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Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea RY, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. MEDICAL TEACHER 2024; 46:446-470. [PMID: 38423127 DOI: 10.1080/0142159x.2024.2314198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
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Affiliation(s)
- Morris Gordon
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
- Blackpool Hospitals NHS Foundation Trust, Blackpool, UK
| | - Michelle Daniel
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Aderonke Ajiboye
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Hussein Uraiby
- Department of Cellular Pathology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Nicole Y Xu
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Rangana Bartlett
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Janice Hanson
- Department of Medicine and Office of Education, School of Medicine, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Mary Haas
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maxwell Spadafore
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | | | - Colin Michie
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Janet Corral
- Department of Medicine, University of Nevada Reno, School of Medicine, Reno, NV, USA
| | - Brian Kwan
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Diana Dolmans
- School of Health Professions Education, Faculty of Health, Maastricht University, Maastricht, NL, USA
| | - Satid Thammasitboon
- Center for Research, Innovation and Scholarship in Health Professions Education, Baylor College of Medicine, Houston, TX, USA
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Ryder CY, Mott NM, Gross CL, Anidi C, Shigut L, Bidwell SS, Kim E, Zhao Y, Ngam BN, Snell MJ, Yu BJ, Forczmanski P, Rooney DM, Jeffcoach DR, Kim GJ. Using Artificial Intelligence to Gauge Competency on a Novel Laparoscopic Training System. JOURNAL OF SURGICAL EDUCATION 2024; 81:267-274. [PMID: 38160118 DOI: 10.1016/j.jsurg.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/08/2023] [Accepted: 10/13/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE Laparoscopic surgical skill assessment and machine learning are often inaccessible to low-and-middle-income countries (LMIC). Our team developed a low-cost laparoscopic training system to teach and assess psychomotor skills required in laparoscopic salpingostomy in LMICs. We performed video review using AI to assess global surgical techniques. The objective of this study was to assess the validity of artificial intelligence (AI) generated scoring measures of laparoscopic simulation videos by comparing the accuracy of AI results to human-generated scores. DESIGN Seventy-four surgical simulation videos were collected and graded by human participants using a modified OSATS (Objective Structured Assessment of Technical Skills). The videos were then analyzed via AI using 3 different time and distance-based calculations of the laparoscopic instruments including path length, dimensionless jerk, and standard deviation of tool position. Predicted scores were generated using 5-fold cross validation and K-Nearest-Neighbors to train classifiers. SETTING Surgical novices and experts from a variety of hospitals in Ethiopia, Cameroon, Kenya, and the United States contributed 74 laparoscopic salpingostomy simulation videos. RESULTS Complete accuracy of AI compared to human assessment ranged from 65-77%. There were no statistical differences in rank mean scores for 3 domains, Flow of Operation, Respect for Tissue, and Economy of Motion, while there were significant differences in ratings for Instrument Handling, Overall Performance, and the total summed score of all 5 domains (Summed). Estimated effect sizes were all less than 0.11, indicating very small practical effect. Estimated intraclass correlation coefficient (ICC) of Summed was 0.72 indicating moderate correlation between AI and Human scores. CONCLUSIONS Video review using AI technology of global characteristics was similar to that of human review in our laparoscopic training system. Machine learning may help fill an educational gap in LMICs where direct apprenticeship may not be feasible.
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Affiliation(s)
| | - Nicole M Mott
- University of Michigan Medical School, Ann Arbor, Michigan
| | | | - Chioma Anidi
- University of Michigan Medical School, Ann Arbor, Michigan
| | - Leul Shigut
- Department of Surgery, Soddo Christian General Hospital, Soddo, Ethiopia
| | | | - Erin Kim
- University of Michigan Medical School, Ann Arbor, Michigan
| | - Yimeng Zhao
- University of Michigan Medical School, Ann Arbor, Michigan
| | | | - Mark J Snell
- Department of Surgery, Mbingo Baptist Hospital, Mbingo, Cameroon
| | - B Joon Yu
- Department of Surgery, University of Michigan, Ann Arbor, Michigan
| | - Pawel Forczmanski
- Department of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, Szczecin, Poland
| | - Deborah M Rooney
- Department of Learning Sciences, University of Michigan, Ann Arbor, Michigan
| | - David R Jeffcoach
- Department of Surgery, Community Regional Medical Center, Fresno, California
| | - Grace J Kim
- Department of Surgery, University of Michigan, Ann Arbor, Michigan.
