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Shang Z, Chauhan V, Devi K, Patil S. Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review. J Multidiscip Healthc 2024; 17:4011-4022. [PMID: 39165254 PMCID: PMC11333562 DOI: 10.2147/jmdh.s482757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 08/09/2024] [Indexed: 08/22/2024] Open
Abstract
Background Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. Main Applications The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Challenges and Limitations Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. Conclusion The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.
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Affiliation(s)
- Zifang Shang
- Guangdong Engineering Technological Research Centre of Clinical Molecular Diagnosis and Antibody Drugs, Meizhou People’s Hospital (Huangtang Hospital), Meizhou Academy of Medical Sciences, Meizhou, People’s Republic of China
| | - Varun Chauhan
- Multi-Disciplinary Research Unit, Government Institute of Medical Sciences, Greater Noida, India
| | - Kirti Devi
- Department of Medicine, Government Institute of Medical Sciences, Greater Noida, India
| | - Sandip Patil
- Department Haematology and Oncology, Shenzhen Children’s Hospital, Shenzhen, People’s Republic of China
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Mazard T, Mollevi C, Loyer EM, Léger J, Chautard R, Bouché O, Borg C, Armand-Dujardin P, Bleuzen A, Assenat E, Lecomte T. Prognostic value of the tumor-to-liver density ratio in patients with metastatic colorectal cancer treated with bevacizumab-based chemotherapy. A post-hoc study of the STIC-AVASTIN trial. Cancer Imaging 2024; 24:77. [PMID: 38886836 PMCID: PMC11181627 DOI: 10.1186/s40644-024-00722-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 06/10/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND The Response Evaluation Criteria in Solid Tumors (RECIST) are often inadequate for the early assessment of the response to cancer therapy, particularly bevacizumab-based chemotherapy. In a first cohort of patients with colorectal cancer liver metastases (CRLM), we showed that variations of the tumor-to-liver density (TTLD) ratio and modified size-based criteria determined using computed tomography (CT) data at the first restaging were better prognostic criteria than the RECIST. The aims of this study were to confirm the relevance of these radiological biomarkers as early predictors of the long-term clinical outcome and to assess their correlation with contrast-enhanced ultrasound (CEUS) parameters in a new patient cohort. METHODS In this post-hoc study of the multicenter STIC-AVASTIN trial, we retrospectively reviewed CT data of patients with CRLM treated with bevacizumab-based regimens. We determined the size, density and TTLD ratio of target liver lesions at baseline and at the first restaging and also performed a morphologic evaluation according to the MD Anderson criteria. We assessed the correlation of these parameters with progression-free survival (PFS) and overall survival (OS) using the log-rank test and a Cox proportional hazard model. We also examined the association between TTLD ratio and quantitative CEUS parameters. RESULTS This analysis concerned 79 of the 137 patients included in the STIC-AVASTIN trial. PFS and OS were significantly longer in patients with tumor size reduction > 15% at first restaging, but were not correlated with TTLD ratio variations. However, PFS was longer in patients with TTLD ratio > 0.6 at baseline and first restaging than in those who did not reach this threshold. In the multivariate analysis, only baseline TTLD ratio > 0.6 was a significant survival predictor. TTLD ratio > 0.6 was associated with improved perfusion parameters. CONCLUSIONS Although TTLD ratio variations did not correlate with the long-term clinical outcomes, TTLD absolute values remained a good predictor of survival at baseline and first restaging, and may reflect tumor microvascular features that might influence bevacizumab-based treatment efficiency. TRIAL REGISTRATION NCT00489697, registration number of the STIC-AVASTIN trial.
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Affiliation(s)
- Thibault Mazard
- Medical Oncology Department, Montpellier Cancer Institute (ICM), University of Montpellier, Institut de Recherche en Cancérologie de Montpellier (IRCM), INSERM U1194, 208 avenue des apothicaires, Parc Euromédecine, Montpellier Cedex 5, Montpellier, 34298, France.
