What is Artificial Intelligence Surgery? Artificial Intelligence Surgery. 2021 Jan 30;1(1):null.

Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268(1):70–76

PubMed  Google Scholar 

Hashimoto DA, Ward TM, Meireles OR (2020) The role of artificial intelligence in surgery. Adv Surg 1(54):89–101

Google Scholar 

Jarvis T, Thornburg D, Rebecca AM, Teven CM (2020) Artificial intelligence in plastic surgery: current applications, future directions, and ethical implications. Plast Reconstr Surg Glob Open 8(10):e3200

PubMed  PubMed Central  Google Scholar 

Pettit RW, Fullem R, Cheng C, Amos CI (2021) Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 5(6):729–745

PubMed  PubMed Central  Google Scholar 

Trevor H, Robert T, Jerome F. The elements of statistical learning: data mining, inference, and prediction.

Shamout F, Zhu T, Clifton DA (2021) Machine learning for clinical outcome prediction. IEEE Rev Biomed Eng 14:116–126

PubMed  Google Scholar 

Buchlak QD, Esmaili N, Leveque JC, Farrokhi F, Bennett C, Piccardi M, Sethi RK (2020) Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev 43:1235–1253

PubMed  Google Scholar 

Castela Forte J, Yeshmagambetova G, van der Grinten ML, Scheeren TWL, Nijsten MWN, Mariani MA et al (2022) Comparison of machine learning models including preoperative, intraoperative, and postoperative data and mortality after cardiac surgery. JAMA Netw Open 5(10):e2237970

PubMed  PubMed Central  Google Scholar 

Yun K, Oh J, Hong TH, Kim EY (2021) Prediction of mortality in surgical intensive care unit patients using machine learning algorithms. Front Med 31(8):621861

Google Scholar 

Manz CR, Chen J, Liu M, Chivers C, Regli SH, Braun J, Draugelis M, Hanson CW, Shulman LN, Schuchter LM, O’Connor N (2020) Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer. JAMA Oncol 6(11):1723–1730

PubMed  PubMed Central  Google Scholar 

Lo YT, Liao JC, Chen MH, Chang CM, Li CT (2021) Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms. BMC Med Inform Decis Mak 21(1):288

PubMed  PubMed Central  Google Scholar 

Stabellini N, Nazha A, Agrawal N, Huhn M, Shanahan J, Hamerschlak N, Waite K, Barnholtz-Sloan JS, Montero AJ (2023) Thirty-day unplanned hospital readmissions in patients with cancer and the impact of social determinants of health: a machine learning approach. Clin Canc Informat 7:e2200143

Google Scholar 

Sutter T, Roth JA, Chin-Cheong K, Hug BL, Vogt JE (2021) A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. J Am Med Inform Assoc 28(4):868–873

PubMed  Google Scholar 

Borisov V, Leemann T, Seßler K, Haug J, Pawelczyk M, Kasneci G (2024) Deep neural networks and tabular data: a survey. Transact Neural Network Learn Syst 35(6):7499–7519

Google Scholar 

Dunnmon JA, Yi D, Langlotz CP, Ré C, Rubin DL, Lungren MP (2019) Assessment of convolutional neural networks for automated classification of chest radiographs. Radiology 290(2):537–544

PubMed  Google Scholar 

Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O (2018) Deep learning with convolutional neural network in radiology. Jpn J Radiol 36(4):257–272

PubMed  Google Scholar 

Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insight Imag 9(4):611–629

Google Scholar 

Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Procedia Comp Sci 1(90):200–205

Google Scholar 

Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V (2020) External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax 75(4):306–312

PubMed  Google Scholar 

Choi E, Schuetz A, Stewart WF, Sun J (2017) Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc 24(2):361–370

PubMed  Google Scholar 

Weerakody PB, Wong KW, Wang G, Ela W (2021) A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing 21(441):161–178

Google Scholar 

Antikainen E, Linnosmaa J, Umer A, Oksala N, Eskola M, van Gils M, Hernesniemi J, Gabbouj M (2023) Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records. Sci Rep 13(1):3517

CAS  PubMed  PubMed Central  Google Scholar 

Lyu W, Dong X, Wong R, Zheng S, Abell-Hart K, Wang F, Chen C. (2023). A multimodal transformer: Fusing clinical notes with structured ehr data for interpretable in-hospital mortality prediction. InAMIA Annual Symposium Proceedings 2022, 719

Suresh H, Szolovits P, Ghassemi M. The use of autoencoders for discovering patient phenotypes. arXiv preprint arXiv:1703.07004. 2017 Mar 20.

