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Pattilachan TM, Christodoulou M, Ross S. Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer. J Robot Surg 2024; 18:259. [PMID: 38900376 DOI: 10.1007/s11701-024-02005-6] [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: 05/09/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024]
Abstract
Gastric cancer remains a formidable health challenge worldwide; early detection and effective surgical intervention are critical for improving patient outcomes. This comprehensive review explores the evolving landscape of gastric cancer management, emphasizing the significant contributions of artificial intelligence (AI) in revolutionizing both diagnostic and therapeutic approaches. Despite advancements in the medical field, the subtle nature of early gastric cancer symptoms often leads to late-stage diagnoses, where survival rates are notably decreased. Historically, the treatment of gastric cancer has transitioned from palliative care to surgical resection, evolving further with the introduction of minimally invasive surgical (MIS) techniques. In the current era, AI has emerged as a transformative force, enhancing the precision of early gastric cancer detection through sophisticated image analysis, and supporting surgical decision-making with predictive modeling and real-time preop-, intraop-, and postoperative guidance. However, the deployment of AI in healthcare raises significant ethical, legal, and practical challenges, including the necessity for ongoing professional education and the development of standardized protocols to ensure patient safety and the effective use of AI technologies. Future directions point toward a synergistic integration of AI with clinical best practices, promising a new era of personalized, efficient, and safer gastric cancer management.
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Affiliation(s)
- Tara Menon Pattilachan
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA
| | - Maria Christodoulou
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA
| | - Sharona Ross
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA.
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2
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Khan JSA, Lonergan PE. Editorial Comment on "Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists". Urology 2024:S0090-4295(24)00449-7. [PMID: 38876390 DOI: 10.1016/j.urology.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Affiliation(s)
| | - Peter E Lonergan
- Department of Urology, St. James's Hospital, Dublin; Department of Surgery, School of Medicine, Trinity College Dublin, Dublin.
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Wilson HH, Ma C, Ku D, Scarola GT, Augenstein VA, Colavita PD, Heniford BT. Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up. Surg Endosc 2024:10.1007/s00464-024-10980-y. [PMID: 38862826 DOI: 10.1007/s00464-024-10980-y] [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: 04/13/2023] [Accepted: 06/02/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM's predictive ability. METHODS Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with < 18 months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence. RESULTS The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7 years (mean ± SD: 86 ± 39 months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5 ± 12.3 years and were majority (65.8%) female with a BMI of 34.2 ± 7.9 kg/m2. There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively. CONCLUSIONS The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.
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Affiliation(s)
- Hadley H Wilson
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Chiyu Ma
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Dau Ku
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Gregory T Scarola
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Vedra A Augenstein
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Paul D Colavita
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - B Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
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Jain H, Marsool MDM, Odat RM, Noori H, Jain J, Shakhatreh Z, Patel N, Goyal A, Gole S, Passey S. Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support. Cardiol Rev 2024:00045415-990000000-00260. [PMID: 38836621 DOI: 10.1097/crd.0000000000000708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.
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Affiliation(s)
- Hritvik Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | | | - Ramez M Odat
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Hamid Noori
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jyoti Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Zaid Shakhatreh
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nandan Patel
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Aman Goyal
- Department of Internal Medicine, Seth GS Medical College and KEM Hospital, Mumbai, India
| | - Shrey Gole
- Department of Immunology and Rheumatology, Stanford University, CA; and
| | - Siddhant Passey
- Department of Internal Medicine, University of Connecticut Health Center, CT
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Parchmann N, Hansen D, Orzechowski M, Steger F. An ethical assessment of professional opinions on concerns, chances, and limitations of the implementation of an artificial intelligence-based technology into the geriatric patient treatment and continuity of care. GeroScience 2024:10.1007/s11357-024-01229-6. [PMID: 38834930 DOI: 10.1007/s11357-024-01229-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024] Open
Abstract
With the introduction of an artificial intelligence-based dashboard into the clinic, the project SURGE-Ahead responds to the importance of improving perioperative geriatric patient treatment and continuity of care. The use of artificial intelligence to process and analyze data automatically, aims at an evidence-based evaluation of the patient's health condition and recommending treatment options. However, its development and introduction raise ethical questions. To ascertain professional perspectives on the clinical use of the dashboard, we have conducted 19 semi-structured qualitative interviews with head physicians, computer scientists, jurists, and ethicists. The application of a qualitative content analysis and thematic analysis enabled the detection of main ethical concerns, chances, and limitations. These ethical considerations were categorized: changes of the patient-physician relationship and the current social reality are expected, causing de-skilling and an active participation of the artificial intelligence. The interviewees anticipated a redistribution of human resources, time, knowledge, and experiences as well as expenses and financing. Concerns of privacy, accuracy, transparency, and explainability were stated, and an insufficient data basis, an intensifying of existing inequalities and systematic discrimination considering a fair access emphasized. Concluding, the patient-physician relationship, social reality, redistribution of resources, fair access, as well as data-related aspects of the artificial intelligence-based system could conflict with the ethical principles of autonomy, non-maleficence, beneficence, and social justice. To respond to these ethical concerns, a responsible use of the dashboard and a critical verification of therapy suggestions is mandatory, and the application limited by questions at the end of life and taking life-changing decisions.
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Affiliation(s)
- Nina Parchmann
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany.
| | - David Hansen
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
| | - Marcin Orzechowski
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
| | - Florian Steger
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
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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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7
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Zhang D, Du J, Shi J, Zhang Y, Jia S, Liu X, Wu Y, An Y, Zhu S, Pan D, Zhang W, Zhang Y, Feng S. A fully automatic MRI-guided decision support system for lumbar disc herniation using machine learning. JOR Spine 2024; 7:e1342. [PMID: 38817341 PMCID: PMC11137648 DOI: 10.1002/jsp2.1342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Background Normalized decision support system for lumbar disc herniation (LDH) will improve reproducibility compared with subjective clinical diagnosis and treatment. Magnetic resonance imaging (MRI) plays an essential role in the evaluation of LDH. This study aimed to develop an MRI-based decision support system for LDH, which evaluates lumbar discs in a reproducible, consistent, and reliable manner. Methods The research team proposed a system based on machine learning that was trained and tested by a large, manually labeled data set comprising 217 patients' MRI scans (3255 lumbar discs). The system analyzes the radiological features of identified discs to diagnose herniation and classifies discs by Pfirrmann grade and MSU classification. Based on the assessment, the system provides clinical advice. Results Eventually, the accuracy of the diagnosis process reached 95.83%. An 83.5% agreement was observed between the system's prediction and the ground-truth in the Pfirrmann grade. In the case of MSU classification, 95.0% precision was achieved. With the assistance of this system, the accuracy, interpretation efficiency and interrater agreement among surgeons were improved substantially. Conclusion This system showed considerable accuracy and efficiency, and therefore could serve as an objective reference for the diagnosis and treatment procedure in clinical practice.
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Affiliation(s)
- Di Zhang
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Jiawei Du
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Jiaxiao Shi
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Yundong Zhang
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Siyue Jia
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Xingyu Liu
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Yu Wu
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Yicheng An
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Shibo Zhu
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Dayu Pan
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Wei Zhang
- School of Control Science and Engineering, Shandong UniversityJinanPeople's Republic of China
| | - Yiling Zhang
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Shiqing Feng
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
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Sosa BR, Cung M, Suhardi VJ, Morse K, Thomson A, Yang HS, Iyer S, Greenblatt MB. Capacity for large language model chatbots to aid in orthopedic management, research, and patient queries. J Orthop Res 2024; 42:1276-1282. [PMID: 38245845 DOI: 10.1002/jor.25782] [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: 10/02/2023] [Revised: 12/18/2023] [Accepted: 12/24/2023] [Indexed: 01/22/2024]
Abstract
Large language model (LLM) chatbots possess a remarkable capacity to synthesize complex information into concise, digestible summaries across a wide range of orthopedic subject matter. As LLM chatbots become widely available they will serve as a powerful, accessible resource that patients, clinicians, and researchers may reference to obtain information about orthopedic science and clinical management. Here, we examined the performance of three well-known and easily accessible chatbots-ChatGPT, Bard, and Bing AI-in responding to inquiries relating to clinical management and orthopedic concepts. Although all three chatbots were found to be capable of generating relevant responses, ChatGPT outperformed Bard and BingAI in each category due to its ability to provide accurate and complete responses to orthopedic queries. Despite their promising applications in clinical management, shortcomings observed included incomplete responses, lack of context, and outdated information. Nonetheless, the ability for these LLM chatbots to address these inquires has largely yet to be evaluated and will be critical for understanding the risks and opportunities of LLM chatbots in orthopedics.