| | - Caroline Mollevi
- Institute Desbrest of Epidemiology and Public Health, University of Montpellier, INSERM, Cancer Institute of Montpellier, Montpellier, France
| | - Evelyne M Loyer
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Julie Léger
- INSERM CIC 1415, CHRU de Tours, Tours Cedex 9, 37044, France
| | - Romain Chautard
- Department of Hepatogastroenterology and Digestive Oncology, UMR INSERM U 1069, Hôpital Trousseau, CHRU de Tours, Université de Tours, Tours Cedex 9, 37044, France
| | - Olivier Bouché
- Department of Hepatogastroenterology, Hôpital Robert Debré, CHU de Reims, Avenue Général Koenig, Reims Cedex, 51092, France
| | - Christophe Borg
- Department of Medical Oncology, Hôpital Jean Minjoz, CHRU de Besançon, 3 Boulevard Alexandre Fleming, Besançon, 25000, France
| | | | - Aurore Bleuzen
- Department of Radiology, CHRU de Tours, Tours Cedex 9, 37044, France
| | - Eric Assenat
- Medical Oncology Department, Montpellier Cancer Institute (ICM), University of Montpellier, CHU Montpellier, Montpellier, France
| | - Thierry Lecomte
- Department of Hepatogastroenterology and Digestive Oncology, UMR INSERM U 1069, Hôpital Trousseau, CHRU de Tours, Université de Tours, Tours Cedex 9, 37044, France
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Uppal S, Kumar Shrivastava P, Khan A, Sharma A, Kumar Shrivastav A. Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review. Int J Med Inform 2024; 186:105421. [PMID: 38552265 DOI: 10.1016/j.ijmedinf.2024.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
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Affiliation(s)
- Simran Uppal
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | | | - Atiya Khan
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Aditi Sharma
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
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Kagawa Y, Smith JJ, Fokas E, Watanabe J, Cercek A, Greten FR, Bando H, Shi Q, Garcia-Aguilar J, Romesser PB, Horvat N, Sanoff H, Hall W, Kato T, Rödel C, Dasari A, Yoshino T. Future direction of total neoadjuvant therapy for locally advanced rectal cancer. Nat Rev Gastroenterol Hepatol 2024; 21:444-455. [PMID: 38485756 DOI: 10.1038/s41575-024-00900-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 05/31/2024]
Abstract
Despite therapeutic advancements, disease-free survival and overall survival of patients with locally advanced rectal cancer have not improved in most trials as a result of distant metastases. For treatment decision-making, both long-term oncologic outcomes and impact on quality-of-life indices should be considered (for example, bowel function). Total neoadjuvant therapy (TNT), comprised of chemotherapy and radiotherapy or chemoradiotherapy, is now a standard treatment approach in patients with features of high-risk disease to prevent local recurrence and distant metastases. In selected patients who have a clinical complete response, subsequent surgery might be avoided through non-operative management, but patients who do not respond to TNT have a poor prognosis. Refined molecular characterization might help to predict which patients would benefit from TNT and non-operative management. Specifically, integrated analysis of spatiotemporal multi-omics using artificial intelligence and machine learning is promising. Three prospective trials of TNT and non-operative management in Japan, the USA and Germany are collaborating to better understand drivers of response to TNT. Here, we address the future direction for TNT.
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Affiliation(s)
- Yoshinori Kagawa
- Department of Gastroenterological Surgery, Osaka General Medical Center, Osaka, Japan
| | - J Joshua Smith
- Department of Surgery, Colorectal Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emmanouil Fokas
- Department of Radiotherapy and Oncology, University of Frankfurt, Frankfurt, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute, Frankfurt, Germany
- Department of Radiation Oncology, CyberKnife and Radiation Therapy, Centre for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
- German Cancer Consortium (DKTK), Frankfurt, Germany
| | - Jun Watanabe
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Andrea Cercek
- Gastrointestinal Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Florian R Greten
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute, Frankfurt, Germany
- German Cancer Consortium (DKTK), Frankfurt, Germany
- Institute for Tumour Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt, Germany
| | - Hideaki Bando
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, Chiba, Japan
| | - Qian Shi
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Julio Garcia-Aguilar
- Department of Surgery, Colorectal Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paul B Romesser
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hanna Sanoff
- Department of Medicine, Division of Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Takeshi Kato
- Department of Surgery, NHO Osaka National Hospital, Osaka, Japan
| | - Claus Rödel
- Department of Radiotherapy and Oncology, University of Frankfurt, Frankfurt, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute, Frankfurt, Germany
- German Cancer Consortium (DKTK), Frankfurt, Germany
| | - Arvind Dasari
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Takayuki Yoshino
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, Chiba, Japan.
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Thompson N, Morley-Bunker A, McLauchlan J, Glyn T, Eglinton T. Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review. BJS Open 2024; 8:zrae033. [PMID: 38637299 PMCID: PMC11026097 DOI: 10.1093/bjsopen/zrae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Risk evaluation of lymph node metastasis for early-stage (T1 and T2) colorectal cancers is critical for determining therapeutic strategies. Traditional methods of lymph node metastasis prediction have limited accuracy. This systematic review aimed to review the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers. METHODS A comprehensive search was performed of papers that evaluated the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers. Studies were appraised using the Joanna Briggs Institute tools. The primary outcome was summarizing artificial intelligence models and their accuracy. Secondary outcomes included influential variables and strategies to address challenges. RESULTS Of 3190 screened manuscripts, 11 were included, involving 8648 patients from 1996 to 2023. Due to diverse artificial intelligence models and varied metrics, no data synthesis was performed. Models included random forest algorithms, support vector machine, deep learning, artificial neural network, convolutional neural network and least absolute shrinkage and selection operator regression. Artificial intelligence models' area under the curve values ranged from 0.74 to 0.9993 (slide level) and 0.9476 to 0.9956 (single-node level), outperforming traditional clinical guidelines. CONCLUSION Artificial intelligence models show promise in predicting lymph node metastasis in early-stage colorectal cancers, potentially refining clinical decisions and improving outcomes. PROSPERO REGISTRATION NUMBER CRD42023409094.