Basu S, Wagstyl K, Zandifar A, Collins L, Romero A, Precup D (2019) Early prediction of Alzheimer’s disease progression using variational autoencoders. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S et al (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Springer International Publishing, Cham, pp 205–213

Google Scholar 

Ferreira MF, Camacho R, Teixeira LF (2020) Using autoencoders as a weight initialization method on deep neural networks for disease detection. Med Inform Decis Mak 20(5):141

Google Scholar 

Aishwarya B, Durgagowri G. Maximizing sentiment detection through comprehensive multimodal data fusion: integrating CNN, RNN, LSTM. In2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) 2025 Jan 20 (pp. 1–7). IEEE.

Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Advan Neural Inform Process Syst. https://doi.org/10.48550/arXiv.1705.07874

Google Scholar 

Amann J, Blasimme A, Vayena E, Frey D, Madai VI (2020) Precise4Q consortium explainability for artificial intelligence in healthcare: a multidisciplinary perspective. Med Informat Dec Mak 20(1):1–9

Google Scholar 

Sahara K, Paredes AZ, Tsilimigras DI, Sasaki K, Moro A, Hyer JM et al (2021) Machine learning predicts unpredicted deaths with high accuracy following hepatopancreatic surgery. Hepatob Surg Nut 10(1):200–230

Google Scholar 

Santos CF, Papa JP (2022) Avoiding overfitting: a survey on regularization methods for convolutional neural networks. ACM Comput Surv 54(10s):1–25

Google Scholar 

Price WN, Cohen IG (2019) Privacy in the age of medical big data. Nat Med 25(1):37–43

CAS  PubMed  PubMed Central  Google Scholar 

Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH (2018) Ensuring fairness in machine learning to advance health equity. Ann Intern Med 169(12):866–872

PubMed  PubMed Central  Google Scholar 

Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453

CAS  PubMed  Google Scholar 

Nassiri K, Akhloufi MA (2024) Recent advances in large language models for healthcare. BioMedInformatics 4(2):1097–1143

Google Scholar 

Miotto R, Li L, Kidd BA, Dudley JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 6(1):26094

CAS  PubMed  PubMed Central  Google Scholar 

Choi E, Bahadori MT, Sun J, Kulas J, Schuetz A, Stewart W (2016) Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. Adv Neural Inform Proces Sys. https://doi.org/10.48550/arXiv.1608.05745

Google Scholar 

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P (2018) Scalable and accurate deep learning with electronic health records. NPJ digital medicine 1(1):18

PubMed  PubMed Central  Google Scholar 

Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, Meyer C, Ravuri S, Protsyuk I, Connell A (2019) A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572(7767):116–119

PubMed  PubMed Central  Google Scholar 

Wu F, Zhao G, Zhou Y, Qian X, Elias BK, Lehman LW (2023) Forecasting treatment outcomes over time using alternating deep sequential models. IEEE Trans Biomed Eng 71(4):1237–1246

Google Scholar 

Rong R, Gu Z, Lai H, Nelson TL, Keller T, Walker C et al (2025) A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records. JAMIA Open. https://doi.org/10.1101/2025.01.21.25320916

PubMed  PubMed Central  Google Scholar 

Merath K, Hyer JM, Mehta R, Farooq A, Bagante F, Sahara K, Tsilimigras DI, Beal E, Paredes AZ, Wu L, Ejaz A (2020) Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg 24(8):1843–1851

PubMed  Google Scholar 

Shipe ME, Deppen SA, Farjah F, Grogan EL (2019) Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis 11(Suppl 4):S574–S584

PubMed  PubMed Central  Google Scholar 

Fukumitsu K, Ishii T, Ogiso S, Yoh T, Uchida Y, Ito T et al (2023) Impact of patient-specific three-dimensional printed liver models on hepatic surgery safety: a pilot study. HPB 25(9):1083–1092

PubMed  Google Scholar 

Hu M, Hu H, Cai W, Mo Z, Xiang N, Yang J et al (2018) The safety and feasibility of three-dimensional visualization technology assisted right posterior lobe allied with Part of V and VIII sectionectomy for right hepatic malignancy therapy. J Laparoendosc Adv Surg Tech 28(5):586–594

Google Scholar 

Hua FC, Su TH, Yang J, Shan FZ, Cai W, Liu J et al (2015) Impact of three-dimensional reconstruction technique in the operation planning of centrally located hepatocellular carcinoma. J Amer Coll Surg 220(1):28

Google Scholar 

Xiang N, Fang C, Fan Y, Yang J, Zeng N, Liu J et al (2015) Application of liver three-dimensional printing in hepatectomy for complex massive hepatocarcinoma with rare variations of portal vein: preliminary experience. Int J Clin Exp Med 8(10):18873–18878

PubMed  PubMed Central