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Affiliation(s)
- Branden R Sosa
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Michelle Cung
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Vincentius J Suhardi
- Research Division and Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Kyle Morse
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Andrew Thomson
- Research Division and Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - He S Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Sravisht Iyer
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Matthew B Greenblatt
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York, USA
- Research Division and Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
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Toledano Trincado M, Bellido-Luque J, Álvarez Gallego M. Robotic surgery as a driver of surgical digitalization. Cir Esp 2024:S2173-5077(24)00135-2. [PMID: 38801975 DOI: 10.1016/j.cireng.2024.05.004] [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: 05/02/2024] [Accepted: 05/04/2024] [Indexed: 05/29/2024]
Abstract
Classical surgery, also called analog surgery, is transmitted to us by our mentors, whose knowledge has been delegated from generation to generation throughout the history of surgery. Its main limitations are limited surgical precision and dependence on the surgeon's skill to achieve surgical goals. So-called digital surgery incorporates the most advanced technology, with the aim of improving the results of all phases of the surgical process. Robotic platforms are currently considered to be one of the main drivers of the digital transformation of surgery. They bring considerable advances to the digitalization of surgery, including: higher quality visualization, more controlled and stable movements with elimination of tremor, minimized risk of errors, data integration throughout the patient's surgical process, use of various systems for better surgical planning, application of virtual and augmented reality, telementoring, and artificial intelligence.
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10
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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [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: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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11
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Lima DL, Kasakewitch J, Nguyen DQ, Nogueira R, Cavazzola LT, Heniford BT, Malcher F. Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review. Hernia 2024:10.1007/s10029-024-03069-x. [PMID: 38761300 DOI: 10.1007/s10029-024-03069-x] [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: 02/27/2024] [Accepted: 04/29/2024] [Indexed: 05/20/2024]
Abstract
INTRODUCTION This systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery. METHODS The PRISMA guidelines were followed throughout this systematic review. The ROBINS-I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis. RESULTS A total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications. CONCLUSION The use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
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Affiliation(s)
- D L Lima
- Department of Surgery, Montefiore Medical Center, New York, NY, USA.
| | - J Kasakewitch
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - D Q Nguyen
- Albert Einstein, College of Medicine, New York, USA
| | - R Nogueira
- Department of Surgery, Montefiore Medical Center, New York, NY, USA
| | - L T Cavazzola
- Federal University of Rio Grande Do Sul, Porto Alegre, Brazil
| | - B T Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - F Malcher
- Division of General Surgery, NYU Langone, New York, USA
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12
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Liu L, Qu S, Zhao H, Kong L, Xie Z, Jiang Z, Zou P. Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study. Front Med (Lausanne) 2024; 11:1406842. [PMID: 38818395 PMCID: PMC11137200 DOI: 10.3389/fmed.2024.1406842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/06/2024] [Indexed: 06/01/2024] Open
Abstract
Objective With the rapid advancement of Chat Generative Pre-Trained Transformer (ChatGPT) in medical research, our study aimed to identify global trends and focal points in this domain. Method All publications on ChatGPT in medical research were retrieved from the Web of Science Core Collection (WoSCC) by Clarivate Analytics from January 1, 2023, to January 31, 2024. The research trends and focal points were visualized and analyzed using VOSviewer and CiteSpace. Results A total of 1,239 publications were collected and analyzed. The USA contributed the largest number of publications (458, 37.145%) with the highest total citation frequencies (2,461) and the largest H-index. Harvard University contributed the highest number of publications (33) among all full-time institutions. The Cureus Journal of Medical Science published the most ChatGPT-related research (127, 10.30%). Additionally, Wiwanitkit V contributed the majority of publications in this field (20). "Artificial Intelligence (AI) and Machine Learning (ML)," "Education and Training," "Healthcare Applications," and "Data Analysis and Technology" emerged as the primary clusters of keywords. These areas are predicted to remain hotspots in future research in this field. Conclusion Overall, this study signifies the interdisciplinary nature of ChatGPT research in medicine, encompassing AI and ML technologies, education and training initiatives, diverse healthcare applications, and data analysis and technology advancements. These areas are expected to remain at the forefront of future research, driving continued innovation and progress in the field of ChatGPT in medical research.
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Affiliation(s)
- Ling Liu
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
- School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Shenhong Qu
- Department of Otolaryngology-Head and Neck Oncology, The People’s Hospital of Guangxi Zhuang Autonoms Region, Nanning, China
| | - Haiyun Zhao
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| | - Lingping Kong
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| | - Zhuzhu Xie
- School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Zhichao Jiang
- Hunan Provincial Brain Hospital, The Second People’s Hospital of Hunan Province, Changsha, China
| | - Pan Zou
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
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13
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Bektaş M, Tan C, Burchell GL, Daams F, van der Peet DL. Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108385. [PMID: 38755062 DOI: 10.1016/j.ejso.2024.108385] [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: 01/19/2024] [Revised: 02/29/2024] [Accepted: 05/01/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgical outcomes. The current status of AI-based clinical decision-making within gastrointestinal surgery is unknown in recent literature. This review aims to provide an overview of AI models used for clinical decision-making within gastrointestinal surgery. METHODS A systematic literature search was performed in databases PubMed, EMBASE, Cochrane, and Web of Science. To be eligible for inclusion, studies needed to use AI models for clinical decision-making involving patients undergoing gastrointestinal surgery. Studies reporting on reviews, children, and study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. RESULTS Out of 1073 studies, 10 articles were eligible for inclusion. AI models have been used to make clinical decisions between surgical procedures, selection of chemotherapy, selection of postoperative follow up programs, and implementation of a temporary ileostomy. Most studies have used a Random Forest or Gradient Boosting model with AUCs up to 0.97. All studies involved a retrospective study design, in which external validation was performed in one study. CONCLUSIONS This review shows that AI models have the potentiality to select the most optimal treatments for patients undergoing gastrointestinal surgery. Clinical benefits could be gained if AI models were used for clinical decision-making. However, prospective studies and randomized controlled trials will reveal the definitive role of AI models in clinical decision-making.
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Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands.
| | - Cevin Tan
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - George L Burchell
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Medical Library, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
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14
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Davis JMK, Niazi MKK, Ricker AB, Tavolara TE, Robinson JN, Annanurov B, Smith K, Mantha R, Hwang J, Shrestha R, Iannitti DA, Martinie JB, Baker EH, Gurcan MN, Vrochides D. Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging. J Surg Oncol 2024. [PMID: 38712939 DOI: 10.1002/jso.27673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/13/2024] [Accepted: 04/28/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND AND OBJECTIVES Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.
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Affiliation(s)
- Joshua M K Davis
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Muhammad Khalid Khan Niazi
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Ansley B Ricker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Thomas E Tavolara
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Jordan N Robinson
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Bayram Annanurov
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Kaylee Smith
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Rohit Mantha
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Jimmy Hwang
- Department of Medical Oncology, Atrium Health Carolinas Medical Center, Levine Cancer Institute, Charlotte, North Carolina, USA
| | - Ruchi Shrestha
- Department of Radiology, Atrium Health, Charlotte, North Carolina, USA
| | - David A Iannitti
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - John B Martinie
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Erin H Baker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Dionisios Vrochides
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
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15
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McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, Stocken DD, Treanor D. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med 2024; 7:114. [PMID: 38704465 PMCID: PMC11069583 DOI: 10.1038/s41746-024-01106-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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Affiliation(s)
- Clare McGenity
- University of Leeds, Leeds, UK.