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Affiliation(s)
- Nasya Thompson
- Department of Surgery, University of Otago, Christchurch, New Zealand
| | - Arthur Morley-Bunker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Jared McLauchlan
- Department of Surgery, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Tamara Glyn
- Department of Surgery, University of Otago, Christchurch, New Zealand
- Department of Surgery, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Tim Eglinton
- Department of Surgery, University of Otago, Christchurch, New Zealand
- Department of Surgery, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
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Wu D, Ni J, Fan W, Jiang Q, Wang L, Sun L, Cai Z. Opportunities and challenges of computer aided diagnosis in new millennium: A bibliometric analysis from 2000 to 2023. Medicine (Baltimore) 2023; 102:e36703. [PMID: 38134105 PMCID: PMC10735127 DOI: 10.1097/md.0000000000036703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND After entering the new millennium, computer-aided diagnosis (CAD) is rapidly developing as an emerging technology worldwide. Expanding the spectrum of CAD-related diseases is a possible future research trend. Nevertheless, bibliometric studies in this area have not yet been reported. This study aimed to explore the hotspots and frontiers of research on CAD from 2000 to 2023, which may provide a reference for researchers in this field. METHODS In this paper, we use bibliometrics to analyze CAD-related literature in the Web of Science database between 2000 and 2023. The scientometric softwares VOSviewer and CiteSpace were used to visually analyze the countries, institutions, authors, journals, references and keywords involved in the literature. Keywords burst analysis were utilized to further explore the current state and development trends of research on CAD. RESULTS A total of 13,970 publications were included in this study, with a noticeably rising annual publication trend. China and the United States are major contributors to the publication, with the United States being the dominant position in CAD research. The American research institutions, lead by the University of Chicago, are pioneers of CAD. Acharya UR, Zheng B and Chan HP are the most prolific authors. Institute of Electrical and Electronics Engineers Transactions on Medical Imaging focuses on CAD and publishes the most articles. New computer technologies related to CAD are in the forefront of attention. Currently, CAD is used extensively in breast diseases, pulmonary diseases and brain diseases. CONCLUSION Expanding the spectrum of CAD-related diseases is a possible future research trend. How to overcome the lack of large sample datasets and establish a universally accepted standard for the evaluation of CAD system performance are urgent issues for CAD development and validation. In conclusion, this paper provides valuable information on the current state of CAD research and future developments.
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Affiliation(s)
- Di Wu
- Department of Proctology, Yongchuan Hospital of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China
- Department of Proctology, Bishan Hospital of Traditional Chinese Medicine, Chongqing, China
- Chongqing College of Traditional Chinese Medicine, Chongqing, China
| | - Jiachun Ni
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wenbin Fan
- Department of Proctology, Bishan Hospital of Traditional Chinese Medicine, Chongqing, China
- Chongqing College of Traditional Chinese Medicine, Chongqing, China
| | - Qiong Jiang
- Chongqing College of Traditional Chinese Medicine, Chongqing, China
| | - Ling Wang
- Department of Proctology, Yongchuan Hospital of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China
| | - Li Sun
- Department of Proctology, Yongchuan Hospital of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China
| | - Zengjin Cai
- Department of Proctology, Yongchuan Hospital of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China
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Yadav A, Kumar A. Artificial intelligence in rectal cancer: What is the future? Artif Intell Cancer 2023; 4:11-22. [DOI: 10.35713/aic.v4.i2.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 12/07/2023] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.
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Affiliation(s)
- Alka Yadav
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
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Wei MYK, Zhang J, Schmidt R, Miller AS, Yeung JMC. Artificial intelligence (AI) in the management of colorectal cancer: on the horizon? ANZ J Surg 2023; 93:2052-2053. [PMID: 37489622 DOI: 10.1111/ans.18504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 07/26/2023]
Affiliation(s)
- Matthew Y K Wei
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
| | - Junyao Zhang
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Reuben Schmidt
- Department of Radiology, Western Health, Melbourne, Victoria, Australia
| | - Andrew S Miller
- Department of Colorectal Surgery, Whangarei Hospital, Whangarei, New Zealand
| | - Justin M C Yeung
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
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