Research graph
References from AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review. Local targets link to admitted publications; unresolved targets remain external evidence.
Achieving goal-concordant care: a conceptual model and approach to measuring serious illness communication and its impact
10.1089/jpm.2017.0459 · External reference
Illness trajectories and palliative care
10.1136/bmj.330.7498.1007 · External reference
Frailty as a predictor of surgical outcomes in older patients
10.1016/j.jamcollsurg.2010.01.028 · External reference
Artificial intelligence and machine learning in prediction of surgical complications: current state, applications, and implications
10.1177/00031348221101488 · External reference
Integration of AI in surgical decision support: improving clinical judgment
10.1007/s44186-024-00257-2 · External reference
Exploring the role of artificial intelligence chatbots in preoperative counseling for head and neck cancer surgery
10.1002/lary.31243 · External reference
The ethics of AI in health care: a mapping review
10.1016/j.socscimed.2020.113172 · External reference
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The crucial role of explainable artificial intelligence (XAI) in improving health care management
10.1007/s10729-025-09720-y · External reference
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Investigating data diversity and model robustness of AI applications in palliative care and hospice: protocol for scoping review
10.2196/56353 · External reference
Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and meta-analysis of diagnostic test accuracy
10.1186/s12882-022-03025-w · External reference
Artificial intelligence in perioperative medicine: a narrative review
10.4097/kja.22157 · External reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
10.1136/bmj.n71 · External reference
Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation
10.1136/bmj.g7647 · External reference
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Deployment of artificial intelligence in radiology: strategies for success
10.2214/ajr.24.31898 · External reference
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
10.1038/s41746-024-01106-8 · External reference
A short guide for medical professionals in the era of artificial intelligence
10.1038/s41746-020-00333-z · External reference
Quo vadis artificial intelligence?
10.1007/s44163-022-00022-8 · External reference
Machine learning and artificial intelligence: definitions, applications, and future directions
10.1007/s12178-020-09600-8 · External reference
Palliative care and end-of-life outcomes following high-risk surgery
10.1001/jamasurg.2019.5083 · External reference
Measuring functional outcomes in therapeutic trials for chronic disease
10.1016/0197-2456(84)90026-6 · External reference
Patient-reported outcomes: a new era in clinical research
10.4103/2229-3485.86879 · External reference
Prospects and challenges in using patient-reported outcomes in clinical practice
10.1007/s11136-008-9379-5 · External reference
Software tools for literature screening in systematic reviews in biomedical research
10.14573/altex.1902131 · External reference
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
10.1136/bmj.i4919 · External reference
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Considerations for addressing bias in artificial intelligence for health equity
10.1038/s41746-023-00913-9 · External reference
Patient safety and artificial intelligence in clinical care
10.1001/jamahealthforum.2023.5514 · External reference
10.36227/techrxiv.171746875.59016695/v1
10.36227/techrxiv.171746875.59016695/v1 · External reference
An introduction to machine learning
10.1002/cpt.1796 · External reference
Natural language processing
10.1002/wics.76 · External reference
Development of machine learning algorithm to predict the risk of incontinence after robot-assisted radical prostatectomy
10.1089/end.2024.0057 · External reference
Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer
10.3748/wjg.v30.i23.2991 · External reference
Postoperative bleeding risk prediction for patients undergoing colorectal surgery
10.1016/j.surg.2018.05.043 · External reference
Prediction of risk factors for pharyngo-cutaneous fistula after total laryngectomy using artificial intelligence
10.1016/j.oraloncology.2021.105357 · External reference
Development of machine learning models for the prediction of long-term feeding tube dependence after oral and oropharyngeal cancer surgery
10.1016/j.oraloncology.2023.106643 · External reference
Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis
10.1097/js9.0000000000001169 · External reference
Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China
10.3389/fonc.2024.1334082 · External reference
Can we use artificial intelligence cluster analysis to identify patients with metastatic breast cancer to the spine at highest risk of postoperative adverse events?