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Emily L Clarke
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charlotte Jennings
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | | | - Darren Treanor
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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16
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Marsh KM, Turrentine FE, Jin R, Schirmer BD, Hanks JB, Davis JP, Schenk WG, Jones RS. Judgment Errors in Surgical Care. J Am Coll Surg 2024; 238:874-879. [PMID: 38258825 PMCID: PMC11023767 DOI: 10.1097/xcs.0000000000001011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
BACKGROUND Human error is impossible to eliminate, particularly in systems as complex as healthcare. The extent to which judgment errors in particular impact surgical patient care or lead to harm is unclear. STUDY DESIGN The American College of Surgeons NSQIP (2018) procedures from a single institution with 30-day morbidity or mortality were examined. Medical records were reviewed and evaluated for judgment errors. Preoperative variables associated with judgment errors were examined using logistic regression. RESULTS Of the surgical patients who experienced a morbidity or mortality, 18% (31 of 170) experienced an error in judgment during their hospitalization. Patients with hepatobiliary procedure (odds ratio [OR] 5.4 [95% CI 1.23 to 32.75], p = 0.002), insulin-dependent diabetes (OR 4.8 [95% CI 1.2 to 18.8], p = 0.025), severe COPD (OR 6.0 [95% CI 1.6 to 22.1], p = 0.007), or with infected wounds (OR 8.2 [95% CI 2.6 to 25.8], p < 0.001) were at increased risk for judgment errors. CONCLUSIONS Specific procedure types and patients with certain preoperative variables had higher risk for judgment errors during their hospitalization. Errors in judgment adversely impacted the outcomes of surgical patients who experienced morbidity or mortality in this cohort. Preventing or mitigating errors and closely monitoring patients after an error in judgment is prudent and may improve surgical safety.
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Affiliation(s)
- Katherine M. Marsh
- Department of Surgery, University of Virginia, Charlottesville, Virginia
| | | | - Ruyun Jin
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Bruce D. Schirmer
- Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - John B. Hanks
- Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - John P. Davis
- Department of Surgery, University of Virginia, Charlottesville, Virginia
| | | | - R. Scott Jones
- Department of Surgery, University of Virginia, Charlottesville, Virginia
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17
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [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: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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18
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Nwaiwu CA, Rivera Perla KM, Abel LB, Sears IJ, Barton AT, Peterson RC, Liu YZ, Khatri IS, Sarkar IN, Shah N. Predicting Colonic Neoplasia Surgical Complications: A Machine Learning Approach. Dis Colon Rectum 2024; 67:700-713. [PMID: 38319746 DOI: 10.1097/dcr.0000000000003166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
BACKGROUND A range of statistical approaches have been used to help predict outcomes associated with colectomy. The multifactorial nature of complications suggests that machine learning algorithms may be more accurate in determining postoperative outcomes by detecting nonlinear associations, which are not readily measured by traditional statistics. OBJECTIVE The aim of this study was to investigate the utility of machine learning algorithms to predict complications in patients undergoing colectomy for colonic neoplasia. DESIGN Retrospective analysis using decision tree, random forest, and artificial neural network classifiers to predict postoperative outcomes. SETTINGS National Inpatient Sample database (2003-2017). PATIENTS Adult patients who underwent elective colectomy with anastomosis for neoplasia. MAIN OUTCOME MEASURES Performance was quantified using sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve to predict the incidence of anastomotic leak, prolonged length of stay, and inpatient mortality. RESULTS A total of 14,935 patients (4731 laparoscopic, 10,204 open) were included. They had an average age of 67 ± 12.2 years, and 53% of patients were women. The 3 machine learning models successfully identified patients who developed the measured complications. Although differences between model performances were largely insignificant, the neural network scored highest for most outcomes: predicting anastomotic leak, area under the receiver operating characteristic curve 0.88/0.93 (open/laparoscopic, 95% CI, 0.73-0.92/0.80-0.96); prolonged length of stay, area under the receiver operating characteristic curve 0.84/0.88 (open/laparoscopic, 95% CI, 0.82-0.85/0.85-0.91); and inpatient mortality, area under the receiver operating characteristic curve 0.90/0.92 (open/laparoscopic, 95% CI, 0.85-0.96/0.86-0.98). LIMITATIONS The patients from the National Inpatient Sample database may not be an accurate sample of the population of all patients undergoing colectomy for colonic neoplasia and does not account for specific institutional and patient factors. CONCLUSIONS Machine learning predicted postoperative complications in patients with colonic neoplasia undergoing colectomy with good performance. Although validation using external data and optimization of data quality will be required, these machine learning tools show great promise in assisting surgeons with risk-stratification of perioperative care to improve postoperative outcomes. See Video Abstract . PREDICCIN DE LAS COMPLICACIONES QUIRRGICAS DE LA NEOPLASIA DE COLON UN ENFOQUE DE MODELO DE APRENDIZAJE AUTOMTICO ANTECEDENTES:Se han utilizado una variedad de enfoques estadísticos para ayudar a predecir los resultados asociados con la colectomía. La naturaleza multifactorial de las complicaciones sugiere que los algoritmos de aprendizaje automático pueden ser más precisos en determinar los resultados posoperatorios al detectar asociaciones no lineales, que generalmente no se miden en las estadísticas tradicionales.OBJETIVO:El objetivo de este estudio fue investigar la utilidad de los algoritmos de aprendizaje automático para predecir complicaciones en pacientes sometidos a colectomía por neoplasia de colon.DISEÑO:Análisis retrospectivo utilizando clasificadores de árboles de decisión, bosques aleatorios y redes neuronales artificiales para predecir los resultados posoperatorios.AJUSTE:Base de datos de la Muestra Nacional de Pacientes Hospitalizados (2003-2017).PACIENTES:Pacientes adultos sometidos a colectomía electiva con anastomosis por neoplasia.INTERVENCIONES:N/A.PRINCIPALES MEDIDAS DE RESULTADO:El rendimiento se cuantificó utilizando la sensibilidad, especificidad, precisión y la característica operativa del receptor del área bajo la curva para predecir la incidencia de fuga anastomótica, duración prolongada de la estancia hospitalaria y mortalidad de los pacientes hospitalizados.RESULTADOS:Se incluyeron un total de 14.935 pacientes (4.731 laparoscópicos, 10.204 abiertos). Presentaron una edad promedio de 67 ± 12,2 años y el 53% eran mujeres. Los tres modelos de aprendizaje automático identificaron con éxito a los pacientes que desarrollaron las complicaciones medidas. Aunque las diferencias entre el rendimiento del modelo fueron en gran medida insignificantes, la red neuronal obtuvo la puntuación más alta para la mayoría de los resultados: predicción de fuga anastomótica, característica operativa del receptor del área bajo la curva 0,88/0,93 (abierta/laparoscópica, IC del 95%: 0,73-0,92/0,80-0,96); duración prolongada de la estancia hospitalaria, característica operativa del receptor del área bajo la curva 0,84/0,88 (abierta/laparoscópica, IC del 95%: 0,82-0,85/0,85-0,91); y mortalidad de pacientes hospitalizados, característica operativa del receptor del área bajo la curva 0,90/0,92 (abierto/laparoscópico, IC del 95%: 0,85-0,96/0,86-0,98).LIMITACIONES:Los pacientes de la base de datos de la Muestra Nacional de Pacientes Hospitalizados pueden no ser una muestra precisa de la población de todos los pacientes sometidos a colectomía por neoplasia de colon y no tienen en cuenta factores institucionales y específicos del paciente.CONCLUSIONES:El aprendizaje automático predijo con buen rendimiento las complicaciones postoperatorias en pacientes con neoplasia de colon sometidos a colectomía. Aunque será necesaria la validación mediante datos externos y la optimización de la calidad de los datos, estas herramientas de aprendizaje automático son muy prometedoras para ayudar a los cirujanos con la estratificación de riesgos de la atención perioperatoria para mejorar los resultados posoperatorios. (Traducción-Dr. Fidel Ruiz Healy ).