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Pedrett R, Mascagni P, Beldi G, Padoy N, Lavanchy JL. Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surg Endosc 2023; 37:7412-7424. [PMID: 37584774 PMCID: PMC10520175 DOI: 10.1007/s00464-023-10335-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/20/2023] [Indexed: 08/17/2023]
Abstract
BACKGROUND Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. METHODS A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. CONCLUSION AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.
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Affiliation(s)
- Romina Pedrett
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pietro Mascagni
- IHU Strasbourg, Strasbourg, France
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Guido Beldi
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Nicolas Padoy
- IHU Strasbourg, Strasbourg, France
- ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Joël L Lavanchy
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- IHU Strasbourg, Strasbourg, France.
- University Digestive Health Care Center Basel - Clarunis, PO Box, 4002, Basel, Switzerland.
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Rigberg DA, Jim J. Considerations for the application of artificial intelligence in vascular surgical education. Semin Vasc Surg 2023; 36:471-474. [PMID: 37863622 DOI: 10.1053/j.semvascsurg.2023.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/23/2023] [Accepted: 07/28/2023] [Indexed: 10/22/2023]
Abstract
The rapid adoption of artificial intelligence (AI) into everyday use has presented multiple issues for surgical educators to consider. In this article, the authors discuss some of the ethical aspects of academic integrity and the use of AI. These issues include the importance of understanding the current limits of AI and the inherent biases of the technology. The authors further discuss the ethical considerations of the use of AI in surgical training and in clinical use, with an emphasis on vascular surgery.
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Affiliation(s)
- David A Rigberg
- Division of Vascular Surgery, University of California, 200 Medical Plaza, Suite 526, Los Angeles, CA 90095.
| | - Jeffrey Jim
- Minneapolis Heart Institute, Abbott Northwestern Hospital, Minneapolis, MN
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Hashemi N, Svendsen MBS, Bjerrum F, Rasmussen S, Tolsgaard MG, Friis ML. Acquisition and usage of robotic surgical data for machine learning analysis. Surg Endosc 2023:10.1007/s00464-023-10214-7. [PMID: 37389741 PMCID: PMC10338401 DOI: 10.1007/s00464-023-10214-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND The increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment. Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting. METHOD We tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: 'Capturing image data from the surgical robot', 'Extracting event data', 'Capturing movement data of the surgeon', 'Annotation of image data'. RESULTS 15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons' arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI. CONCLUSION With our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI.
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Affiliation(s)
- Nasseh Hashemi
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark.
- Nordsim-Centre for Skills Training and Simulation, Aalborg, Denmark.
- ROCnord-Robot Centre, Aalborg University Hospital, Aalborg, Denmark.
- Department of Urology, Aalborg University Hospital, Aalborg, Denmark.
| | - Morten Bo Søndergaard Svendsen
- Copenhagen Academy for Medical Education and Simulation, Center for Human Resources and Education, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Bjerrum
- Copenhagen Academy for Medical Education and Simulation, Center for Human Resources and Education, Copenhagen, Denmark
- Department of Gastrointestinal and Hepatic Diseases, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Sten Rasmussen
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
| | - Martin G Tolsgaard
- Nordsim-Centre for Skills Training and Simulation, Aalborg, Denmark
- Copenhagen Academy for Medical Education and Simulation, Center for Human Resources and Education, Copenhagen, Denmark
| | - Mikkel Lønborg Friis
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
- Nordsim-Centre for Skills Training and Simulation, Aalborg, Denmark
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Pan M, Wang S, Li J, Li J, Yang X, Liang K. An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094496. [PMID: 37177699 PMCID: PMC10181496 DOI: 10.3390/s23094496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.
