{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:56:06Z","timestamp":1787273766412,"version":"build-2736575974"},"reference-count":71,"publisher":"Georg Thieme Verlag KG","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clin Colon Rectal Surg"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multidisciplinary team (MDT) discussions have become a cornerstone of colorectal cancer (CRC) management, integrating the expertise of surgeons, oncologists, radiologists, pathologists, and allied health professionals to facilitate personalized, evidence-based care. However, increasing complexity in treatment options, particularly with the rise of neoadjuvant strategies and immunotherapies, has rendered decision-making more challenging. Traditional tools\u2014including clinical guidelines, risk calculators, and nomograms\u2014offer structured decision support but lack flexibility and personalization. Artificial intelligence (AI), particularly through machine learning (ML), radiomics, and large language models (LLMs), is emerging as a transformative adjunct to clinical decision-making in CRC. Machine learning models have demonstrated strong predictive performance for treatment response, recurrence risk, and surgical complications, while radiomics and deep learning have improved diagnostic accuracy and treatment response prediction using imaging and endoscopy. LLMs such as ChatGPT have shown promising concordance with MDT recommendations in early studies, especially for standard clinical scenarios. However, limitations remain in handling complex, nuanced cases. Despite their growing capabilities, AI and LLMs are not yet integrated into routine MDT workflows due to concerns about interpretability, regulatory oversight, and ethical challenges. Future directions include developing real-time, multimodal AI-MDT platforms, improving explainability, ensuring equitable data representation, and integrating AI training into medical education. This review outlines current evidence on AI integration within CRC MDTs, highlighting both its clinical potential and the barriers that must be addressed to ensure safe, effective, and equitable implementation. Ultimately, AI is poised to augment\u2014not replace\u2014human expertise, enhancing the consistency, efficiency, and personalization of multidisciplinary CRC care.<\/jats:p>","DOI":"10.1055\/a-2769-1363","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:51:11Z","timestamp":1767138671000},"page":"227-234","source":"Crossref","is-referenced-by-count":1,"title":["AI Clinical Decision Tools in Multidisciplinary Team Discussions for Colorectal Cancer"],"prefix":"10.1055","volume":"39","author":[{"given":"Nir","family":"Horesh","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, Ellen Leifer Shulman and Steven Shulman Digestive Disease Center, Cleveland Clinic Florida, Weston, Florida, United States"},{"name":"Department of Surgery and Transplantations, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjelli","family":"Wignakumar","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, Ellen Leifer Shulman and Steven Shulman Digestive Disease Center, Cleveland Clinic Florida, Weston, Florida, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameh Hany","family":"Emile","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, Ellen Leifer Shulman and Steven Shulman Digestive Disease Center, Cleveland Clinic Florida, Weston, Florida, United States"},{"name":"Department of General Surgery, Colorectal Surgery Unit, Mansoura University, Mansoura, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"issue":"10","key":"ref1","first-page":"e71269","article-title":"The Mayo brothers: pioneers of modern medicine and patient-centered care","volume":"16","author":"K Gnanapandithan","year":"2024","journal-title":"Cureus"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fonc.2020.00085","article-title":"The multidisciplinary team (MDT) approach and quality of care","volume":"10","author":"M Taberna","year":"2020","journal-title":"Front Oncol"},{"issue":"11","key":"ref3","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.7150\/jca.56171","article-title":"The effect of multidisciplinary team discussion intervention on the prognosis of advanced colorectal cancer","volume":"12","author":"H Zhang","year":"2021","journal-title":"J Cancer"},{"issue":"03","key":"ref4","doi-asserted-by":"crossref","first-page":"zrab044","DOI":"10.1093\/bjsopen\/zrab044","article-title":"A systematic review of colorectal multidisciplinary team meetings: an international comparison","volume":"5","author":"M Fehervari","year":"2021","journal-title":"BJS Open"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.2147\/JMDH.S362550","article-title":"From \u201ccure\u201d to \u201ccare\u201d: the role of the multidisciplinary team on colorectal cancer patients' satisfaction and oncological outcomes","volume":"15","author":"A Lucarini","year":"2022","journal-title":"J Multidiscip