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Affiliation(s)
- Chibueze A Nwaiwu
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Krissia M Rivera Perla
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Logan B Abel
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Isaac J Sears
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Andrew T Barton
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Yao Z Liu
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Ishaani S Khatri
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Indra N Sarkar
- Center for Biomedical Informatics, Brown University, Providence, Rhode Island
- Rhode Island Quality Institute, Providence, Rhode Island
| | - Nishit Shah
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
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Tan HJ, Spratte BN, Deal AM, Heiling HM, Nazzal EM, Meeks W, Fang R, Teal R, Vu MB, Bennett AV, Blalock SJ, Chung AE, Gotz D, Nielsen ME, Reuland DS, Harris AH, Basch E. Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists. Urology 2024:S0090-4295(24)00307-8. [PMID: 38697362 DOI: 10.1016/j.urology.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/08/2024] [Accepted: 04/20/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVE To assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited. METHODS A sequential explanatory mixed methods study from 2019 to 2020 was performed. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. RESULTS Among a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. CONCLUSION Urologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.
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Affiliation(s)
- Hung-Jui Tan
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC.
| | - Brooke N Spratte
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Allison M Deal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Hillary M Heiling
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Elizabeth M Nazzal
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - William Meeks
- American Urological Association Data Management and Statistical Services
| | - Raymond Fang
- American Urological Association Data Management and Statistical Services
| | - Randall Teal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC
| | - Maihan B Vu
- Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC; Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill, NC
| | - Antonia V Bennett
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Susan J Blalock
- Pharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC
| | - Arlene E Chung
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Bioinformatics, Duke University, Durham, NC
| | - David Gotz
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; School of Information and Library Science, University of North Carolina, Chapel Hill, NC
| | - Matthew E Nielsen
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Daniel S Reuland
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Alex Hs Harris
- Department of Surgery, School of Medicine, Stanford University, Palo Alto, CA
| | - Ethan Basch
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC
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20
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Borna S, Barry BA, Makarova S, Parte Y, Haider CR, Sehgal A, Leibovich BC, Forte AJ. Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review. Eur J Investig Health Psychol Educ 2024; 14:1182-1196. [PMID: 38785576 PMCID: PMC11119077 DOI: 10.3390/ejihpe14050078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/11/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.
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Affiliation(s)
- Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Barbara A. Barry
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Yogesh Parte
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| | - Clifton R. Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Ajai Sehgal
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley C. Leibovich
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
- Department of Urology, Mayo Clinic, Rochester, MN 55905, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
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21
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Hennocq Q, Garcelon N, Bongibault T, Bouygues T, Marlin S, Amiel J, Boutaud L, Douillet M, Lyonnet S, Pingault V, Picard A, Rio M, Attie-Bitach T, Khonsari RH, Roux N. Artificial intelligence-based diagnosis in fetal pathology using external ear shapes. Prenat Diagn 2024. [PMID: 38635411 DOI: 10.1002/pd.6577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 03/28/2024] [Accepted: 04/07/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVE Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls. METHOD We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes. RESULTS The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. CONCLUSION We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | | | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Thomas Bouygues
- Imagine Institute, INSERM UMR1163, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Sandrine Marlin
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Lucile Boutaud
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | | | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Vèronique Pingault
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Arnaud Picard
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Marlèe Rio
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Tania Attie-Bitach
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Roman H Khonsari
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Nathalie Roux
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
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Pressman SM, Borna S, Gomez-Cabello CA, Haider SA, Haider C, Forte AJ. AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research. Healthcare (Basel) 2024; 12:825. [PMID: 38667587 PMCID: PMC11050155 DOI: 10.3390/healthcare12080825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/02/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
INTRODUCTION As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice. METHODS A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected. RESULTS The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility. CONCLUSION The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety.
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Affiliation(s)
| | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Syed A. Haider
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Clifton Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Antonio J. Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
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23
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Chen J, Li M, Han H, Zhao Z, Chen X. SurgNet: Self-Supervised Pretraining With Semantic Consistency for Vessel and Instrument Segmentation in Surgical Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1513-1525. [PMID: 38090838 DOI: 10.1109/tmi.2023.3341948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (RAG) based on local semantic consistency in unlabeled surgical images and use it as a self-supervision signal for pseudo-mask segmentation. We then use the pseudo-mask to perform guided masked image modeling (GMIM) to learn representations that integrate structural information of intraoperative objectives more effectively. Our pretrained model, paired with various segmentation methods, can be applied to perform vessel and instrument segmentation accurately using limited labeled data for fine-tuning. We build an Intraoperative Vessel and Instrument Segmentation (IVIS) dataset, comprised of ~3 million unlabeled images and over 4,000 labeled images with manual vessel and instrument annotations to evaluate the effectiveness of our self-supervised pretraining method. We also evaluated the generalizability of our method to similar tasks using two public datasets. The results demonstrate that our approach outperforms the current state-of-the-art (SOTA) self-supervised representation learning methods in various surgical image segmentation tasks.
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24
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Zheng H, Yang L, Hu J, Yang Y. Behaviour, barriers and facilitators of shared decision making in breast cancer surgical treatment: A qualitative systematic review using a 'Best Fit' framework approach. Health Expect 2024; 27:e14019. [PMID: 38558230 PMCID: PMC10982676 DOI: 10.1111/hex.14019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Due to the diversity and high sensitivity of the treatment, there were difficulties and uncertainties in the breast cancer surgical decision-making process. We aimed to describe the patient's decision-making behaviour and shared decision-making (SDM)-related barriers and facilitators in breast cancer surgical treatment. METHODS We searched eight databases for qualitative studies and mixed-method studies about breast cancer patients' surgical decision-making process from inception to March 2021. The quality of the studies was critically appraised by two researchers independently. We used a 'best fit framework approach' to analyze and synthesize the evidence. RESULTS Twenty-eight qualitative studies and three mixed-method studies were included in this study. Four themes and 10 subthemes were extracted: (a) struggling with various considerations, (b) actual decision-making behaviours, (c) SDM not routinely implemented and (d) multiple facilitators and barriers to SDM. CONCLUSIONS Patients had various considerations of breast surgery and SDM was not routinely implemented. There was a discrepancy between information exchange behaviours, value clarification, decision support utilization and SDM due to cognitive and behavioural biases. When individuals made surgical decisions, their behaviours were affected by individual-level and system-level factors. Therefore, healthcare providers and other stakeholders should constantly improve communication skills and collaboration, and emphasize the importance of decision support, so as to embed SDM into routine practice. PATIENT AND PUBLIC CONTRIBUTION This systematic review was conducted as part of a wider research entitled: Breast cancer patients' actual participation roles in surgical decision making: a mixed method research. The results of this project helped us to better analyze and generalize patients' views.
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Affiliation(s)
- Hongying Zheng
- School of Nursing, School of MedicineShanghai Jiao Tong UniversityShanghaiChina
| | - Linning Yang
- School of Nursing, School of MedicineShanghai Jiao Tong UniversityShanghaiChina
| | - Jiale Hu
- Department of Nurse Anesthesia, College of Health ProfessionsVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Yan Yang
- Department of Nursing, Renji HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
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25
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Rogers MP, Janjua HM, Walczak S, Baker M, Read M, Cios K, Velanovich V, Pietrobon R, Kuo PC. Artificial Intelligence in Surgical Research: Accomplishments and Future Directions. Am J Surg 2024; 230:82-90. [PMID: 37981516 DOI: 10.1016/j.amjsurg.2023.10.045] [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: 08/26/2023] [Accepted: 10/22/2023] [Indexed: 11/21/2023]
Abstract
MINI-ABSTRACT The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data. OBJECTIVE Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice. SUMMARY BACKGROUND DATA The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies. METHODS The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm. RESULTS AND CONCLUSIONS The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.
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Affiliation(s)
- Michael P Rogers
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Haroon M Janjua
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Steven Walczak
- School of Information & Florida Center for Cybersecurity, University of South Florida, Tampa, FL, USA
| | - Marshall Baker
- Department of Surgery, Loyola University Medical Center, Maywood, IL, USA
| | - Meagan Read
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Konrad Cios
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Vic Velanovich
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | | | - Paul C Kuo
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
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26
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Ferreres AR. Ethical and legal issues regarding artificial intelligence (AI) and management of surgical data. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108279. [PMID: 38555230 DOI: 10.1016/j.ejso.2024.108279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 03/05/2024] [Accepted: 03/17/2024] [Indexed: 04/02/2024]
Abstract
The advent of AI in surgical practice is representing a major innovation. As its role expands and due to its several implications, strict compliance with ethical, legal and regulatory good practices is mandatory. Observance of ethical principles and legal rules will be a professional imperative for the application of AI in surgical practice, both clinically and scientifically.