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Affiliation(s)
- Mingzhang Pan
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Nanning 530004, China
| | - Shuo Wang
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Jingao Li
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Jing Li
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Xiuze Yang
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
| | - Ke Liang
- College of Mechanical Engineering, Guangxi University, Nanning 530004, China
- Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, School of Mechanical Engineering, Guangxi University, Nanning 530004, China
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Artificial Intelligence in Surgical Learning. SURGERIES 2023. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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Joosten M, de Blaauw I, Botden SM. Validated simulation models in pediatric surgery: A review. J Pediatr Surg 2022; 57:876-886. [PMID: 35871858 DOI: 10.1016/j.jpedsurg.2022.06.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION This review evaluates the validation and availability of simulation models in the field of pediatric surgery that can be used for training purposes. METHODS MEDLINE and EMBASE were searched for studies describing a simulation models in pediatric surgery. Articles were included if face, content and/or construct validity was described. Additionally, the costs and availability were assessed. Validation scores for each model were depicted as percentage (0-100), based on the reported data, to compare the outcomes. A score of >70% was considered adequate. RESULTS Forty-three studies were identified, describing the validation process of 38 simulation models. Face validity was evaluated in 33 articles, content in 36 and construct in 19. Twenty-two models received adequate validation scores (>70%). The majority (27/38, 70%) was strictly inanimate. Five models were available for purchase and eleven models were replicable based on the article. CONCLUSION The number of validated inanimate simulation models for pediatric surgery procedures is growing, however, few are replicable or available for widespread training purposes. LEVEL OF EVIDENCE Level II.
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Affiliation(s)
- Maja Joosten
- Department of Pediatric Surgery, Radboudumc - Amalia Children's Hospital, Geert Grooteplein Zuid 10 Route 618, Nijmegen 6500HB, the Netherlands.
| | - Ivo de Blaauw
- Department of Pediatric Surgery, Radboudumc - Amalia Children's Hospital, Geert Grooteplein Zuid 10 Route 618, Nijmegen 6500HB, the Netherlands
| | - Sanne Mbi Botden
- Department of Pediatric Surgery, Radboudumc - Amalia Children's Hospital, Geert Grooteplein Zuid 10 Route 618, Nijmegen 6500HB, the Netherlands
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Zheng Y, Ershad M, Fey AM. Toward Correcting Anxious Movements Using Haptic Cues on the Da Vinci Surgical Robot. PROCEEDINGS OF THE ... IEEE/RAS-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS. IEEE/RAS-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS 2022; 2022:10.1109/biorob52689.2022.9925380. [PMID: 37408769 PMCID: PMC10321328 DOI: 10.1109/biorob52689.2022.9925380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Surgical movements have an important stylistic quality that individuals without formal surgical training can use to identify expertise. In our prior work, we sought to characterize quantitative metrics associated with surgical style and developed a near-real-time detection framework for stylistic deficiencies using a commercial haptic device. In this paper, we implement bimanual stylistic detection on the da Vinci Research Kit (dVRK) and focus on one stylistic deficiency, "Anxious", which may describe movements under stressful conditions. Our goal is to potentially correct these "Anxious" movements by exploring the effects of three different types of haptic cues (time-variant spring, damper, and spring-damper feedback) on performance during a basic surgical training task using the da Vinci Research Kit (dVRK). Eight subjects were recruited to complete peg transfer tasks using a randomized order of haptic cues and with baseline trials between each task. Overall, all cues lead to a significant improvement over baseline economy of volume and time-variant spring haptic cues lead to significant improvements in reducing the classified "Anxious" movements and also corresponded with significantly lower path length and economy of volume for the non-dominant hand. This work is the first step in evaluating our stylistic detection model on a surgical robot and could lay the groundwork for future methods to actively and adaptively reduce the negative effect of stress in the operating room.
