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Le KDR, Tay SBP, Choy KT, Verjans J, Sasanelli N, Kong JCH. Applications of natural language processing tools in the surgical journey. Front Surg 2024; 11:1403540. [PMID: 38826809 PMCID: PMC11140056 DOI: 10.3389/fsurg.2024.1403540] [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: 03/19/2024] [Accepted: 05/07/2024] [Indexed: 06/04/2024] Open
Abstract
Background Natural language processing tools are becoming increasingly adopted in multiple industries worldwide. They have shown promising results however their use in the field of surgery is under-recognised. Many trials have assessed these benefits in small settings with promising results before large scale adoption can be considered in surgery. This study aims to review the current research and insights into the potential for implementation of natural language processing tools into surgery. Methods A narrative review was conducted following a computer-assisted literature search on Medline, EMBASE and Google Scholar databases. Papers related to natural language processing tools and consideration into their use for surgery were considered. Results Current applications of natural language processing tools within surgery are limited. From the literature, there is evidence of potential improvement in surgical capability and service delivery, such as through the use of these technologies to streamline processes including surgical triaging, data collection and auditing, surgical communication and documentation. Additionally, there is potential to extend these capabilities to surgical academia to improve processes in surgical research and allow innovation in the development of educational resources. Despite these outcomes, the evidence to support these findings are challenged by small sample sizes with limited applicability to broader settings. Conclusion With the increasing adoption of natural language processing technology, such as in popular forms like ChatGPT, there has been increasing research in the use of these tools within surgery to improve surgical workflow and efficiency. This review highlights multifaceted applications of natural language processing within surgery, albeit with clear limitations due to the infancy of the infrastructure available to leverage these technologies. There remains room for more rigorous research into broader capability of natural language processing technology within the field of surgery and the need for cross-sectoral collaboration to understand the ways in which these algorithms can best be integrated.
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Affiliation(s)
- Khang Duy Ricky Le
- Department of General Surgical Specialties, The Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Geelong Clinical School, Deakin University, Geelong, VIC, Australia
- Department of Medical Education, The University of Melbourne, Melbourne, VIC, Australia
| | - Samuel Boon Ping Tay
- Department of Anaesthesia and Pain Medicine, Eastern Health, Box Hill, VIC, Australia
| | - Kay Tai Choy
- Department of Surgery, Austin Health, Melbourne, VIC, Australia
| | - Johan Verjans
- Australian Institute for Machine Learning (AIML), University of Adelaide, Adelaide, SA, Australia
- Lifelong Health Theme (Platform AI), South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Nicola Sasanelli
- Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, SA, Australia
- Department of Operations (Strategic and International Partnerships), SmartSAT Cooperative Research Centre, Adelaide, SA, Australia
- Agora High Tech, Adelaide, SA, Australia
| | - Joseph C. H. Kong
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Monash University Department of Surgery, Alfred Hospital, Melbourne, VIC, Australia
- Department of Colorectal Surgery, Alfred Hospital, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
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Wang H, Alanis N, Haygood L, Swoboda TK, Hoot N, Phillips D, Knowles H, Stinson SA, Mehta P, Sambamoorthi U. Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis. Acad Emerg Med 2024. [PMID: 38757352 DOI: 10.1111/acem.14937] [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: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVES Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. METHODS We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined. RESULTS A total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%. CONCLUSIONS Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.
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Affiliation(s)
- Hao Wang
- Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA
| | - Naomi Alanis
- Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA
| | - Laura Haygood
- Health Sciences Librarian for Public Health, Brown University, Providence, Rhode Island, USA
| | - Thomas K Swoboda
- Department of Emergency Medicine, The Valley Health System, Touro University Nevada School of Osteopathic Medicine, Las Vegas, Nevada, USA
| | - Nathan Hoot
- Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA
| | - Daniel Phillips
- Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA
| | - Heidi Knowles
- Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA
| | - Sara Ann Stinson
- Mary Couts Burnett Library, Burnett School of Medicine at Texas Christian University, Fort Worth, Texas, USA
| | - Prachi Mehta
- Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA
| | - Usha Sambamoorthi
- College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, USA
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Kooragayala K, Lou J, Hong YK. ASO Author Reflections: Natural Language Processing Aids in the Detection of Incidental Pancreatic Lesions in Cross-Sectional Imaging. Ann Surg Oncol 2022; 29:8520-8521. [PMID: 35969301 DOI: 10.1245/s10434-022-12407-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 11/18/2022]
Affiliation(s)
| | - Johanna Lou
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Young Ki Hong
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
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