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Affiliation(s)
- Alberto R Ferreres
- University of Buenos Aires, Buenos Aires, Argentina; University of Washington, Seattle, USA.
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27
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Gao L, Xing B. Bone cement reinforcement improves the therapeutic effects of screws in elderly patients with pelvic fragility factures. J Orthop Surg Res 2024; 19:191. [PMID: 38500199 PMCID: PMC10949620 DOI: 10.1186/s13018-024-04666-3] [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: 01/23/2024] [Accepted: 03/06/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Pelvic fragility fractures in elderly individuals present significant challenges in orthopedic and geriatric medicine due to reduced bone density and increased frailty associated with aging. METHODS This study involved 150 elderly patients with pelvic fragility fractures. The patients were divided into two groups, the observation group (Observation) and the control group (Control), using a random number table. Artificial intelligence, specifically the Tianji Orthopedic Robot, was employed for surgical assistance. The observation group received bone cement reinforcement along with screw fixation using the robotic system, while the control group received conventional screw fixation alone. Follow-up data were collected for one-year post-treatment. RESULTS The observation group exhibited significantly lower clinical healing time of fractures and reduced bed rest time compared to the control group. Additionally, the observation group experienced less postoperative pain at 1 and 3 months, indicating the benefits of bone cement reinforcement. Moreover, patients in the observation group demonstrated significantly better functional recovery at 1-, 3-, and 6-months post-surgery compared to the control group. CONCLUSION The combination of bone cement reinforcement and robotic technology resulted in accelerated fracture healing, reduced bed rest time, and improved postoperative pain relief and functional recovery.
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Affiliation(s)
- Lecai Gao
- Department of Orthopaedic Surgery, Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, Hebei, 061000, China
| | - Baorui Xing
- Department of Orthopaedic Surgery, Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, Hebei, 061000, China.
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28
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Kovoor JG, Bacchi S, Sharma P, Sharma S, Kumawat M, Stretton B, Gupta AK, Chan W, Abou-Hamden A, Maddern GJ. Artificial intelligence for surgical services in Australia and New Zealand: opportunities, challenges and recommendations. Med J Aust 2024; 220:234-237. [PMID: 38321813 DOI: 10.5694/mja2.52225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Affiliation(s)
- Joshua G Kovoor
- University of Adelaide, Adelaide, SA
- Ballarat Base Hospital, Ballarat, VIC
| | | | | | | | | | | | | | - WengOnn Chan
- University of Adelaide, Adelaide, SA
- Queen Elizabeth Hospital, Adelaide, SA
| | - Amal Abou-Hamden
- University of Adelaide, Adelaide, SA
- Royal Adelaide Hospital, Adelaide, SA
| | - Guy J Maddern
- University of Adelaide, Adelaide, SA
- Queen Elizabeth Hospital, Adelaide, SA
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29
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Dhawan R, Brooks KD. Limitations of Artificial Intelligence in Plastic Surgery. Aesthet Surg J 2024; 44:NP323-NP324. [PMID: 38015802 PMCID: PMC10942797 DOI: 10.1093/asj/sjad357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 11/30/2023] Open
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30
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Goh WW, Chia KY, Cheung MF, Kee KM, Lwin MO, Schulz PJ, Chen M, Wu K, Ng SS, Lui R, Ang TL, Yeoh KG, Chiu HM, Wu DC, Sung JJ. Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study. JMIR AI 2024; 3:e50525. [PMID: 38875591 PMCID: PMC11041476 DOI: 10.2196/50525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. OBJECTIVE This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. METHODS We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). RESULTS In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. CONCLUSIONS Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.
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Affiliation(s)
- Wilson Wb Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Kendrick Ya Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Max Fk Cheung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
| | - Kalya M Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Peter J Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Minhu Chen
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kaichun Wu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Simon Sm Ng
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Rashid Lui
- Prince of Wales Hospital, Hospital Authority, Hong Kong, China (Hong Kong)
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, SingHealth, Singapore, Singapore
| | - Khay Guan Yeoh
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taiwan, China
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taiwan, China
| | | | - Joseph Jy Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
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31
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Moazzam Z, Alaimo L, Endo Y, Lima HA, Woldesenbet S, Rueda BO, Yang J, Ratti F, Marques HP, Cauchy F, Lam V, Poultsides GA, Popescu I, Alexandrescu S, Martel G, Guglielmi A, Hugh T, Aldrighetti L, Shen F, Endo I, Pawlik TM. A Prognostic Model To Predict Survival After Recurrence Among Patients With Recurrent Hepatocellular Carcinoma. Ann Surg 2024; 279:471-478. [PMID: 37522251 DOI: 10.1097/sla.0000000000006056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
OBJECTIVE We sought to develop and validate a preoperative model to predict survival after recurrence (SAR) in hepatocellular carcinoma (HCC). BACKGROUND Although HCC is characterized by recurrence as high as 60%, models to predict outcomes after recurrence remain relatively unexplored. METHODS Patients who developed recurrent HCC between 2000 and 2020 were identified from an international multi-institutional database. Clinicopathologic data on primary disease and laboratory and radiologic imaging data on recurrent disease were collected. Multivariable Cox regression analysis and internal bootstrap validation (5000 repetitions) were used to develop and validate the SARScore. Optimal Survival Tree analysis was used to characterize SAR among patients treated with various treatment modalities. RESULTS Among 497 patients who developed recurrent HCC, median SAR was 41.2 months (95% CI 38.1-52.0). The presence of cirrhosis, number of primary tumors, primary macrovascular invasion, primary R1 resection margin, AFP>400 ng/mL on the diagnosis of recurrent disease, radiologic extrahepatic recurrence, radiologic size and number of recurrent lesions, radiologic recurrent bilobar disease, and early recurrence (≤24 months) were included in the model. The SARScore successfully stratified 1-, 3- and 5-year SAR and demonstrated strong discriminatory ability (3-year AUC: 0.75, 95% CI 0.70-0.79). While a subset of patients benefitted from resection/ablation, Optimal Survival Tree analysis revealed that patients with high SARScore disease had the worst outcomes (5-year AUC; training: 0.79 vs. testing: 0.71). The SARScore model was made available online for ease of use and clinical applicability ( https://yutaka-endo.shinyapps.io/SARScore/ ). CONCLUSION The SARScore demonstrated strong discriminatory ability and may be a clinically useful tool to help stratify risk and guide treatment for patients with recurrent HCC.
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Affiliation(s)
- Zorays Moazzam
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Laura Alaimo
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
- Department of Surgery, University of Verona, Verona, Italy
| | - Yutaka Endo
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Henrique A Lima
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Selamawit Woldesenbet
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Belisario Ortiz Rueda
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Jason Yang
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - Francois Cauchy
- Department of Hepatobiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, NSW, Australia
| | | | - Irinel Popescu
- Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
| | | | | | | | - Tom Hugh
- Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia
| | | | - Feng Shen
- The Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Itaru Endo
- Yokohama City University School of Medicine, Yokohama, Japan
| | - Timothy M Pawlik
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
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Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
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Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
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Stark M, Mynbaev O, Malvasi A, Tinelli A. Revolutionizing patient care: the harmonious blend of artificial intelligence and surgical tradition. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2024; 17:47-50. [PMID: 38455508 PMCID: PMC10915289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 01/23/2024] [Indexed: 03/09/2024]
Abstract
Surgery has undergone remarkable evolution over the past decades, propelled by unprecedented technological advancement. Despite these changes, the role of surgeons and their irreplaceable qualities remains pivotal. This article delves into the intersection of surgery and artificial intelligence (AI), underscoring the enduring significance of human expertise and values. The potential of AI to learn and improve over time holds great promise for enhancing various facets of surgery, including diagnostics, personalized treatment, preoperative planning, real-time support in the operating room, and comprehensive postoperative analytics of the outcome. However, it is essential to emphasize the continued importance of the surgeon's role to uphold universal surgical principles. This includes a commitment to minimalism and the use of evidence-based practice, ensuring optimal outcomes and standardized procedures. By recognizing the synergies between AI and traditional surgical approaches, we can navigate the evolving landscape of surgery to achieve the highest standards of patient care.