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Affiliation(s)
- Yi Zheng
- Department of Mechanical Engineering, the University of Texas at Austin, 204 East Dean Keeton Street, Austin, TX 78712, USA
| | - Marzieh Ershad
- Intuitive Surgical, Inc., 1020 Kifer Road Sunnyvale, CA 94086
| | - Ann Majewicz Fey
- Department of Mechanical Engineering, the University of Texas at Austin, 204 East Dean Keeton Street, Austin, TX 78712, USA
- Department of Surgery, UT South-western Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
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Lam K, Chen J, Wang Z, Iqbal FM, Darzi A, Lo B, Purkayastha S, Kinross JM. Machine learning for technical skill assessment in surgery: a systematic review. NPJ Digit Med 2022; 5:24. [PMID: 35241760 PMCID: PMC8894462 DOI: 10.1038/s41746-022-00566-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 01/21/2022] [Indexed: 12/18/2022] Open
Abstract
Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive, and subject to bias. Machine learning (ML) has the potential to provide rapid, automated, and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66), and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed the performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment of basic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.PROSPERO: CRD42020226071.
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Affiliation(s)
- Kyle Lam
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
| | - Junhong Chen
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
| | - Zeyu Wang
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
| | - Fahad M Iqbal
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
| | - Ara Darzi
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
| | - Benny Lo
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
| | - Sanjay Purkayastha
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK.
| | - James M Kinross
- Department of Surgery and Cancer, 10th Floor Queen Elizabeth the Queen Mother Building, St Mary's Hospital, Imperial College, London, W2 1NY, UK
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Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions. JOURNAL OF SURGICAL EDUCATION 2022; 79:500-515. [PMID: 34756807 DOI: 10.1016/j.jsurg.2021.09.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/21/2021] [Accepted: 09/16/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021. SETTING/PARTICIPANTS We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion. RESULTS After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods. CONCLUSIONS There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.
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Affiliation(s)
| | - Dylan Young
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
| | - Shawn Khan
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Noelle Crasto
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
| | - Mara Sobel
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Ryerson University and St. Michael's Hospital, Toronto, Ontario, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Ryerson University and St. Michael's Hospital, Toronto, Ontario, Canada; Department of Obstetrics and Gynaecology, University of Toronto, Toronto, Ontario, Canada; The Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada
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12
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Shankar PR. Artificial intelligence in health professions education. ARCHIVES OF MEDICINE AND HEALTH SCIENCES 2022. [DOI: 10.4103/amhs.amhs_234_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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Bilgic E, Gorgy A, Yang A, Cwintal M, Ranjbar H, Kahla K, Reddy D, Li K, Ozturk H, Zimmermann E, Quaiattini A, Abbasgholizadeh-Rahimi S, Poenaru D, Harley JM. Exploring the roles of artificial intelligence in surgical education: A scoping review. Am J Surg 2021; 224:205-216. [PMID: 34865736 DOI: 10.1016/j.amjsurg.2021.11.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education. METHODS Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education. RESULTS Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors. CONCLUSIONS AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.
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Affiliation(s)
- Elif Bilgic
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Andrew Gorgy
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Alison Yang
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Michelle Cwintal
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Hamed Ranjbar
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Kalin Kahla
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Dheeksha Reddy
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Kexin Li
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Helin Ozturk
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Eric Zimmermann
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Andrea Quaiattini
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Canada; Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine, McGill University, Montreal, Quebec, Canada; Department of Electrical and Computer Engineering, McGill University, Montreal, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada; Mila Quebec AI Institute, Montreal, Canada
| | - Dan Poenaru
- Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Department of Pediatric Surgery, McGill University, Canada
| | - Jason M Harley
- Department of Surgery, McGill University, Montreal, Quebec, Canada; Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Steinberg Centre for Simulation and Interactive Learning, McGill University, Montreal, Quebec, Canada.