10.1016/j.wneu.2023.02.064 · External reference
Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer
10.3389/fendo.2023.1206840 · External reference
Machine learning methods to predict presence of residual cancer following hysterectomy
10.1038/s41598-022-06585-x · External reference
Machine learning models to prognose 30-day mortality in postoperative disseminated cancer patients
10.1016/j.suronc.2022.101810 · External reference
Using machine learning to predict 30-day readmission and reoperation following resection of supratentorial high-grade gliomas: an ACS NSQIP study involving 9418 patients
10.3171/2023.3.focus22652 · External reference
A preoperative risk model for early recurrence after radical resection may facilitate initial treatment decisions concerning the use of neoadjuvant therapy for patients with pancreatic ductal adenocarcinoma
10.1016/j.surg.2020.02.013 · External reference
Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence
10.3748/wjg.v26.i30.4453 · External reference
Development and validation of a nomogram for predicting postoperative early relapse and survival in hepatocellular carcinoma
10.6004/jnccn.2023.7069 · External reference
Alcohol consumption and cardiovascular disease risk: placing new data in context
10.1007/s11883-022-00992-1 · External reference
Identifying an optimal machine learning model generated circulating biomarker to predict chronic postoperative pain in patients undergoing hepatectomy
10.3389/fsurg.2022.1068321 · External reference
Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms
10.1007/s00330-022-08703-9 · External reference
Development and validation of a machine learning prognostic model for hepatocellular carcinoma recurrence after surgical resection
10.3389/fonc.2020.593741 · External reference
Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence
10.1007/s10151-022-02685-y · External reference
The Toronto postliver transplantation hepatocellular carcinoma recurrence calculator: a machine learning approach
10.1002/lt.26332 · External reference
Machine learning based prediction of recurrence after curative resection for rectal cancer
10.1371/journal.pone.0290141 · External reference
Nomogram prediction model of postoperative pneumonia in patients with lung cancer: a retrospective cohort study
10.3389/fonc.2023.1114302 · External reference
Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer
10.1007/s00432-022-04063-5 · External reference
Development of a machine learning-based risk model for postoperative complications of lung cancer surgery
10.1007/s00595-024-02878-y · External reference
A machine learning-based online prediction tool for predicting short-term postoperative outcomes following spinal tumor resections
10.3390/cancers15030812 · External reference
Development of machine learning algorithms for prediction of 30-day mortality after surgery for spinal metastasis
10.1093/neuros/nyy469 · External reference
Prediction of postoperative length of hospital stay based on differences in nursing narratives in elderly patients with epithelial ovarian cancer
10.1055/s-0040-1705122 · External reference
Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer
10.1038/s41598-023-42964-8 · External reference
Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology
10.1080/21681805.2019.1665579 · External reference
Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer
10.18632/oncotarget.24468 · External reference
Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancer
10.3892/ol.2023.14087 · External reference
Stratification of length of stay prediction following surgical cytoreduction in advanced high-grade serous ovarian cancer patients using artificial intelligence; the Leeds L-AI-OS score
10.3390/curroncol29120711 · External reference
Machine learning-based risk prediction of critical care unit admission for advanced stage high grade serous ovarian cancer patients undergoing cytoreductive surgery: the Leeds-Natal score
10.3390/jcm11010087 · External reference
Evolutionary learning-derived clinical-radiomic models for predicting early recurrence of hepatocellular carcinoma after resection
10.1159/000518728 · External reference
Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: retrospective cohort study
10.1016/j.ijsu.2021.106050 · External reference
Preoperative data-based deep learning model for predicting postoperative survival in pancreatic cancer patients
10.1016/j.ijsu.2022.106851 · External reference
Development of a machine learning algorithm to forecast the likelihood of postoperative neurological complications in patients with parotid tumors
10.1177/01455613241258648 · External reference
Multidimensional characteristics, prognostic role, and preoperative prediction of peritoneal sarcomatosis in retroperitoneal sarcoma
10.3389/fonc.2022.950418 · External reference
Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients
10.1111/jog.15960 · External reference
Training prediction models for individual risk assessment of postoperative complications after surgery for colorectal cancer
10.1007/s10151-022-02624-x · External reference
A nomogram based on nutrition-related indicators and computed tomography imaging features for predicting preoperative lymph node metastasis in curatively resected esophagogastric junction adenocarcinoma
10.1245/s10434-023-13378-7 · External reference
Prediction model for hepatocellular carcinoma recurrence after hepatectomy: machine learning-based development and interpretation study
10.1016/j.heliyon.2023.e22458 · External reference
Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study
10.1007/s00464-024-10681-6 · External reference
Machine learning algorithms to predict recurrence within 10 years after breast cancer surgery: a prospective cohort study
10.3390/cancers12123817 · External reference
Influencing factors and predictive model of postoperative infection in patients with primary hepatic carcinoma
10.1186/s12876-023-02713-7 · External reference
Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma
10.1016/j.surg.2020.06.031 · External reference
Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer
10.1007/s12672-022-00472-7 · External reference
A novel prediction model for colon cancer recurrence using auto-artificial intelligence
10.21873/anticanres.15276 · External reference