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Affiliation(s)
- Michael Stark
- The New European Surgical Academy10117 Berlin, Germany
| | - Ospan Mynbaev
- The New European Surgical Academy10117 Berlin, Germany
| | - Antonio Malvasi
- The New European Surgical Academy10117 Berlin, Germany
- Department of Biomedical Sciences and Human Oncology, University of Bari70121 Bari, Italy
| | - Andrea Tinelli
- The New European Surgical Academy10117 Berlin, Germany
- Department of Obstetrics and Gynecology and CERICSAL (CEntro di RIcerca Clinico SALentino), Veris delli Ponti HospitalScorrano, 73020 Lecce, Italy
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Abbaker N, Minervini F, Guttadauro A, Solli P, Cioffi U, Scarci M. The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review. Front Oncol 2024; 14:1347464. [PMID: 38414748 PMCID: PMC10897973 DOI: 10.3389/fonc.2024.1347464] [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: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024] Open
Abstract
Objectives To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. Methods A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. Conclusion While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.
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Affiliation(s)
- Namariq Abbaker
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
| | - Fabrizio Minervini
- Division of Thoracic Surgery, Luzerner Kantonsspital, Lucern, Switzerland
| | - Angelo Guttadauro
- Division of Surgery, Università Milano-Bicocca and Istituti Clinici Zucchi, Monza, Italy
| | - Piergiorgio Solli
- Division of Thoracic Surgery, Policlinico S. Orsola-Malpighi, Bologna, Italy
| | - Ugo Cioffi
- Department of Surgery, University of Milan, Milan, Italy
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
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Aedo-Martín D. [Translated article] Artificial intelligence: Future and challenges in modern medicine. Rev Esp Cir Ortop Traumatol (Engl Ed) 2024:S1888-4415(24)00047-X. [PMID: 38325569 DOI: 10.1016/j.recot.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 02/09/2024] Open
Affiliation(s)
- D Aedo-Martín
- Servicio de Cirugía Ortopédica y Traumatología, Hospital Universitario del Henares, Madrid, Spain; Unidad de Medicina Deportiva y Traumatología, Hospital Vithas Internacional, Madrid, Spain; Unidad de Mano, Invictum Medical Sports Center, Madrid, Spain.
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Tsai AY, Carter SR, Greene AC. Artificial intelligence in pediatric surgery. Semin Pediatr Surg 2024; 33:151390. [PMID: 38242061 DOI: 10.1016/j.sempedsurg.2024.151390] [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] [Indexed: 01/21/2024]
Abstract
Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.
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Affiliation(s)
- Anthony Y Tsai
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States.
| | - Stewart R Carter
- Division of Pediatric Surgery, University of Louisville School of Medicine, Louisville, KY, United States
| | - Alicia C Greene
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States
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Ali JT, Yang G, Green CA, Reed BL, Madani A, Ponsky TA, Hazey J, Rothenberg SS, Schlachta CM, Oleynikov D, Szoka N. Defining digital surgery: a SAGES white paper. Surg Endosc 2024; 38:475-487. [PMID: 38180541 DOI: 10.1007/s00464-023-10551-7] [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: 10/01/2023] [Accepted: 10/17/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Digital surgery is a new paradigm within the surgical innovation space that is rapidly advancing and encompasses multiple areas. METHODS This white paper from the SAGES Digital Surgery Working Group outlines the scope of digital surgery, defines key terms, and analyzes the challenges and opportunities surrounding this disruptive technology. RESULTS In its simplest form, digital surgery inserts a computer interface between surgeon and patient. We divide the digital surgery space into the following elements: advanced visualization, enhanced instrumentation, data capture, data analytics with artificial intelligence/machine learning, connectivity via telepresence, and robotic surgical platforms. We will define each area, describe specific terminology, review current advances as well as discuss limitations and opportunities for future growth. CONCLUSION Digital Surgery will continue to evolve and has great potential to bring value to all levels of the healthcare system. The surgical community has an essential role in understanding, developing, and guiding this emerging field.
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Affiliation(s)
- Jawad T Ali
- University of Texas at Austin, Austin, TX, USA
| | - Gene Yang
- University at Buffalo, Buffalo, NY, USA
| | | | | | - Amin Madani
- University of Toronto, Toronto, ON, Canada
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
| | - Todd A Ponsky
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | | | | | - Dmitry Oleynikov
- Monmouth Medical Center, Robert Wood Johnson Barnabas Health, Rutgers School of Medicine, Long Branch, NJ, USA
| | - Nova Szoka
- Department of Surgery, West Virginia University, Suite 7500 HSS, PO Box 9238, Morgantown, WV, 26506-9238, USA.
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Hennocq Q, Willems M, Amiel J, Arpin S, Attie-Bitach T, Bongibault T, Bouygues T, Cormier-Daire V, Corre P, Dieterich K, Douillet M, Feydy J, Galliani E, Giuliano F, Lyonnet S, Picard A, Porntaveetus T, Rio M, Rouxel F, Shotelersuk V, Toutain A, Yauy K, Geneviève D, Khonsari RH, Garcelon N. Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome. Sci Rep 2024; 14:2330. [PMID: 38282012 PMCID: PMC10822856 DOI: 10.1038/s41598-024-52691-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024] Open
Abstract
The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, 75015, Paris, France.
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France.
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France.
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France.
- Hôpital Necker-Enfants Malades, 149 rue de Sèvres, 75015, Paris, France.
| | - Marjolaine Willems
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Stéphanie Arpin
- Service de Génétique, CHU Tours, UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
| | - Tania Attie-Bitach
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Thomas Bouygues
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Valérie Cormier-Daire
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Pierre Corre
- Nantes Université, CHU Nantes, Service de chirurgie maxillo-faciale et stomatologie, 44000, Nantes, France
- Nantes Université, Oniris, UnivAngers, CHU Nantes, INSERM, Regenerative Medicine and Skeleton, RMeS, UMR 1229, 44000, Nantes, France
| | - Klaus Dieterich
- Univ. Grenoble Alpes, Inserm, U1209, IAB, CHU Grenoble Alpes, 38000, Grenoble, France
| | | | | | - Eva Galliani
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | | | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Arnaud Picard
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Thantrira Porntaveetus
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Marlène Rio
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Flavien Rouxel
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Annick Toutain
- Service de Génétique, CHU Tours, UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
| | - Kevin Yauy
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - David Geneviève
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - Roman H Khonsari
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
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Schonfeld E, Pant A, Shah A, Sadeghzadeh S, Pangal D, Rodrigues A, Yoo K, Marianayagam N, Haider G, Veeravagu A. Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery. J Clin Med 2024; 13:656. [PMID: 38337352 PMCID: PMC10856542 DOI: 10.3390/jcm13030656] [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: 12/19/2023] [Revised: 01/10/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p < 10-5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.
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Affiliation(s)
- Ethan Schonfeld
- Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (A.P.); (S.S.)
| | - Aaradhya Pant
- Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (A.P.); (S.S.)
| | - Aaryan Shah
- Department of Computer Science, Stanford University, Stanford, CA 94304, USA;
| | - Sina Sadeghzadeh
- Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (A.P.); (S.S.)
| | - Dhiraj Pangal
- Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (D.P.); (K.Y.); (N.M.); (G.H.); (A.V.)
| | - Adrian Rodrigues
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Kelly Yoo
- Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (D.P.); (K.Y.); (N.M.); (G.H.); (A.V.)
| | - Neelan Marianayagam
- Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (D.P.); (K.Y.); (N.M.); (G.H.); (A.V.)
| | - Ghani Haider
- Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (D.P.); (K.Y.); (N.M.); (G.H.); (A.V.)
| | - Anand Veeravagu
- Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA; (D.P.); (K.Y.); (N.M.); (G.H.); (A.V.)