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Lajkó G, Nagyné Elek R, Haidegger T. Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery. SENSORS (BASEL, SWITZERLAND) 2021; 21:5412. [PMID: 34450854 PMCID: PMC8398563 DOI: 10.3390/s21165412] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/02/2021] [Accepted: 08/05/2021] [Indexed: 02/06/2023]
Abstract
Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic-acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors-or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU-ISI Gesture and Skill Assessment Working Set dataset-co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy-averaged over five cross-validation trials-for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features-such as image-based orientation and image-based collision detection-or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.
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Affiliation(s)
- Gábor Lajkó
- Autonomous Systems Track, Double Degree Programme, EIT Digital Master School, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany;
- ELTE Faculty of Informatics, Pázmány Péter Sétány 1/C, Eötvös Loránd University, Egyetem tér 1-3, 1117 Budapest, Hungary
| | - Renáta Nagyné Elek
- Antal Bejczy Center for Intelligent Robotics, University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary;
- Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/b, 1034 Budapest, Hungary
- John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b, 1034 Budapest, Hungary
| | - Tamás Haidegger
- Antal Bejczy Center for Intelligent Robotics, University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary;
- Austrian Center for Medical Innovation and Technology, Viktor Kaplan-Straße 2/1, 2700 Wiener Neustadt, Austria
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15
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Jones KA, Jani KH, Jones GW, Nye ML, Duff JP, Cheng A, Lin Y, Davidson J, Chatfield J, Tofil N, Gaither S, Kessler DO. Using natural language processing to compare task-specific verbal cues in coached versus noncoached cardiac arrest teams during simulated pediatrics resuscitation. AEM EDUCATION AND TRAINING 2021; 5:e10707. [PMID: 34926971 PMCID: PMC8643156 DOI: 10.1002/aet2.10707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 10/20/2021] [Accepted: 10/25/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Coaches improve cardiopulmonary (CPR) outcomes in real-world and simulated settings. To explore verbal feedback that targets CPR quality, we used natural language processing (NLP) methodologies on transcripts from a published pediatric randomized trial (coach vs. no coach in simulated CPR). Study objectives included determining any differences by trial arm in (1) overall communication and (2) metrics over minutes of CPR and (3) exploring overall frequencies and temporal patterns according to degrees of CPR excellence. METHODS A human-generated transcription service produced 40 team transcripts. Automated text search with manual review assigned semantic category; word count; and presence of verbal cues for general CPR, compression depth or rate, or positive feedback to transcript utterances. Resulting cue counts per minute (CPM) were corresponded to CPR quality based on compression rate and depth per minute. CPMs were compared across trial arms and over the 18 min of CPR. Adaptation to excellence was analyzed across four patterns of CPR excellence determined by k-shape methods. RESULTS Overall coached teams experienced more rate-directive, depth-directive, and positive verbal cues compared with noncoached teams. The frequency of coaches' depth cues changed over minutes of CPR, indicating adaptation. In coached teams, the number of depth-directive cues differed among the four patterns of CPR excellence. Noncoached teams experienced fewer utterances by type, with no adaptation over time or to CPR performance. CONCLUSION NLP extracted verbal metrics and their patterns in resuscitation sessions provides insight into communication patterns and skills used by CPR coaches and other team members. This could help to further optimize CPR training, feedback, excellence, and outcomes.