Healthc"},{"issue":"07","key":"ref6","doi-asserted-by":"crossref","first-page":"075001","DOI":"10.1088\/1361-6560\/ab7970","article-title":"Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer","volume":"65","author":"J Fu","year":"2020","journal-title":"Phys Med Biol"},{"issue":"09","key":"ref7","doi-asserted-by":"crossref","first-page":"e13712","DOI":"10.1002\/acm2.13712","article-title":"Automation of radiation treatment planning for rectal cancer","volume":"23","author":"K Huang","year":"2022","journal-title":"J Appl Clin Med Phys"},{"issue":"01","key":"ref8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1055\/s-0041-1740034","article-title":"The essential role of a multidisciplinary approach in inflammatory bowel diseases","volume":"35","author":"P G Kotze","year":"2022","journal-title":"Clin Colon Rectal Surg"},{"issue":"06","key":"ref9","doi-asserted-by":"crossref","first-page":"366","DOI":"10.6004\/jnccn.2024.0041","article-title":"NCCN Guidelines\u00ae insights: rectal cancer, version 3.2024","volume":"22","author":"A B Benson","year":"2024","journal-title":"J Natl Compr Canc Netw"},{"issue":"09","key":"ref10","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1097\/DCR.0000000000001762","article-title":"The American Society of Colon and Rectal Surgeons Clinical Practice Guidelines for the management of rectal cancer","volume":"63","author":"Y N You","year":"2020","journal-title":"Dis Colon Rectum"},{"issue":"09","key":"ref11","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.annonc.2025.05.528","article-title":"Localised rectal cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up","volume":"36","author":"R D Hofheinz","year":"2025","journal-title":"Ann Oncol"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/1754-9493-8-19","article-title":"\u201cThe National Surgery Quality Improvement Project\u201d (NSQIP): a new tool to increase patient safety and cost efficiency in a surgical intensive care unit","volume":"8","author":"J McNelis","year":"2014","journal-title":"Patient Saf Surg"},{"issue":"11","key":"ref13","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1001\/archsurg.142.11.1043","article-title":"Comparison of P-POSSUM and Cr-POSSUM scores in patients undergoing colorectal cancer resection","volume":"142","author":"M Horzic","year":"2007","journal-title":"Arch Surg"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"115777","DOI":"10.1016\/j.amjsurg.2024.115777","article-title":"Predictors of nodal positivity in clinically under-staged patients with colon cancer: a national cancer database study and proposal of a predictive scoring system","volume":"235","author":"J Dourado","year":"2024","journal-title":"Am J Surg"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"102034","DOI":"10.1016\/j.suronc.2024.102034","article-title":"Surgical outcomes and predictors of overall survival of stage I-III appendiceal adenocarcinoma: retrospective cohort analysis of the national cancer database","volume":"52","author":"S H Emile","year":"2024","journal-title":"Surg Oncol"},{"issue":"41","key":"ref16","doi-asserted-by":"crossref","first-page":"11877","DOI":"10.3748\/wjg.v21.i41.11877","article-title":"Nomograms for colorectal cancer: a systematic review","volume":"21","author":"K Kawai","year":"2015","journal-title":"World J Gastroenterol"},{"issue":"01","key":"ref17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.canlet.2016.05.033","article-title":"Big Data and machine learning in radiation oncology: state of the art and future prospects","volume":"382","author":"J E Bibault","year":"2016","journal-title":"Cancer Lett"},{"issue":"09","key":"ref18","doi-asserted-by":"crossref","first-page":"4571","DOI":"10.1073\/pnas.1906831117","article-title":"Expert-augmented machine learning","volume":"117","author":"E D Gennatas","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"04","key":"ref19","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1007\/s13311-023-01384-2","article-title":"Machine learning in clinical trials: a primer with applications to neurology","volume":"20","author":"M I Miller","year":"2023","journal-title":"Neurotherapeutics"},{"issue":"03","key":"ref20","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.surg.2023.06.045","article-title":"Optimal policy tree to assist in adjuvant therapy decision-making after resection of colorectal liver metastases","volume":"175","author":"Y Endo","year":"2024","journal-title":"Surgery"},{"issue":"13","key":"ref21","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1200\/JCO.23.01080","article-title":"Machine learning predicts oxaliplatin benefit in early colon cancer","volume":"42","author":"L Chen","year":"2024","journal-title":"J Clin Oncol"},{"issue":"28","key":"ref22","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.1200\/JCO.2011.36.4539","article-title":"Oxaliplatin as adjuvant therapy for colon cancer: updated results of NSABP C-07 trial, including survival and subset analyses","volume":"29","author":"G Yothers","year":"2011","journal-title":"J