data in healthcare: path towards standardization.J Healthc Inform Res2018;2:248-71 PMCID:PMC8982788

[130] Brancato V,Coppola L.Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.J Transl Med2024;22:136 PMCID:PMC10845786

[131] Al-antari MA.Artificial intelligence for medical diagnostics - existing and future AI technology!.Diagnostics2023;13:688 PMCID:PMC9955430

[132] Parvin N,Jung JH.Multimodal AI in biomedicine: pioneering the future of biomaterials, diagnostics, and personalized healthcare.Nanomaterials2025;15:895 PMCID:PMC12195918

[133] Zubair M,Albashrawi MA,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 Biomed2025;272:109014

[134] Chatzipanagiotou OP,Vailas M.Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.J Gastroenterol Hepatol2024;39:1994-2005

[135] Chen X,Zhang K.Recent advances and clinical applications of deep learning in medical image analysis.Med Image Anal2022;79:102444 PMCID:PMC9156578

[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 Med2024;7:58 PMCID:PMC10918103

[137] Ennab M.Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Front Robot AI2024;11:1444763 PMCID:PMC11638409