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Affiliation(s)
- Kai A. Jones
- Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Karan H. Jani
- Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | | | - Megan L. Nye
- Department of Emergency MedicineColumbia UniversityNew YorkNew YorkUSA
| | - Jonathan P. Duff
- Department of PediatricsUniversity of AlbertaEdmontonAlbertaCanada
| | - Adam Cheng
- Department of PediatricsUniversity of CalgaryAlberta Children's HospitalCalgaryAlbertaCanada
- Department of Emergency MedicineUniversity of CalgaryCalgaryAlbertaCanada
| | - Yiqun Lin
- Department of PediatricsUniversity of CalgaryAlberta Children's HospitalCalgaryAlbertaCanada
| | - Jennifer Davidson
- Department of PediatricsUniversity of CalgaryAlberta Children's HospitalCalgaryAlbertaCanada
| | - Jenny Chatfield
- Department of PediatricsUniversity of CalgaryAlberta Children's HospitalCalgaryAlbertaCanada
| | - Nancy Tofil
- Department of PediatricsUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Stacy Gaither
- Department of PediatricsUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - David O. Kessler
- Department of Emergency MedicineColumbia UniversityNew YorkNew YorkUSA
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16
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Beyersdorffer P, Kunert W, Jansen K, Miller J, Wilhelm P, Burgert O, Kirschniak A, Rolinger J. Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks. ACTA ACUST UNITED AC 2021; 66:413-421. [PMID: 33655738 DOI: 10.1515/bmt-2020-0106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 02/16/2021] [Indexed: 01/17/2023]
Abstract
Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
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Affiliation(s)
| | - Wolfgang Kunert
- Department of Surgery and Transplantation, Tübingen University Hospital, Tübingen, Germany
| | - Kai Jansen
- Department of Surgery and Transplantation, Tübingen University Hospital, Tübingen, Germany
| | - Johanna Miller
- Department of Surgery and Transplantation, Tübingen University Hospital, Tübingen, Germany
| | - Peter Wilhelm
- Department of Surgery and Transplantation, Tübingen University Hospital, Tübingen, Germany
| | - Oliver Burgert
- Department of Medical Informatics, Reutlingen University, Reutlingen, Germany
| | - Andreas Kirschniak
- Department of Surgery and Transplantation, Tübingen University Hospital, Tübingen, Germany
| | - Jens Rolinger
- Department of Surgery and Transplantation, Tübingen University Hospital, Tübingen, Germany
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17
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Alnafisee N, Zafar S, Vedula SS, Sikder S. Current methods for assessing technical skill in cataract surgery. J Cataract Refract Surg 2021; 47:256-264. [PMID: 32675650 DOI: 10.1097/j.jcrs.0000000000000322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/19/2020] [Indexed: 12/18/2022]
Abstract
Surgery is a major source of errors in patient care. Preventing complications from surgical errors in the operating room is estimated to lead to reduction of up to 41 846 readmissions and save $620.3 million per year. It is now established that poor technical skill is associated with an increased risk of severe adverse events postoperatively and traditional models to train surgeons are being challenged by rapid advances in technology, an intensified patient-safety culture, and a need for value-driven health systems. This review discusses the current methods available for evaluating technical skills in cataract surgery and the recent technological advancements that have enabled capture and analysis of large amounts of complex surgical data for more automated objective skills assessment.
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Affiliation(s)
- Nouf Alnafisee
- From the The Wilmer Eye Institute, Johns Hopkins University School of Medicine (Alnafisee, Zafar, Sikder), Baltimore, and the Department of Computer Science, Malone Center for Engineering in Healthcare, The Johns Hopkins University Whiting School of Engineering (Vedula), Baltimore, Maryland, USA
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18
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Castillo-Segura P, Fernández-Panadero C, Alario-Hoyos C, Muñoz-Merino PJ, Delgado Kloos C. Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review. Artif Intell Med 2021; 112:102007. [PMID: 33581827 DOI: 10.1016/j.artmed.2020.102007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/25/2020] [Accepted: 12/28/2020] [Indexed: 11/18/2022]
Abstract
The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons' levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.