Clin Oncol"},{"issue":"01","key":"ref23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1200\/JCO.2010.30.0855","article-title":"Phase III trial assessing bevacizumab in stages II and III carcinoma of the colon: results of NSABP protocol C-08","volume":"29","author":"C J Allegra","year":"2011","journal-title":"J Clin Oncol"},{"issue":"09","key":"ref24","doi-asserted-by":"crossref","first-page":"110194","DOI":"10.1016\/j.ejso.2025.110194","article-title":"Leveraging automated machine learning to predict colon cancer prognosis from clinical features and risk groups: a retrospective cohort study","volume":"51","author":"J Woerner","year":"2025","journal-title":"Eur J Surg Oncol"},{"issue":"13","key":"ref25","doi-asserted-by":"crossref","first-page":"8717","DOI":"10.1245\/s10434-023-14136-5","article-title":"Machine-learning algorithms using systemic inflammatory markers to predict the oncologic outcomes of colorectal cancer after surgery","volume":"30","author":"S Yang","year":"2023","journal-title":"Ann Surg Oncol"},{"issue":"02","key":"ref26","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.ejso.2022.09.020","article-title":"Artificial intelligence assists surgeons' decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection","volume":"49","author":"S Shao","year":"2023","journal-title":"Eur J Surg Oncol"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jss.2023.07.040","article-title":"A risk-prediction platform for acute kidney injury and 30-day readmission after colorectal surgery","volume":"292","author":"J R Nellis","year":"2023","journal-title":"J Surg Res"},{"issue":"04","key":"ref28","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s13304-024-01915-2","article-title":"An artificial intelligence-designed predictive calculator of conversion from minimally invasive to open colectomy in colon cancer","volume":"76","author":"S H Emile","year":"2024","journal-title":"Updates Surg"},{"issue":"12","key":"ref29","doi-asserted-by":"crossref","first-page":"3100","DOI":"10.1007\/s00268-022-06728-1","article-title":"Machine learning algorithms for predicting surgical outcomes after colorectal surgery: a systematic review","volume":"46","author":"M Bekta\u015f","year":"2022","journal-title":"World J Surg"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"1590620","DOI":"10.1155\/2022\/1590620","article-title":"Application of radiomics in predicting treatment response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: strategies and challenges","volume":"2022","author":"R Chen","year":"2022","journal-title":"J Oncol"},{"issue":"17","key":"ref31","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.3390\/diagnostics13172760","article-title":"Redefining radiology: a review of artificial intelligence integration in medical imaging","volume":"13","author":"R Najjar","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"02","key":"ref32","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"R J Gillies","year":"2016","journal-title":"Radiology"},{"issue":"05","key":"ref33","doi-asserted-by":"crossref","first-page":"e185","DOI":"10.1002\/mp.13678","article-title":"Machine and deep learning methods for radiomics","volume":"47","author":"M Avanzo","year":"2020","journal-title":"Med Phys"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"1230698","DOI":"10.3389\/fonc.2023.1230698","article-title":"Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer","volume":"13","author":"Y Niu","year":"2023","journal-title":"Front Oncol"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"585767","DOI":"10.3389\/fonc.2020.585767","article-title":"Multiregional-based magnetic resonance imaging radiomics combined with clinical data improves efficacy in predicting lymph node metastasis of rectal cancer","volume":"10","author":"X Liu","year":"2021","journal-title":"Front Oncol"},{"issue":"02","key":"ref36","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1007\/s00330-021-08242-9","article-title":"Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer","volume":"32","author":"Z Shu","year":"2022","journal-title":"Eur Radiol"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.amjsurg.2024.01.002","article-title":"Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer","volume":"232","author":"Z Sun","year":"2024","journal-title":"Am J Surg"},{"issue":"01","key":"ref38","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1186\/s12876-024-03316-6","article-title":"Preoperative prediction of rectal cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3","volume":"24","author":"L Fan","year":"2024","journal-title":"BMC Gastroenterol"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"1194120","DOI":"10.3389\/fonc.2023.1194120","article-title":"Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer","volume":"13","author":"X Dong","year":"2023","journal-title":"Front Oncol"},{"issue":"01","key":"ref40","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s11701-024-01978-8","article-title":"MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer","volume":"18","author":"Z