REFERENCES
1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209-49.
2. Rumgay H, Arnold M, Ferlay J, et al. Global burden of primary liver cancer in 2020 and predictions to 2040. J Hepatol. 2022;77:1598-606.
3. Rodriguez LA, Schmittdiel JA, Liu L, et al. Hepatocellular carcinoma in metabolic dysfunction-associated steatotic liver disease. JAMA Netw Open. 2024;7:e2421019.
4. Reig M, Cabibbo G. Antiviral therapy in the palliative setting of HCC (BCLC-B and -C). J Hepatol. 2021;74:1225-33.
5. Cabibbo G, Aghemo A, Lai Q, Masarone M, Montagnese S, Ponziani FR; Italian Association for the Study of the Liver (AISF). Optimizing systemic therapy for advanced hepatocellular carcinoma: the key role of liver function. Dig Liver Dis. 2022;54:452-60.
6. Association for the Study of the Liver. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69:182-236.
7. Singal AG, Llovet JM, Yarchoan M, et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78:1922-65.
8. Llovet JM, Villanueva A, Marrero JA, et al; AASLD Panel of Experts on Trial Design in HCC. Trial design and endpoints in hepatocellular carcinoma: AASLD consensus conference. Hepatology. 2021;73 Suppl 1:158-91.
9. Calderaro J, Couchy G, Imbeaud S, et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J Hepatol. 2017;67:727-38.
10. Sia D, Jiao Y, Martinez-Quetglas I, et al. Identification of an immune-specific class of hepatocellular carcinoma, based on molecular features. Gastroenterology. 2017;153:812-26.
11. Pecorelli A, Lenzi B, Gramenzi A, et al; Italian LiverCancer (ITA.LI.CA) group. Curative therapies are superior to standard of care (transarterial chemoembolization) for intermediate stage hepatocellular carcinoma. Liver Int. 2017;37:423-33.
12. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6:94-8.
13. Cheng N, Ren Y, Zhou J, et al. Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images. Gastroenterology. 2022;162:1948-61.e7.
14. Shi JY, Wang X, Ding GY, et al. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut. 2021;70:951-61.
15. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.
16. Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020;370:m3210.
17. Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep learning in medical image analysis. Adv Exp Med Biol. 2020;1213:3-21.
18. Mall PK, Singh PK, Srivastav S, et al. A comprehensive review of deep neural networks for medical image processing: recent developments and future opportunities. Healthc Anal. 2023;4:100216.
19. Rodriguez JPM, Rodriguez R, Silva VWK, et al. Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: a systematic review. J Pathol Inform. 2022;13:100138.
20. 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.
21. 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.
22. Le NQK, Li W, Cao Y. Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. Brief Bioinform. 2023;24:bbad319.
23. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature. 2023;620:172-80.
24. Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology. 2020;72:2000-13.
25. Yamashita R, Long J, Saleem A, Rubin DL, Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep. 2021;11:2047.
26. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res. 2018;24:1248-59.
27. Zeng Q, Klein C, Caruso S, et al. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J Hepatol. 2022;77:116-27.
28. Zeng Q, Klein C, Caruso S, et al; HCC-AI study group. Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study. Lancet Oncol. 2023;24:1411-22.
29. Liu Y, Xun Z, Ma K, et al. Identification of a tumour immune barrier in the HCC microenvironment that determines the efficacy of immunotherapy. J Hepatol. 2023;78:770-82.
30. Lipkova J, Chen RJ, Chen B, et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40:1095-110.
31. Nguyen HS, Ho DKN, Nguyen NN, Tran HM, Tam KW, Le NQK. Predicting EGFR mutation status in non-small cell lung cancer using artificial intelligence: a systematic review and meta-analysis. Acad Radiol. 2024;31:660-83.
32. Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. 2019;29:3338-47.
33. Zhen SH, Cheng M, Tao YB, et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol. 2020;10:680.
34. Ding Y, Ruan S, Wang Y, et al. Novel deep learning radiomics model for preoperative evaluation of hepatocellular carcinoma differentiation based on computed tomography data. Clin Transl Med. 2021;11:e570.
35. Xia TY, Zhou ZH, Meng XP, et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology. 2023;307:e222729.
36. Jensen CT, Gupta S, Saleh MM, et al. Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology. 2022;303:90-8.
37. 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. 2020;30:413-24.
38. Maida M, Celsa C, Lau LHS, et al. The application of large language models in gastroenterology: a review of the literature. Cancers. 2024;16:3328.
39. Maida M, Ramai D, Mori Y, Dinis-Ribeiro M, Facciorusso A, Hassan C; and the AI-CORE (Artificial Intelligence COlorectal cancer Research) Working Group. The role of generative language systems in increasing patient awareness of colon cancer screening. Endoscopy. 2024;Online ahead of print.
40. Truhn D, Reis-Filho JS, Kather JN. Large language models should be used as scientific reasoning engines, not knowledge databases. Nat Med. 2023;29:2983-4.
41. Clusmann J, Kolbinger FR, Muti HS, et al. The future landscape of large language models in medicine. Commun Med. 2023;3:141.
42. Cheng K, Guo Q, He Y, et al. Artificial intelligence in sports medicine: could GPT-4 make human doctors obsolete? Ann Biomed Eng. 2023;51:1658-62.
43. Yeo YH, Samaan JS, Ng WH, et al. Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol. 2023;29:721-32.
44. Cao JJ, Kwon DH, Ghaziani TT, et al. Accuracy of information provided by ChatGPT regarding liver cancer surveillance and diagnosis. AJR Am J Roentgenol. 2023;221:556-9.
45. Singal AG, Lampertico P, Nahon P. Epidemiology and surveillance for hepatocellular carcinoma: new trends. J Hepatol. 2020;72:250-61.
46. Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021;27:2176-82.
47. Chu CH, Donato-Woodger S, Khan SS, et al. Age-related bias and artificial intelligence: a scoping review. Humanit Soc Sci Commun. 2023;10:510.
48. Kaissis GA, Makowski MR, Rückert D, et al. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2:305-11.
49. World Health Organization. Regulatory considerations on artificial intelligence for health: World Health Organization 2023. Available from: https://books.google.com/books?hl=zh-CN&lr=&id=YHoOEQAAQBAJ&oi=fnd&pg=PR5&dq=+Regulatory+considerations+on+artificial+intelligence+for+health:+World+Health+Organization+&ots=WpgouRrmWW&sig=GNSP3CzM51dMuoZ-AVVwV6C25jc#v=onepage&q=Regulatory%20considerations%20on%20artificial%20intelligence%20for%20health%3A%20World%20Health%20Organization&f=false. [Last accessed on 19 Dec 2024].
50. Warraich HJ, Tazbaz T, Califf RM. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA. 2024;Online ahead of print.
51. Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3:118.
52. U.S. Food and Drug Administration. Good machine learning practice for medical device development: guiding principles. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles. [Last accessed on 19 Dec 2024].
53. Vokinger KN, Feuerriegel S, Kesselheim AS. Continual learning in medical devices: FDA’s action plan and beyond. Lancet Digit Health. 2021;3:e337-8.
54. Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, et al. Human-in-the-loop machine learning: a state of the art. Artif Intell Rev. 2023;56:3005-54.
55. Stefanini B, Manfredi GF, D'Alessio A, et al. Delivering adjuvant and neoadjuvant treatments in the early stages of hepatocellular carcinoma. Expert Rev Gastroenterol Hepatol. 2024;18:647-60.