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Affiliation(s)
- Pablo Castillo-Segura
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
| | | | - Carlos Alario-Hoyos
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
| | - Pedro J Muñoz-Merino
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
| | - Carlos Delgado Kloos
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
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19
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Tolsgaard MG, Boscardin CK, Park YS, Cuddy MM, Sebok-Syer SS. The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs. ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2020; 25:1057-1086. [PMID: 33141345 DOI: 10.1007/s10459-020-10009-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/24/2020] [Indexed: 06/11/2023]
Abstract
Data science is an inter-disciplinary field that uses computer-based algorithms and methods to gain insights from large and often complex datasets. Data science, which includes Artificial Intelligence techniques such as Machine Learning (ML), has been credited with the promise to transform Health Professions Education (HPE) by offering approaches to handle big (and often messy) data. To examine this promise, we conducted a critical review to explore: (1) published applications of data science and ML in HPE literature and (2) the potential role of data science and ML in shifting theoretical and epistemological perspectives in HPE research and practice. Existing data science studies in HPE are often not informed by theory, but rather oriented towards developing applications for specific problems, uses, and contexts. The most common areas currently being studied are procedural (e.g., computer-based tutoring or adaptive systems and assessment of technical skills). We found that epistemic beliefs informing the use of data science and ML in HPE poses a challenge for existing views on what constitutes objective knowledge and the role of human subjectivity for instruction and assessment. As a result, criticisms have emerged that the integration of data science in the field of HPE is in danger of becoming technically driven and narrowly focused in its approach to teaching, learning and assessment. Our findings suggest that researchers tend to formalize around the epistemological stance driven largely by traditions of a research paradigm. Future data science studies in HPE need to involve both education scientists and data scientists to ensure mutual advancements in the development of educational theory and practical applications. This may be one of the most important tasks in the integration of data science and ML in HPE research in the years to come.
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Affiliation(s)
- Martin G Tolsgaard
- Copenhagen Academy for Medical Education and Simulation (CAMES), Rigshospitalet, Copenhagen, Denmark.
- Department of Obstetrics, Centre for Fetal Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
| | - Christy K Boscardin
- Department of Medicine, Department of Anesthesia, University of California San Francisco, San Francisco, CA, USA
| | - Yoon Soo Park
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Monica M Cuddy
- National Board of Medical Examiners, Philadelphia, PA, USA
| | - Stefanie S Sebok-Syer
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, USA
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20
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Masters K. Artificial Intelligence developments in medical education: a conceptual and practical framework. MEDEDPUBLISH 2020; 9:239. [PMID: 38058891 PMCID: PMC10697470 DOI: 10.15694/mep.2020.000239.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
This article was migrated. The article was marked as recommended. As Artificial Intelligence (AI) develops in medicine, there is a greater awareness that medical education may also benefit from AI. Many AI projects are already underway, and many more are still to come. Most medical education administrators and educators are aware of AI, but are not necessarily familiar enough with it to understand the areas of possible application in both using AI in medical education, and also in the content areas that need to be raised with their students. As such, they are at a disadvantage of not understanding the current lie of the land, and are at an even greater disadvantage of not being able to influence and guide future AI projects. This paper attempts to provide an AI conceptual and practical framework for medical education administrators and educators, so that they may have a clearer understanding of the current situation, and may be better placed to guide future AI developments to meet their needs in medical education.
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21
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Feasibility Study of the Low-Cost Motion Tracking System for Assessing Endoscope Holding Skills. World Neurosurg 2020; 140:312-319. [DOI: 10.1016/j.wneu.2020.04.191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 11/22/2022]
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22
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Artificial Intelligence for Education of Vascular Surgeons. Eur J Vasc Endovasc Surg 2020; 59:870-871. [DOI: 10.1016/j.ejvs.2020.02.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/31/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023]
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23