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Patel V, Saikali S, Moschovas MC, Patel E, Satava R, Dasgupta P, Dohler M, Collins JW, Albala D, Marescaux J. Technical and ethical considerations in telesurgery. J Robot Surg 2024; 18:40. [PMID: 38231309 DOI: 10.1007/s11701-023-01797-3] [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: 10/12/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024]
Abstract
Telesurgery, a cutting-edge field at the intersection of medicine and technology, holds immense promise for enhancing surgical capabilities, extending medical care, and improving patient outcomes. In this scenario, this article explores the landscape of technical and ethical considerations that highlight the advancement and adoption of telesurgery. Network considerations are crucial for ensuring seamless and low-latency communication between remote surgeons and robotic systems, while technical challenges encompass system reliability, latency reduction, and the integration of emerging technologies like artificial intelligence and 5G networks. Therefore, this article also explores the critical role of network infrastructure, highlighting the necessity for low-latency, high-bandwidth, secure and private connections to ensure patient safety and surgical precision. Moreover, ethical considerations in telesurgery include patient consent, data security, and the potential for remote surgical interventions to distance surgeons from their patients. Legal and regulatory frameworks require refinement to accommodate the unique aspects of telesurgery, including liability, licensure, and reimbursement. Our article presents a comprehensive analysis of the current state of telesurgery technology and its potential while critically examining the challenges that must be navigated for its widespread adoption.
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Affiliation(s)
- Vipul Patel
- AdventHealth Global Robotics Institute, Celebration, FL, USA
- University of Central Florida (UCF), Orlando, FL, USA
| | - Shady Saikali
- AdventHealth Global Robotics Institute, Celebration, FL, USA.
| | - Marcio Covas Moschovas
- AdventHealth Global Robotics Institute, Celebration, FL, USA
- University of Central Florida (UCF), Orlando, FL, USA
| | - Ela Patel
- Stanford University, Stanford, CA, 94305, USA
| | | | - Prokar Dasgupta
- MRC Centre for Transplantation, Department of Urology, King's Health Partners, King's College London, London, UK
| | - Mischa Dohler
- Advanced Technology Group, Ericsson Inc., Santa Clara, CA, 95054, USA
| | - Justin W Collins
- Division of Uro-Oncology, University College London Hospital, London, UK
- Division of Surgery and Interventional Science, Research Department of Targeted Intervention, University College London, London, UK
- CMR Surgical, Cambridge, UK
| | - David Albala
- Downstate Health Sciences University, Syracuse, NY, USA
- Department of Urology, Crouse Hospital, Syracuse, NY, USA
| | - Jacques Marescaux
- IRCAD, Research Institute Against Digestive Cancer, Strasbourg, France
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Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
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Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
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TerKonda SP, TerKonda AA, Sacks JM, Kinney BM, Gurtner GC, Nachbar JM, Reddy SK, Jeffers LL. Artificial Intelligence: Singularity Approaches. Plast Reconstr Surg 2024; 153:204e-217e. [PMID: 37075274 DOI: 10.1097/prs.0000000000010572] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
SUMMARY Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.
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Affiliation(s)
- Sarvam P TerKonda
- From the Division of Plastic and Reconstructive Surgery, Mayo Clinic Florida
| | - Anurag A TerKonda
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Justin M Sacks
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Brian M Kinney
- Division of Plastic Surgery, University of Southern California
| | - Geoff C Gurtner
- Division of Plastic and Reconstructive Surgery, Stanford University
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Singam A. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia. Cureus 2023; 15:e49887. [PMID: 38174199 PMCID: PMC10762564 DOI: 10.7759/cureus.49887] [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: 11/24/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024] Open
Abstract
This review explores the intersection of artificial intelligence (AI) and anesthesia, examining its transformative impact on patient care across various phases. Beginning with a historical overview of anesthesia, we highlight the critical role of technological advancements in ensuring optimal patient outcomes. The emergence of AI in healthcare sets the stage for a comprehensive analysis of its applications in anesthesia. In the preoperative phase, AI facilitates personalized risk assessments and decision support, optimizing anesthesia planning and drug dosage predictions. Moving to the intraoperative phase, we delve into AI's role in monitoring and control through sophisticated anesthesia monitoring and closed-loop systems. Additionally, we discuss the integration of robotics and AI-guided procedures, revolutionizing surgical assistance. Transitioning to the postoperative phase, we explore AI-driven postoperative monitoring, predictive analysis for complications, and the integration of AI into rehabilitation programs and long-term follow-up. These new applications redefine patient recovery, emphasizing personalized care and proactive interventions. However, the integration of AI in anesthesia poses challenges and ethical considerations. Data security, interpretability, and bias in AI algorithms demand scrutiny. Moreover, the evolving patient-doctor relationship in an AI-driven care landscape requires a delicate balance between efficiency and human touch. Looking forward, we discuss the future directions of AI in anesthesia, anticipating advances in technology and AI algorithms. The integration of AI into routine clinical practice and its potential impact on anesthesia education and training are explored, emphasizing the need for collaboration, education, and ethical guidelines. This review provides a comprehensive overview of AI applications in anesthesia, offering insights into the present landscape, challenges, and future directions. The synthesis of historical perspectives, current applications, and future possibilities underscores the transformative potential of AI in revolutionizing patient care within the dynamic field of anesthesia.
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Affiliation(s)
- Amol Singam
- Critical Care Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Benhenneda R, Brouard T, Dordain F, Gadéa F, Charousset C, Berhouet J. Can artificial intelligence help decision-making in arthroscopy? Part 1: Use of a standardized analysis protocol improves inter-observer agreement of arthroscopic diagnostic assessments of the long head of biceps tendon in small rotator cuff tears. Orthop Traumatol Surg Res 2023; 109:103648. [PMID: 37356800 DOI: 10.1016/j.otsr.2023.103648] [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: 03/17/2023] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 06/27/2023]
Abstract
INTRODUCTION Injuries of the long head of biceps (LHB) tendon are common but difficult to diagnose clinically or using imaging. Arthroscopy is the preferred means of diagnostic assessment of the LHB, but it often proves challenging. Its reliability and reproducibility have not yet been assessed. Artificial intelligence (AI) could assist in the arthroscopic analysis of the LHB. The main objective of this study was to evaluate the inter-observer agreement for the specific LHB assessment, according to an analysis protocol based on images of interest. The secondary objective was to define a video database, called "ground truth", intended to create and train AI for the LHB assessment. HYPOTHESIS The hypothesis was that the inter-observer agreement analysis, on standardized images, was strong enough to allow the "ground truth" videos to be used as an input database for an AI solution to be used in making arthroscopic LHB diagnoses. MATERIALS AND METHOD One hundred and ninety-nine sets of standardized arthroscopic images of LHB exploration were evaluated by 3 independent observers. Each had to characterize the healthy or pathological state of the tendon, specifying the type of lesion: partial tear, hourglass hypertrophy, instability, fissure, superior labral anterior posterior lesion (SLAP 2), chondral print and pathological pulley without instability. Inter-observer agreement levels were measured using Cohen's Kappa (K) coefficient and Kappa Accuracy. RESULTS The strength of agreement was moderate to strong according to the observers (Kappa 0.54 to 0.7 and KappaAcc from 86 to 92%), when determining the healthy or pathological state of the LHB. When the tendon was pathological, the strength of agreement was moderate to strong when it came to a partial tear (Kappa 0.49 to 0.71 and KappaAcc from 85 to 92%), fissure (Kappa -0.5 to 0.7 and KappaAcc from 36 to 93%) or a SLAP tear (0.54 to 0.88 and KappaAcc from 90 to 97%). It was low for unstable lesion (Kappa 0.04 to 0.25 and KappaAcc from 36 to 88%). CONCLUSION The analysis of the LHB, from arthroscopic images, had a high level of agreement for the diagnosis of its healthy or pathological nature. However, the agreement rate decreased for the diagnosis of rare or dynamic tendon lesions. Thus, AI engineered from human analysis would have the same difficulties if it was limited only to an arthroscopic analysis. The integration of clinical and paraclinical data is necessary to improve the arthroscopic diagnosis of LHB injuries. It also seems to be an essential prerequisite for making a so-called "ground truth" database for building a high-performance AI solution. LEVEL OF EVIDENCE III; inter-observer prospective series.