Liu","year":"2024","journal-title":"J Robot Surg"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"109326","DOI":"10.1016\/j.ejrad.2020.109326","article-title":"Preoperative prediction of peritoneal metastasis in colorectal cancer using a clinical-radiomics model","volume":"132","author":"M Li","year":"2020","journal-title":"Eur J Radiol"},{"issue":"01","key":"ref42","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/s13244-024-01733-5","article-title":"A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer","volume":"15","author":"D Zhang","year":"2024","journal-title":"Insights Imaging"},{"issue":"08","key":"ref43","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1111\/codi.16641","article-title":"How reliable is restaging MRI after neoadjuvant therapy in rectal cancer?","volume":"25","author":"R Gefen","year":"2023","journal-title":"Colorectal Dis"},{"issue":"04","key":"ref44","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.canrad.2024.04.003","article-title":"Significance of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer: a narrative review","volume":"28","author":"Y Li","year":"2024","journal-title":"Cancer Radiother"},{"issue":"21","key":"ref45","doi-asserted-by":"crossref","first-page":"5256","DOI":"10.1158\/1078-0432.CCR-15-2997","article-title":"Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI","volume":"22","author":"K Nie","year":"2016","journal-title":"Clin Cancer Res"},{"issue":"03","key":"ref46","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1007\/s00261-019-02321-8","article-title":"Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer","volume":"45","author":"J JM van Griethuysen","year":"2020","journal-title":"Abdom Radiol (NY)"},{"issue":"12","key":"ref47","doi-asserted-by":"crossref","first-page":"e1195","DOI":"10.1097\/DCR.0000000000002931","article-title":"A longitudinal MRI-based artificial intelligence system to predict pathological complete response after neoadjuvant therapy in rectal cancer: a multicenter validation study","volume":"66","author":"J Ke","year":"2023","journal-title":"Dis Colon Rectum"},{"issue":"01","key":"ref48","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41698-024-00516-x","article-title":"An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study","volume":"8","author":"L Cai","year":"2024","journal-title":"NPJ Precis Oncol"},{"issue":"01","key":"ref49","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s00384-025-04809-w","article-title":"Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review","volume":"40","author":"B K Jong","year":"2025","journal-title":"Int J Colorectal Dis"},{"issue":"06","key":"ref50","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3390\/jpm15060244","article-title":"Can radiomics predict pathologic complete response after neoadjuvant chemoradiotherapy for rectal cancer? A systematic review and meta-analysis of diagnostic-accuracy studies","volume":"15","author":"F Seretis","year":"2025","journal-title":"J Pers Med"},{"issue":"32","key":"ref51","doi-asserted-by":"crossref","first-page":"5306","DOI":"10.3748\/wjg.v27.i32.5306","article-title":"Radiomics and machine learning applications in rectal cancer: current update and future perspectives","volume":"27","author":"A Stanzione","year":"2021","journal-title":"World J Gastroenterol"},{"issue":"01","key":"ref52","doi-asserted-by":"crossref","first-page":"25453","DOI":"10.1038\/s41598-024-77079-1","article-title":"A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection","volume":"14","author":"D K Park","year":"2024","journal-title":"Sci Rep"},{"issue":"02","key":"ref53","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1159\/000543345","article-title":"Current status of artificial intelligence use in colonoscopy","volume":"106","author":"M Misawa","year":"2025","journal-title":"Digestion"},{"issue":"05","key":"ref54","doi-asserted-by":"crossref","first-page":"3592","DOI":"10.1007\/s00464-021-08685-7","article-title":"The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation","volume":"36","author":"H E Haak","year":"2022","journal-title":"Surg Endosc"},{"issue":"02","key":"ref55","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1148\/radiol.2021202208","article-title":"Assessing rectal cancer treatment response using coregistered endorectal photoacoustic and US imaging paired with deep learning","volume":"299","author":"X Leng","year":"2021","journal-title":"Radiology"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"217555","DOI":"10.1016\/j.canlet.2025.217555","article-title":"Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications","volume":"614","author":"S Zhou","year":"2025","journal-title":"Cancer Lett"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"1239788","DOI":"10.3389\/fonc.2023.1239788","article-title":"Application of artificial intelligence in endoscopic gastrointestinal tumors","volume":"13","author":"Y