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Griffin AC, Wang KH, Leung TI, Facelli JC. Recommendations to promote fairness and inclusion in biomedical AI research and clinical use. J Biomed Inform 2024; 157:104693. [PMID: 39019301 PMCID: PMC11402591 DOI: 10.1016/j.jbi.2024.104693] [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: 09/30/2023] [Revised: 06/25/2024] [Accepted: 07/14/2024] [Indexed: 07/19/2024]
Abstract
OBJECTIVE Understanding and quantifying biases when designing and implementing actionable approaches to increase fairness and inclusion is critical for artificial intelligence (AI) in biomedical applications. METHODS In this Special Communication, we discuss how bias is introduced at different stages of the development and use of AI applications in biomedical sciences and health care. We describe various AI applications and their implications for fairness and inclusion in sections on 1) Bias in Data Source Landscapes, 2) Algorithmic Fairness, 3) Uncertainty in AI Predictions, 4) Explainable AI for Fairness and Equity, and 5) Sociological/Ethnographic Issues in Data and Results Representation. RESULTS We provide recommendations to address biases when developing and using AI in clinical applications. CONCLUSION These recommendations can be applied to informatics research and practice to foster more equitable and inclusive health care systems and research discoveries.
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Affiliation(s)
- Ashley C Griffin
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California and Stanford University School of Medicine, Stanford, California, USA.
| | - Karen H Wang
- Department of Internal Medicine and Equity Research and Innovation Center, Yale School of Medicine, USA.
| | - Tiffany I Leung
- Southern Illinois University School of Medicine, Scientific Editorial Director, JMIR Publications, USA.
| | - Julio C Facelli
- Department of Biomedical Informatics and Utah Center for Clinical and Translatinal Science, Spencer Fox Eccles School of Medicine, University of Utah, USA.
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Tignanelli CJ, Shah S, Vock D, Siegel L, Serrano C, Haut E, Switzer S, Martin CL, Rizvi R, Peta V, Jenkins PC, Lemke N, Thyvalikakath T, Osheroff JA, Torres D, Vawdrey D, Callcut RA, Butler M, Melton GB. A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury. Implement Sci 2024; 19:57. [PMID: 39103955 DOI: 10.1186/s13012-024-01386-4] [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: 06/09/2024] [Accepted: 07/14/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. METHODS The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a "Living Guideline" process model for the VTE prevention CDS system. DISCUSSION The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics. TRIAL REGISTRATION Clinicaltrials.gov - NCT05628207. Prospectively registered 11/28/2022, https://classic. CLINICALTRIALS gov/ct2/show/NCT05628207 .
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Affiliation(s)
- Christopher J Tignanelli
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA.
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA.
- Center for Quality Outcomes, Discovery and Evaluation, University of Minnesota, Minneapolis, MN, USA.
| | - Surbhi Shah
- Department of Medicine, Mayo Clinic, Scottsdale, AZ, USA
| | - David Vock
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA
| | - Lianne Siegel
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA
| | - Carlos Serrano
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA
| | - Elliott Haut
- Department of Surgery, Johns Hopkins University, Baltimore, MD, USA
| | | | | | - Rubina Rizvi
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
| | - Vincent Peta
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA
| | - Peter C Jenkins
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nicholas Lemke
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA
| | - Thankam Thyvalikakath
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA
- Indiana University School of Dentistry, Indianapolis, IN, USA
| | | | - Denise Torres
- Department of Surgery, Geisinger Health, Danville, PA, USA
| | - David Vawdrey
- Department of Biomedical Informatics, Geisinger Health, Danville, PA, USA
| | - Rachael A Callcut
- Department of Surgery, UC Davis School of Medicine, Sacramento, CA, USA
| | - Mary Butler
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
- School of Publish Health, University of Minnesota, Minneapolis, MN, USA
| | - Genevieve B Melton
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
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3
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Ferdush J, Begum M, Hossain ST. ChatGPT and Clinical Decision Support: Scope, Application, and Limitations. Ann Biomed Eng 2024; 52:1119-1124. [PMID: 37516680 DOI: 10.1007/s10439-023-03329-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 07/18/2023] [Indexed: 07/31/2023]
Abstract
This study examines ChatGPT's role in clinical decision support, by analyzing its scope, application, and limitations. By analyzing patient data and providing evidence-based recommendations, ChatGPT, an AI language model, can help healthcare professionals make well-informed decisions. This study examines ChatGPT's use in clinical decision support, including diagnosis and treatment planning. However, it acknowledges limitations like biases, lack of contextual understanding, and human oversight and also proposes a framework for the future clinical decision support system. Understanding these factors will allow healthcare professionals to utilize ChatGPT effectively and make accurate clinical decisions. Further research is needed to understand the implications of using ChatGPT in healthcare settings and to develop safeguards for responsible use.
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Affiliation(s)
- Jannatul Ferdush
- Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
| | - Mahbuba Begum
- Department of Computer Science and Engineering, Mawlana Bhasani Science and Technology, Tangail, 1902, Bangladesh
| | - Sakib Tanvir Hossain
- Department of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh
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Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
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Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
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Loftus TJ, Altieri MS, Balch JA, Abbott KL, Choi J, Marwaha JS, Hashimoto DA, Brat GA, Raftopoulos Y, Evans HL, Jackson GP, Walsh DS, Tignanelli CJ. Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions. Ann Surg 2023; 278:51-58. [PMID: 36942574 DOI: 10.1097/sla.0000000000005853] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting. BACKGROUND To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown. METHODS Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy. CONCLUSIONS Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.