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Affiliation(s)
- Rayane Benhenneda
- Service de Chirurgie Orthopédique, Hôpital Trousseau, Faculté de Médecine, Université de Tours Centre-Val de Loire, CHRU de Tours, Tours, France.
| | - Thierry Brouard
- LIFAT (EA6300), École Polytechnique Universitaire de Tours, 64, avenue Jean-Portalis, 37200 Tours, France
| | - Franck Dordain
- Hôpital Privé Saint-Martin, 18, rue des Roquemonts, 14000 Caen, France
| | - François Gadéa
- Centre Ortho-Globe, place du Globe, 83000 Toulon, France
| | | | - Julien Berhouet
- Service de Chirurgie Orthopédique, Hôpital Trousseau, Faculté de Médecine, Université de Tours Centre-Val de Loire, CHRU de Tours, Tours, France; LIFAT (EA6300), École Polytechnique Universitaire de Tours, 64, avenue Jean-Portalis, 37200 Tours, France
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Rogers MP, Janjua H, Read M, Pietrobon R, Kuo PC. Interpretable machine learning accurately reclassifies lobectomy surgical approaches by cost. Surgery 2023; 174:1422-1427. [PMID: 37833152 DOI: 10.1016/j.surg.2023.09.001] [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: 03/03/2023] [Revised: 06/27/2023] [Accepted: 09/05/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND The volume of robotic lung resection continues to increase despite its higher costs and unproven superiority to video-assisted thoracoscopic surgery. We evaluated whether machine learning can accurately identify factors influencing cost and reclassify high-cost operative approaches into lower-cost alternatives. METHODS The Florida Agency for Healthcare Administration and Centers for Medicare and Medicaid Services Hospital and Physician Compare datasets were queried for patients undergoing open, video-assisted thoracoscopic surgery and robotic lobectomy. K-means cluster analysis was used to identify robotic clusters based on total cost. Predictive models were built using artificial neural networks, Support Vector Machines, Classification and Regression Trees, and Gradient Boosted Machines algorithms. Models were applied to the high-volume robotic group to determine patients whose cost cluster changed if undergoing a video-assisted thoracoscopic surgery approach. A local interpretable model-agnostic explanation approach personalized cost per patient. RESULTS Of the 6,618 cases included in the analysis, we identified 4 cost clusters. Application of artificial neural networks to the robotic subgroup identified 1,642 (65%) cases with no re-assignment of cost cluster, 583 (23%) with reduced costs, and 300 (12%) with increased costs if they had undergone video-assisted thoracoscopic surgery approach. The 5 overall highest cost predictors were patient admission from the clinic, diagnosis of metastatic cancer, presence of cancer, urgent hospital admission, and dementia. CONCLUSION K-means cluster analysis and machine learning identify a patient population that may undergo video-assisted thoracoscopic surgery or robotic lobectomy without a significant difference in total cost. Local interpretable model-agnostic explanation identifies individual patient factors contributing to cost. Application of this modeling may reliably stratify high-cost patients into lower-cost approaches and provide a rationale for reducing expenditure.
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Affiliation(s)
- Michael P Rogers
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | - Haroon Janjua
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | - Meagan Read
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | | | - Paul C Kuo
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL.
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Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, Chen Y, Wang Y, Shi N. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart 2023; 10:e002432. [PMID: 38016787 PMCID: PMC10685930 DOI: 10.1136/openhrt-2023-002432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023] Open
Abstract
Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yijiu Yang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xingyu Zong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Nannan Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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Schulz PJ, Lwin MO, Kee KM, Goh WWB, Lam TYT, Sung JJY. Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice. Front Public Health 2023; 11:1301563. [PMID: 38089040 PMCID: PMC10715310 DOI: 10.3389/fpubh.2023.1301563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice. Methods We utilized online surveys to gather data from clinicians in the field of gastroenterology. Results A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians. Discussion The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.
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Affiliation(s)
- Peter J. Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O. Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Kalya M. Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Wilson W. B. Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Thomas Y. T Lam
- Faculty of Medicine, Institute of Digestive Diseases, The Chinese University of Hong Kong, Hong Kong, China
| | - Joseph J. Y. Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Qi C, Hu L, Zhang C, Wang K, Qiu B, Yi J, Shen Y. Role of surgery in T4N0-3M0 esophageal cancer. World J Surg Oncol 2023; 21:369. [PMID: 38008742 PMCID: PMC10680323 DOI: 10.1186/s12957-023-03239-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/13/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND This study aimed to investigate an unsettled issue that whether T4 esophageal cancer could benefit from surgery. METHODS Patients with T4N0-3M0 esophageal cancer from 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Kaplan-Meier method, Cox proportional hazard regression, and propensity score matching (PSM) were used to compare overall survival (OS) between the surgery and no-surgery group. RESULTS A total of 1822 patients were analyzed. The multivariable Cox regression showed the HR (95% CI) for surgery vs. no surgery was 0.492 (0.427-0.567) (P < 0.001) in T4N0-3M0 cohort, 0.471 (0.354-0.627) (P < 0.001) in T4aN0-3M0 cohort, and 0.480 (0.335-0.689) (P < 0.001) in T4bN0-3M0 cohort. The HR (95% CI) for neoadjuvant therapy plus surgery vs. no surgery and surgery without neoadjuvant therapy vs. no surgery were 0.548 (0.461-0.650) (P < 0.001) and 0.464 (0.375-0.574) (P < 0.001), respectively. No significant OS difference was observed between neoadjuvant therapy plus surgery and surgery without neoadjuvant therapy: 0.966 (0.686-1.360) (P = 0.843). Subgroup analyses and PSM-adjusted analyses showed consistent results. CONCLUSION Surgery might bring OS improvement for T4N0-3M0 esophageal cancer patients, no matter in T4a disease or in T4b disease. Surgery with and without neoadjuvant therapy might both achieve better OS than no surgery.
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Affiliation(s)
- Chen Qi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Liwen Hu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China
| | - Chi Zhang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Kang Wang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China
| | - Bingmei Qiu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Anesthesiology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, 210004, China
| | - Jun Yi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China.
| | - Yi Shen
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China.
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Giorgino R, Alessandri-Bonetti M, Luca A, Migliorini F, Rossi N, Peretti GM, Mangiavini L. ChatGPT in orthopedics: a narrative review exploring the potential of artificial intelligence in orthopedic practice. Front Surg 2023; 10:1284015. [PMID: 38026475 PMCID: PMC10654618 DOI: 10.3389/fsurg.2023.1284015] [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: 08/27/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
The field of orthopedics faces complex challenges requiring quick and intricate decisions, with patient education and compliance playing crucial roles in treatment outcomes. Technological advancements in artificial intelligence (AI) can potentially enhance orthopedic care. ChatGPT, a natural language processing technology developed by OpenAI, has shown promise in various sectors, including healthcare. ChatGPT can facilitate patient information exchange in orthopedics, provide clinical decision support, and improve patient communication and education. It can assist in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, ChatGPT has limitations, such as insufficient expertise in specialized domains and a lack of contextual understanding. The application of ChatGPT in orthopedics is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. The results indicate both the benefits and limitations of ChatGPT, emphasizing the need for caution, ethical considerations, and human oversight. Addressing training data quality, biases, data privacy, and accountability challenges is crucial for responsible implementation. While ChatGPT has the potential to transform orthopedic healthcare, further research and development are necessary to ensure its reliability, accuracy, and ethical use in patient care.
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Affiliation(s)
- Riccardo Giorgino
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Residency Program in Orthopedics and Traumatology, University of Milan, Milan, Italy
| | - Mario Alessandri-Bonetti
- Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Andrea Luca
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Filippo Migliorini
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, Germany
- Department of Orthopedics and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical University, Bolzano, Italy
| | - Nicolò Rossi
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Residency Program in Orthopedics and Traumatology, University of Milan, Milan, Italy
| | - Giuseppe M. Peretti
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Laura Mangiavini
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
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50
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Vicente L, Matute H. Humans inherit artificial intelligence biases. Sci Rep 2023; 13:15737. [PMID: 37789032 PMCID: PMC10547752 DOI: 10.1038/s41598-023-42384-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/09/2023] [Indexed: 10/05/2023] Open
Abstract
Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.
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Affiliation(s)
- Lucía Vicente
- Department of Psychology, Deusto University, Avenida Universidades 24, 48007, Bilbao, Spain
| | - Helena Matute
- Department of Psychology, Deusto University, Avenida Universidades 24, 48007, Bilbao, Spain.
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