Clinics in Colon and Rectal Surgery最新文献

{"title":"Can Artificial Intelligence Be Used to Predict Response in Rectal Cancer? Current Evidence and Future Possibilities.","authors":"Nicole E Lopez, Nicholas C Neel","doi":"10.1055/a-2769-1185","DOIUrl":"10.1055/a-2769-1185","url":null,"abstract":"<p><p>Artificial intelligence (AI) offers a promising solution to the long-standing challenge of accurately predicting treatment response in rectal cancer. In this narrative review, we summarize current AI-driven approaches to predicting pathologic complete response in rectal cancer. We also outline key barriers to clinical translation, including lack of standardization, small and geographically skewed training cohorts, domain shift across scanners and institutions, and broader ethical, regulatory, and medicolegal concerns. Finally, we highlight future directions, including federated learning to enable privacy-preserving multicenter model training, and emerging concepts such as virtual and digital twins that may support real-time adaptive therapy. These advances suggest that AI-based prediction of response in rectal cancer could be extremely valuable, but will require methodologically rigorous, multi-institutional efforts to be safely and equitably implemented.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"200-208"},"PeriodicalIF":1.6,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Patient Voice in Decisions Around Organ-Preserving Rectal Cancer Treatment","authors":"Kera Kwan, Tara A. Russell","doi":"10.1055/a-2771-7784","DOIUrl":"https://doi.org/10.1055/a-2771-7784","url":null,"abstract":"Abstract The patient's decision to pursue organ-sparing treatment for rectal cancer is nuanced and profoundly personal. As organ-sparing approaches like Watch-and-Wait become more widely adopted, it is increasingly important to understand how patients experience and navigate these complex decisions. Patients and providers may weigh treatment outcomes differently, reflecting varied priorities around survival, bowel function, and quality of life. This commentary draws on patient quotes to explore three key stages of decision-making: learning about treatment options, aligning values with choices, and coping with the aftermath of the decision. We highlight that patients often need tailored communication and educational resources to support understanding, that for some, avoiding an ostomy carries as much weight as oncologic outcomes like recurrence and survival, and that coping with intensive surveillance after treatment involves both emotional and logistical challenges that require ongoing support. These reflections underscore the need for accessible, patient-centered educational resources, communication strategies, and decision-support tools as organ-sparing treatments for rectal cancer become more widely implemented.","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147897184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Surgical Outcomes and Optimizing Patients for Surgery Using AI.","authors":"Neal Bhutiani, Abhineet Uppal","doi":"10.1055/a-2769-1109","DOIUrl":"10.1055/a-2769-1109","url":null,"abstract":"<p><p>Artificial intelligence (AI) platforms and machine learning (ML) algorithms provide the ability to utilize large amounts of electronically available data and leverage them to augment physicians' risk assessment and predictive abilities. These tools allow for refinement and/or reimagination of current tools aimed at predicting and optimizing outcomes in the perioperative period. In this section, we review AI and ML construction and implementation, the current state of predictive model use in surgical patients during the perioperative period, and discuss opportunities for integration into clinical care to optimize patients for surgery, improve perioperative patient care, and enable earlier detection of postoperative complications. We conclude with a discussion of limitations of AI and ML tools, along with points for consideration regarding their implementation in perioperative surgical care.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"194-199"},"PeriodicalIF":1.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethical Considerations for the Use of Artificial Intelligence Tools in Surgery.","authors":"Nicole Meredyth, Paola A Barrios","doi":"10.1055/a-2769-0687","DOIUrl":"10.1055/a-2769-0687","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming surgical care, offering unprecedented capabilities in diagnostics, planning, and intraoperative guidance. However, its integration into clinical practice raises complex ethical challenges that must be addressed to ensure responsible and equitable use. This chapter aims to explore the ethical implications of AI in surgery through the lens of the four foundational principles of medical ethics: autonomy, beneficence, nonmaleficence, and justice. Respecting patient autonomy requires clinicians to disclose all relevant information and ensure understanding of a proposed intervention (or AI tool) for a patient to make an informed, voluntary decision. Numerous studies have shown that both patients and