Xin","year":"2023","journal-title":"Front Oncol"},{"issue":"17","key":"ref58","doi-asserted-by":"crossref","first-page":"4693","DOI":"10.3390\/s20174693","article-title":"Artificial intelligence-driven oncology clinical decision support system for multidisciplinary teams","volume":"20","author":"K Lee","year":"2020","journal-title":"Sensors (Basel)"},{"issue":"01","key":"ref59","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1097\/DCR.0000000000003504","article-title":"Comparing the management recommendations of large language model and colorectal cancer multidisciplinary team: a pilot study","volume":"68","author":"N Horesh","year":"2025","journal-title":"Dis Colon Rectum"},{"issue":"03","key":"ref60","doi-asserted-by":"crossref","first-page":"zraf040","DOI":"10.1093\/bjsopen\/zraf040","article-title":"Concordance of ChatGPT artificial intelligence decision-making in colorectal cancer multidisciplinary meetings: retrospective study","volume":"9","author":"D Chatziisaak","year":"2025","journal-title":"BJS Open"},{"issue":"03","key":"ref61","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1111\/ans.18749","article-title":"Conversational artificial intelligence (chatGPT\u2122) in the management of complex colorectal cancer patients: early experience","volume":"94","author":"J M Choo","year":"2024","journal-title":"ANZ J Surg"},{"issue":"10","key":"ref62","doi-asserted-by":"crossref","first-page":"3535","DOI":"10.3390\/jcm14103535","article-title":"The role of artificial intelligence (ChatGPT-4o) in supporting tumor board decisions","volume":"14","author":"B Karabu\u011fa","year":"2025","journal-title":"J Clin Med"},{"issue":"01","key":"ref63","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41523-023-00557-8","article-title":"Large language model (ChatGPT) as a support tool for breast tumor board","volume":"9","author":"V Sorin","year":"2023","journal-title":"NPJ Breast Cancer"},{"issue":"06","key":"ref64","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1007\/s00404-023-07130-5","article-title":"Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases","volume":"308","author":"S Lukac","year":"2023","journal-title":"Arch Gynecol Obstet"},{"issue":"08","key":"ref65","doi-asserted-by":"crossref","first-page":"e70319","DOI":"10.14814\/phy2.70319","article-title":"Artificial intelligence in colorectal surgery multidisciplinary team approach\u2014from innovation to application","volume":"13","author":"A Murtada","year":"2025","journal-title":"Physiol Rep"},{"issue":"09","key":"ref66","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1111\/codi.17091","article-title":"Artificial intelligence in colorectal multidisciplinary team meetings. What are the medicolegal implications?","volume":"26","author":"Y Tjhin","year":"2024","journal-title":"Colorectal Dis"},{"issue":"15","key":"ref67","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.3390\/cancers14153803","article-title":"Artificial intelligence in colorectal cancer surgery: present and future perspectives","volume":"14","author":"G Quero","year":"2022","journal-title":"Cancers (Basel)"},{"issue":"10","key":"ref68","doi-asserted-by":"crossref","first-page":"e20265","DOI":"10.2196\/20265","article-title":"Clinical decision support may link multiple domains to improve patient care: viewpoint","volume":"8","author":"D Kao","year":"2020","journal-title":"JMIR Med Inform"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"101829","DOI":"10.1016\/j.copsyc.2024.101829","article-title":"Artificial intelligence and its implications for data privacy","volume":"58","author":"K D Martin","year":"2024","journal-title":"Curr Opin Psychol"},{"issue":"12","key":"ref70","doi-asserted-by":"crossref","first-page":"100609","DOI":"10.1016\/j.modpat.2024.100609","article-title":"Regulatory aspects of artificial intelligence and machine learning","volume":"37","author":"L Pantanowitz","year":"2024","journal-title":"Mod Pathol"},{"issue":"09","key":"ref71","doi-asserted-by":"crossref","first-page":"4984","DOI":"10.3390\/curroncol31090369","article-title":"The role of artificial intelligence on tumor boards: perspectives from surgeons, medical oncologists and radiation oncologists","volume":"31","author":"V Nardone","year":"2024","journal-title":"Curr Oncol"}],"container-title":["Clinics in Colon and Rectal Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2769-1363.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T22:40:06Z","timestamp":1775515206000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2769-1363"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,30]]},"references-count":71,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2026,4,6]]},"published-print":{"date-parts":[[2026,5]]}},"URL":"https:\/\/doi.org\/10.1055\/a-2769-1363","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["1531-0043","1530-9681"],"issn-type":[{"value":"1531-0043","type":"print"},{"value":"1530-9681","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,30]]}}}