Prediction of 10-year overall survival in patients with operable cervical cancer using a probabilistic neural network
10.7150/jca.33945 · External reference
Machine learning model for predicting postoperative survival of patients with colorectal cancer
10.4143/crt.2021.206 · External reference
Machine learning approach to stratifying prognosis relative to tumor burden after resection of colorectal liver metastases: an international cohort analysis
10.1097/xcs.0000000000000094 · External reference
Machine learning risk prediction model of 90-day mortality after gastrectomy for cancer
10.1097/sla.0000000000005616 · External reference
Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study
10.1186/s12885-024-11996-2 · External reference
Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study
10.21037/jgo-23-18 · External reference
Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics
10.1007/s10151-017-1701-1 · External reference
Prediction of sentinel lymph node metastasis in breast cancer patients based on preoperative features: a deep machine learning approach
10.1038/s41598-024-51244-y · External reference
Application of machine learning for predicting anastomotic leakage in patients with gastric adenocarcinoma who received total or proximal gastrectomy
10.3390/jpm11080748 · External reference
Prediction of symptomatic anastomotic leak after rectal cancer surgery: a machine learning approach
10.1002/jso.27470 · External reference
Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models
10.1007/s10549-012-2174-6 · External reference
Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques
10.1016/j.spinee.2023.09.001 · External reference
Artificial neural networks—a method for prediction of survival following liver resection for colorectal cancer metastases
10.1016/j.ejso.2013.02.024 · External reference
A model based on artificial intelligence algorithm for monitoring recurrence of HCC after hepatectomy
10.1177/00031348211063549 · External reference
Complication prediction after esophagectomy with machine learning
10.3390/diagnostics14040439 · External reference
Machine learning algorithm identifies patients at high risk for early complications after intracranial tumor surgery: registry-based cohort study
10.1093/neuros/nyz145 · External reference
Machine learning-based prediction of postoperative pancreatic fistula following pancreaticoduodenectomy
10.1097/sla.0000000000006123 · External reference
The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model
10.1186/s12957-023-03278-1 · External reference
Combined application of inflammation-related biomarkers to predict postoperative complications of rectal cancer patients: a retrospective study by machine learning analysis
10.1007/s00423-023-03127-5 · External reference
Data-driven assisted decision making for surgical procedure of hepatocellular carcinoma resection and prognostic prediction: development and validation of machine learning models
10.3390/cancers15061784 · External reference
Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer
10.21037/jgo-20-436 · External reference
Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women
10.1016/j.apjon.2022.100101 · External reference
Using machine learning methods to assess lymphovascular invasion and survival in breast cancer: performance of combining preoperative clinical and MRI characteristics
10.1002/jmri.28647 · External reference
Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
10.1186/s41747-021-00248-8 · External reference
Utility of an artificial intelligence enabled electrocardiogram for risk assessment in liver transplant candidates
10.1007/s10620-023-07928-y · External reference
Deep learning model for predicting postoperative survival of patients with gastric cancer
10.3389/fonc.2024.1329983 · External reference
The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer
10.3389/fonc.2023.1117420 · External reference
Development and comparison of machine-learning models for predicting prolonged postoperative length of stay in lung cancer patients following video-assisted thoracoscopic surgery
10.1016/j.apjon.2024.100493 · External reference
Deep learning nomogram for preoperative distinction between xanthogranulomatous cholecystitis and gallbladder carcinoma: a novel approach for surgical decision
10.1016/j.compbiomed.2023.107786 · External reference
Early postoperative prediction of the risk of distant metastases in medullary thyroid cancer
10.3389/fendo.2023.1209978 · External reference
A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer
10.3389/fonc.2023.1034047 · External reference
A scoring model for predicting early recurrence of gastric cancer with normal preoperative tumor markers: a multicenter study
10.1016/j.ejso.2023.107094 · External reference
AI-teaming: redefining collaboration in the digital era
10.1016/j.copsyc.2024.101837 · External reference
The ethical dimensions of utilizing artificial intelligence in palliative care
10.1177/09697330241296874 · External reference
“You are not alone”: the allure and limitations of artificial intelligence in serious illness communication
10.1089/jpm.2023.0471 · External reference
Recommendations to ensure safety of AI in real-world clinical care
10.1001/jama.2024.24598 · External reference
Artificial intelligence and liability in medicine: balancing safety and innovation
10.1111/1468-0009.12504 · External reference
Artificial intelligence in surgery: the future is now
10.1159/000536393 · External reference
Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice
10.1371/journal.pdig.0000810 · External reference
Would you trust an AI team member? Team trust in human–AI teams
10.1111/joop.12504 · External reference
Utility and comparative performance of current artificial intelligence large language models as postoperative medical support chatbots in aesthetic surgery
10.1093/asj/sjae025 · External reference
Teaming in the AI era: AI-augmented frameworks for forming, simulating, and optimizing human teams
10.1145/3699682.3727574 · External reference
Generative artificial intelligence in surgery
10.1016/j.surg.2024.02.019 · External reference
ChatGPT and generative AI in urology and surgery—a narrative review
10.1002/bco2.390 · External reference
AI for IMPACTS framework for evaluating the long-term real-world IMPACTS of AI-powered clinician tools: systematic review and narrative synthesis
10.2196/67485 · External reference