clinicians often lack sufficient understanding of AI tools, complicating efforts to explain their intended function, or obtain truly informed consent for their use in patient care. This has the potential to diminish trust in the patient-clinician relationship and must be considered when using AI tools. AI's potential to uphold beneficence is evident in its ability to enhance surgical precision and outcomes. Yet, its reliance on historical data introduces risks of bias and error, threatening the principle of nonmaleficence. In this chapter, we explore these topics further to highlight the need for robust oversight and clinician involvement to prevent harm to patients. When biased data are used to train an AI tool, it may lead to unequal care across patient populations-i.e., exacerbating existing disparities in health care. Additionally, the lack of clear accountability for AI-driven errors raises legal and ethical concerns about liability-whether it lies with clinicians, health care institutions, or developers. To ethically integrate AI into surgical practice, the chapter calls for comprehensive frameworks that ensure transparency, data integrity, clinician and patient education, and regulatory oversight. These measures are essential to safeguard patient welfare as AI continues to reshape the future of surgical care.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"169-173"},"PeriodicalIF":1.6,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence to Improve Patient-Physician Communication in Surgery.","authors":"Elizabeth A Clement, Lawrence Lee","doi":"10.1055/a-2769-1301","DOIUrl":"10.1055/a-2769-1301","url":null,"abstract":"<p><p>Effective patient-physician communication is a cornerstone of surgical care, yet increasing clinical complexity and time constraints often limit opportunities for shared decision-making. Artificial intelligence (AI) is emerging as a tool to supplement these interactions by improving perioperative risk communication, tailoring patient education, and reducing physician workload. Current applications include chatbots for preconsultation history gathering, AI-generated decision aids, and risk prediction models, which synthesize large datasets to provide individualized outcome predictions. Visualization platforms and large language model-driven documents further demonstrate potential to improve readability, completeness, and patient comprehension compared with traditional surgeon-generated materials. Research suggests that AI may enhance shared decision-making by increasing patient understanding, reducing decisional conflict, and supporting individualized risk-benefit discussions. Populations with complex needs-including patients with multiple comorbidities, limited English proficiency, or low health literacy-may experience particular benefit from AI-assisted communication. Indirect advantages are also evident; studies of ambient AI scribes report decreased documentation burden, reduced after-hours work, and improved patient engagement, suggesting downstream improvements in the patient-physician relationship. Despite these promising developments, limitations persist. AI models are prone to bias and lack transparency, raising concerns regarding fairness, accuracy, and trustworthiness. Overreliance on AI risks diminishing essential human elements of care, while errors in translation or misrepresentation of nuance may disproportionately affect vulnerable groups. Ensuring robust evaluation, representative training datasets, and clinician oversight is critical to safe implementation. AI tools show substantial promise in improving surgical communication, but more research is required to establish their effectiveness, address ethical challenges, and define best practices for integration into clinical care.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"215-218"},"PeriodicalIF":1.6,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence as the Ultimate Operative Assistant.","authors":"Carl G Engelke, Rami J N Aoun, Emily Huang","doi":"10.1055/a-2769-1318","DOIUrl":"10.1055/a-2769-1318","url":null,"abstract":"<p><p>Artificial intelligence (AI) is poised to become transformational in many aspects of modern society and has attracted significant interest within the field of medicine. This review outlines the foundational components of modern AI such as including machine learning, deep learning, natural language processing, computer vision, and generative modeling, and examines their emerging applications within surgery. In the preoperative domain, AI-driven risk stratification models inform patient selection and resource allocation, while parallel advances in deep learning-enabled anatomic segmentation and three-dimensional reconstruction have the potential to streamline surgical planning by automating labor-intensive imaging workflows. Intraoperatively, maturing capabilities in phase recognition, anatomic identification, augmented reality overlay, and real-time decision support demonstrate the possibility for improved safety, workflow efficiency, and early recognition of surgical and physiologic challenges. And although the first fully autonomous AI-driven surgical robot in humans is likely still far off, the recent advances in robotic surgery suggest this may no longer be the purview of science fiction. For all its promise, significant challenges still persist for the robust implementation of AI into surgical workflows regarding data governance, algorithmic transparency, regulatory oversight, model generalizability, and, especially, many philosophical and ethical questions that remain unanswered.