| [1] |
KANG HJ, LEE JM, AHN C, et al. Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: Prospective, randomized, double-blind study[J]. Eur Radiol, 2023, 33( 5): 3660- 3670. DOI: 10.1007/s00330-023-09520-4.
|
| [2] |
BAE JS, LEE JM, KIM SW, et al. Low-contrast-dose liver CT using low monoenergetic images with deep learning-based denoising for assessing hepatocellular carcinoma: A randomized controlled noninferiority trial[J]. Eur Radiol, 2023, 33( 6): 4344- 4354. DOI: 10.1007/s00330-022-09298-x.
|
| [3] |
YOON JH, PARK JY, LEE SM, et al. Renal protection CT protocol using low-dose and low-concentration iodine contrast medium in at-risk patients of HCC and with chronic kidney disease: A randomized controlled non-inferiority trial[J]. Cancer Imaging, 2023, 23( 1): 100. DOI: 10.1186/s40644-023-00616-0.
|
| [4] |
ODISIO BC, ALBUQUERQUE J, LIN YM, et al. Software-based versus visual assessment of the minimal ablative margin in patients with liver tumours undergoing percutaneous thermal ablation(COVER-ALL): A randomised phase 2 trial[J]. Lancet Gastroenterol Hepatol, 2025, 10( 5): 442- 451. DOI: 10.1016/S2468-1253(25)00024-X.
|
| [5] |
JEON SK, LEE JM, JOO I, et al. Two-dimensional convolutional neural network using quantitative US for noninvasive assessment of hepatic steatosis in NAFLD[J]. Radiology, 2023, 307( 1): e221510. DOI: 10.1148/radiol.221510.
|
| [6] |
WU CH, YEN KC, WANG LY, et al. Automated whole-liver fat quantification with magnetic resonance imaging-derived proton density fat fraction map: A prospective study in Taiwan[J]. Gut Liver, 2025, 19( 4): 617- 626. DOI: 10.5009/gnl240408.
|
| [7] |
JOSHI S, SHAMANNA P, DHARMALINGAM M, et al. Digital twin-enabled personalized nutrition improves metabolic dysfunction-associated fatty liver disease in type 2 diabetes: Results of a 1-year randomized controlled study[J]. Endocr Pract, 2023, 29( 12): 960- 970. DOI: 10.1016/j.eprac.2023.08.016.
|
| [8] |
TIYARATTANACHAI T, APIPARAKOON T, CHAICHUEN O, et al. Artificial intelligence assists operators in real-time detection of focal liver lesions during ultrasound: A randomized controlled study[J]. Eur J Radiol, 2023, 165: 110932. DOI: 10.1016/j.ejrad.2023.110932.
|
| [9] |
LEE DH, LEE JM, LEE CH, et al. Image quality and diagnostic performance of low-dose liver CT with deep learning reconstruction versus standard-dose CT[J]. Radiol Artif Intell, 2024, 6( 2): e230192. DOI: 10.1148/ryai.230192.
|
| [10] |
ZHANG YM, CUI J, WAN W, et al. Multimodal imaging under artificial intelligence algorithm for the diagnosis of liver cancer and its relationship with expressions of EZH2 and p57[J]. Comput Intell Neurosci, 2022, 2022: 4081654. DOI: 10.1155/2022/4081654.
|
| [11] |
KANG M, KIM MS. Managing postembolization syndrome through a machine learning-based clinical decision support system: A randomized controlled trial[J]. Comput Inform Nurs, 2024, 42( 11): 817- 828. DOI: 10.1097/CIN.0000000000001188.
|
| [12] |
LOOMBA R, NOUREDDIN M, KOWDLEY KV, et al. Combination therapies including cilofexor and firsocostat for bridging fibrosis and cirrhosis attributable to NASH[J]. Hepatology, 2021, 73( 2): 625- 643. DOI: 10.1002/hep.31622.
|
| [13] |
MCMICHAEL J, LIEBERMAN R, MCCAULEY J, et al. Computer-guided randomized concentration-controlled trials of tacrolimus in autoimmunity: Multiple sclerosis and primary biliary cirrhosis[J]. Ther Drug Monit, 1996, 18( 4): 435- 437. DOI: 10.1097/00007691-199608000-00021.
|
| [14] |
FENG SJ, WANG JH, WANG LH, et al. Current status and analysis of machine learning in hepatocellular carcinoma[J]. J Clin Transl Hepatol, 2023, 11( 5): 1184- 1191. DOI: 10.14218/JCTH.2022.00077S.
|
| [15] |
CALDERARO J, ŽIGUTYTĖ L, TRUHN D, et al. Artificial intelligence in liver cancer: New tools for research and patient management[J]. Nat Rev Gastroenterol Hepatol, 2024, 21( 8): 585- 599. DOI: 10.1038/s41575-024-00919-y.
|
| [16] |
GHOSH S, ZHAO X, ALIM M, et al. Artificial intelligence applied to’omics data in liver disease: Towards a personalised approach for diagnosis, prognosis and treatment[J]. Gut, 2025, 74( 2): e331740. DOI: 10.1136/gutjnl-2023-331740.
|
| [17] |
CALDERARO J, SERAPHIN TP, LUEDDE T, et al. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma[J]. J Hepatol, 2022, 76( 6): 1348- 1361. DOI: 10.1016/j.jhep.2022.01.014.
|
| [18] |
MATTHAY EC, NEILL DB, TITUS AR, et al. Integrating artificial intelligence into causal research in epidemiology[J]. Curr Epidemiol Rep, 2025, 12( 1): 6. DOI: 10.1007/s40471-025-00359-5.
|
| [19] |
CLUSMANN J, BALAGUER-MONTERO M, BASSEGODA O, et al. The barriers for uptake of artificial intelligence in hepatology and how to overcome them[J]. J Hepatol, 2025 DOI: 10.1016/j.jhep.2025.07.003.[ Online ahead of print]
|