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Wojtera B, Szewczyk M, Pieńkowski P, Golusiński W. Artificial intelligence in head and neck surgery: Potential applications and future perspectives. J Surg Oncol 2024; 129:1051-1055. [PMID: 38419212 DOI: 10.1002/jso.27616] [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: 02/01/2024] [Revised: 02/05/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.
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Affiliation(s)
- Bartosz Wojtera
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Mateusz Szewczyk
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Piotr Pieńkowski
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Wojciech Golusiński
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
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Li W, Ge X, Liu S, Xu L, Zhai X, Yu L. Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Front Med (Lausanne) 2024; 10:1336175. [PMID: 38274445 PMCID: PMC10808796 DOI: 10.3389/fmed.2023.1336175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners' expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality.
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Affiliation(s)
- Wenyu Li
- School of Marxism, Capital Normal University, Beijing, China
| | - Xiaolei Ge
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Shuai Liu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Lili Xu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Xu Zhai
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Linyong Yu
- China Academy of Chinese Medical Sciences, Beijing, China
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Marcus HJ, Ramirez PT, Khan DZ, Layard Horsfall H, Hanrahan JG, Williams SC, Beard DJ, Bhat R, Catchpole K, Cook A, Hutchison K, Martin J, Melvin T, Stoyanov D, Rovers M, Raison N, Dasgupta P, Noonan D, Stocken D, Sturt G, Vanhoestenberghe A, Vasey B, McCulloch P. The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring. Nat Med 2024; 30:61-75. [PMID: 38242979 DOI: 10.1038/s41591-023-02732-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/20/2023] [Indexed: 01/21/2024]
Abstract
The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot's development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots-providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.
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Affiliation(s)
- Hani J Marcus
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK.
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK.
| | - Pedro T Ramirez
- Department of Obstetrics and Gynaecology, Houston Methodist Hospital Neal Cancer Center, Houston, TX, USA
| | - Danyal Z Khan
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Simon C Williams
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - David J Beard
- RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR), Nuffield Dept Orthopaedics, Rheumatology and Musculo-skeletal Sciences, University of Oxford, Oxford, UK
| | - Rani Bhat
- Department of Gynaecological Oncology, Apollo Hospital, Bengaluru, India
| | - Ken Catchpole
- Department of Anaesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew Cook
- NIHR Coordinating Centre and Clinical Trials Unit, University of Southampton, Southampton, UK
| | | | - Janet Martin
- Department of Anesthesia & Perioperative Medicine, University of Western Ontario, Ontario, Canada
| | - Tom Melvin
- Department of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Republic of Ireland
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Maroeska Rovers
- Department of Medical Imaging, Radboudumc, Nijmegen, the Netherlands
| | - Nicholas Raison
- Department of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Prokar Dasgupta
- King's Health Partners Academic Surgery, King's College London, London, UK
| | | | - Deborah Stocken
- RCSEng Surgical Trials Centre, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | | | - Anne Vanhoestenberghe
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Baptiste Vasey
- Department of Surgery, Geneva University Hospital, Geneva, Switzerland
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK.
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Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Semin Cancer Biol 2023; 93:83-96. [PMID: 37116818 DOI: 10.1016/j.semcancer.2023.04.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/12/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.
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Affiliation(s)
- Zhe Wang
- Department of Digestive Diseases 1, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning, China
| | - Yang Liu
- Department of Gastric Surgery, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning, China.
| | - Xing Niu
- China Medical University, Shenyang 110122, Liaoning, China.
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