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"219-226"},"PeriodicalIF":1.6,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Its Role in Endoscopic Adenoma and Cancer Detection.","authors":"Hannah R Phillips, Wilfor J Diaz Fernandez, Cadman L Leggett","doi":"10.1055/a-2769-1233","DOIUrl":"10.1055/a-2769-1233","url":null,"abstract":"<p><p>Colorectal cancer incidence and mortality have declined over time, due in part to high-quality screening and surveillance colonoscopy. Nevertheless, postcolonoscopy colorectal cancer (PCCRC) occurs in up to 7% of cases and is inversely related to examination quality. Artificial intelligence-assisted colonoscopy aims to improve performance metrics and, ultimately, patient outcomes. Multiple randomized trials show that computer-aided polyp detection (CADe) increases adenoma detection, predominantly for diminutive lesions (≤5 mm). Computer-aided polyp characterization (CADx) enables real-time optical diagnosis, potentially shifting management of diminutive polyps by supporting resect-and-discard and diagnose-and-leave in situ strategies. Computer-aided quality assessment (CAQ) systems monitor key metrics-including cecal intubation rate, withdrawal time, speed, and mucosal exposure. Whether CADe alone leads to a reduction in PCCRC or cancer-related mortality remains to be determined; in the near term, a combined approach using CADe, CADx, and CAQ is most likely to deliver the greatest improvements in patient outcomes.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"209-214"},"PeriodicalIF":1.6,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Skills Training in Resident Education.","authors":"Grace J Kim","doi":"10.1055/a-2769-0941","DOIUrl":"10.1055/a-2769-0941","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming surgical education. AI is defined by the characteristics of objectivity, live feedback, and adaptive learning environment and is well suited for mentor-led self-directed learning. AI can be used to teach cognitive, psychomotor, and nontechnical skills. With limited supportive data, the use of AI in surgical education should proceed with caution with careful study of the process and its outcomes until more data supporting its use are available.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"174-177"},"PeriodicalIF":1.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface: Artificial Intelligence in Surgical Research, Education, and Patient Care.","authors":"Carrie Y Peterson","doi":"10.1055/a-2769-0290","DOIUrl":"10.1055/a-2769-0290","url":null,"abstract":"","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"167-168"},"PeriodicalIF":1.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Perioperative Artificial Intelligence in Colon and Rectal Surgery.","authors":"Kevin A Chen, Shah Feroz, Stefan D Holubar","doi":"10.1055/a-2769-1052","DOIUrl":"10.1055/a-2769-1052","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming surgical practice with growing applications in colon and rectal surgery. This review explores perioperative AI tools that assist with operative planning, intraoperative guidance, and outcome optimization. Preoperative innovations include machine learning models that outperform traditional risk calculators for predicting complications and readmissions, as well as computer vision and radiomics for analyzing imaging in colorectal cancer and inflammatory bowel disease. The integration of molecular and multiomics data further enhances personalized, precision surgical planning. Intraoperatively, deep learning enables computational identification of critical anatomy, including vascular structures, ureters, and pelvic nerves, and supports the objective analysis of advanced imaging techniques such as indocyanine green fluorescence. In terms of surgical techniques, AI-driven video analysis facilitates surgical phase recognition and automated skill assessment, whereas emerging vision-language models and surgical foundation models promise improved documentation and context-aware guidance. Future directions include generative AI for simulation, AI-based coaching, and progress toward autonomous surgical robotics. Although research remains in the early stages and is not yet ready for widespread clinical implementation, ongoing work within the field of colorectal surgery underscores the potential of AI to augment decision-making and standardize surgical care.</p>","PeriodicalId":48754,"journal":{"name":"Clinics in Colon and Rectal Surgery","volume":"39 3","pages":"185-193"},"PeriodicalIF":1.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13053104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}