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Affiliation(s)
- Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
| | - Maria S Altieri
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, University of Pennsylvania, Philadelphia, PA
| | - Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
| | - Kenneth L Abbott
- Department of Surgery, University of Florida Health, Gainesville, FL
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
| | - Jeff Choi
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, Stanford University, Stanford, CA
| | - Jayson S Marwaha
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, Beth Israel Deaconess Medical Center
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Daniel A Hashimoto
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- General Robotics, Automation, Sensing, and Perception Laboratory, University of Pennsylvania School of Engineering and Applied Science, Philadelphia, PA
| | - Gabriel A Brat
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, Beth Israel Deaconess Medical Center
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Yannis Raftopoulos
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Weight Management Program, Holyoke Medical Center, Holyoke, MA
| | - Heather L Evans
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, Medical University of South Carolina, Charleston, SC
| | - Gretchen P Jackson
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Digital, Intuitive Surgical, Sunnyvale, CA; Departments of Pediatric Surgery, Pediatrics, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Danielle S Walsh
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery, University of Kentucky, Lexington, KY
| | - Christopher J Tignanelli
- American College of Surgeons Health Information Technology Committee and Artificial Intelligence Subcommittee, Chicago, IL
- Department of Surgery
- Institute for Health Informatics
- Program for Clinical Artificial Intelligence, Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN
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6
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RE-AIMing the Focus on Health Equity in Surgery. Ann Surg 2023; 277:365-366. [PMID: 36102192 DOI: 10.1097/sla.0000000000005711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Invited Commentary: Postoperative Artificial Intelligence Model for ICU Triage. J Am Coll Surg 2023; 236:292-293. [PMID: 36395417 DOI: 10.1097/xcs.0000000000000487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Kohn R, Weissman GE, Wang W, Ingraham NE, Scott S, Bayes B, Anesi GL, Halpern SD, Kipnis P, Liu VX, Dudley RA, Kerlin MP. Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2). MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.19.23284796. [PMID: 36712116 PMCID: PMC9882631 DOI: 10.1101/2023.01.19.23284796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes. Objectives Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. Research design Retrospective cohort study. Subjects All ICU patients in five hospitals from October 2017 through September 2019. Measures We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots. Results The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone. Conclusions Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.
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Affiliation(s)
- Rachel Kohn
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania,Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania,Leonard Davis Institute of Health Economics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gary E. Weissman
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania,Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania,Leonard Davis Institute of Health Economics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Wei Wang
- Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Stefania Scott
- Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Brian Bayes
- Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - George L. Anesi
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania,Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania,Leonard Davis Institute of Health Economics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Scott D. Halpern
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania,Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania,Leonard Davis Institute of Health Economics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania,Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania,Department of Medical Ethics and Health Policy, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Patricia Kipnis
- Division of Research, Kaiser Permanente, Oakland, California
| | - Vincent X. Liu
- Division of Research, Kaiser Permanente, Oakland, California
| | | | - Meeta Prasad Kerlin
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania,Palliative and Advanced Illness Research (PAIR) Center at the University of Pennsylvania, Philadelphia, Pennsylvania,Leonard Davis Institute of Health Economics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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Cobianchi L, Piccolo D, Dal Mas F, Agnoletti V, Ansaloni L, Balch J, Biffl W, Butturini G, Catena F, Coccolini F, Denicolai S, De Simone B, Frigerio I, Fugazzola P, Marseglia G, Marseglia GR, Martellucci J, Modenese M, Previtali P, Ruta F, Venturi A, Kaafarani HM, Loftus TJ. Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey. World J Emerg Surg 2023; 18:1. [PMID: 36597105 PMCID: PMC9811693 DOI: 10.1186/s13017-022-00467-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. METHODS An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. RESULTS 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. DISCUSSION The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.
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Affiliation(s)
- Lorenzo Cobianchi
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy.
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy.
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy.
| | - Daniele Piccolo
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- Department of Neurosurgery, ASUFC Santa Maria Della Misericordia, Udine, Italy
| | - Francesca Dal Mas
- Department of Management, Ca' Foscari University of Venice, Venice, Italy
| | | | - Luca Ansaloni
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Jeremy Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
| | - Walter Biffl
- Division of Trauma and Acute Care Surgery, Scripps Memorial Hospital La Jolla, La Jolla, CA, USA
| | - Giovanni Butturini
- Department of HPB Surgery, Pederzoli Hospital, Peschiera del Garda, Italy
| | | | - Federico Coccolini
- General, Emergency and Trauma Surgery Department, Pisa University Hospital Pisa, Pisa, Italy
| | - Stefano Denicolai
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Economics and Management, University of Pavia, Pavia, Italy
| | - Belinda De Simone
- Department of Emergency, Digestive and Metabolic Minimally Invasive Surgery, Poissy and Saint Germain en Laye Hospitals, Poissy, France
| | - Isabella Frigerio
- Department of HPB Surgery, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Paola Fugazzola
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Gianluigi Marseglia
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- IRCCS Policlinico San Matteo Foundation, Pediatric Clinic., Pavia, Italy
| | | | | | | | - Pietro Previtali
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Economics and Management, University of Pavia, Pavia, Italy
| | - Federico Ruta
- General Direction, ASL BAT (Health Agency), Andria, Italy
| | - Alessandro Venturi
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- Bureau of the Presidency, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Haytham M Kaafarani
- Harvard Medical School, Boston, MA, USA
- Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA, USA
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
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