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Oussi N, Georgiou K, Larentzakis A, Thanasas D, Castegren M, Georgiou E, Enochsson L. Validation of a Novel Needle Holder to Train Advanced Laparoscopy Skills to Novices in a Simulator Environment. Surg Innov 2020; 27:211-219. [PMID: 32008414 DOI: 10.1177/1553350619901222] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background. Our aim was to determine if a newly designed Najar needle holder (NNH) shortens the time for novices to improve advanced laparoscopy (AL) techniques (suturing/knot tying), compared with a conventional macro needle holder (MNH) in a simulator. Furthermore, we aimed to validate a new video scoring system determining AL skills. Methods. Forty-six medical students performed identical surgical tasks in a prospective, crossover study evaluating AL skills (NNH vs MNH). All subjects performed a double-throw knot, 2 single-throw knots following 3 running sutures in the Simball Box (SB) simulator. After resting, subjects switched needle holders. All tasks were videotaped and analyzed using SB software and by 2 independent reviewers using the Objective Video Evaluation Scoring Table (OVEST). Trial performance expressed as SB Overall Score (SBOS) and OVEST. Results. In the group starting with NNH (followed by MNH) OVEST was consistently high during both trials (median = 12.5, range = 6.5-18.0, and median = 13.5, range = 6.5-21.0; P = .2360). However, in the group starting with MNH, OVEST improved significantly when the participants changed to NNH (median = 10.0, range = 2.5-19.5, vs median = 14.5, range = 4.5-18.0; P = .0003); an improvement was also found with SBOS (median = 37%, range = 27% to 92%, vs median = 48%, range = 34% to 70%; P = .0289). In both trials, both independent reviewers' OVEST measures correlated well: Trial 1: β = 0.97, P < .0001; and Trial 2: β = 0.95, P < .0001. A correlation also existed between SBOS and OVEST in both trials (β = 2.1, P < .0001; and β = 1.9, P = .0002). Conclusions. This study indicates a significantly higher improvement in laparoscopic suturing skills in novices training AL skills using NNH compared with MNH. Starting early, AL training in novices using NNH is a feasible option. Furthermore, OVEST used in experimental settings as an evaluation tool is comparable with the validated SBOS.
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Affiliation(s)
- Ninos Oussi
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska University Hospital, Stockholm, Sweden.,Centre for Clinical Research Sörmland, Uppsala University, Eskilstuna, Sweden
| | - Konstantinos Georgiou
- 1st Department of Propaedeutic Surgery, Hippocrateion General Hospital of Athens, Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Andreas Larentzakis
- 1st Department of Propaedeutic Surgery, Hippocrateion General Hospital of Athens, Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios Thanasas
- Medical Physics Lab-Simulation Center (MPLSC), Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Markus Castegren
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska University Hospital, Stockholm, Sweden.,Perioperative medicine and intensive care (PMI), Karolinska University Hospital, Stockholm, Sweden
| | - Evangelos Georgiou
- Medical Physics Lab-Simulation Center (MPLSC), Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Lars Enochsson
- Department of Surgical and Perioperative Sciences, Division of Surgery, Umeå University, Sunderby Research Unit, Umeå, Sweden
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Vaidya A, Aydin A, Ridgley J, Raison N, Dasgupta P, Ahmed K. Current Status of Technical Skills Assessment Tools in Surgery: A Systematic Review. J Surg Res 2020; 246:342-378. [DOI: 10.1016/j.jss.2019.09.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/29/2019] [Accepted: 09/11/2019] [Indexed: 12/18/2022]
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25
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Levin M, McKechnie T, Khalid S, Grantcharov TP, Goldenberg M. Automated Methods of Technical Skill Assessment in Surgery: A Systematic Review. JOURNAL OF SURGICAL EDUCATION 2019; 76:1629-1639. [PMID: 31272846 DOI: 10.1016/j.jsurg.2019.06.011] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 06/04/2019] [Accepted: 06/14/2019] [Indexed: 06/09/2023]
Abstract
OBJECTIVE The goal of the current study is to systematically review the literature addressing the use of automated methods to evaluate technical skills in surgery. BACKGROUND The classic apprenticeship model of surgical training includes subjective assessments of technical skill. However, automated methods to evaluate surgical technical skill have been recently studied. These automated methods are a more objective, versatile, and analytical way to evaluate a surgical trainee's technical skill. STUDY DESIGN A literature search of the Ovid Medline, Web of Science, and EMBASE Classic databases was performed. Articles evaluating automated methods for surgical technical skill assessment were abstracted. The quality of all included studies was assessed using the Medical Education Research Study Quality Instrument. RESULTS A total of 1715 articles were identified, 76 of which were selected for final analysis. An automated methods pathway was defined that included kinetics and computer vision data extraction methods. Automated methods included tool motion tracking, hand motion tracking, eye motion tracking, and muscle contraction analysis. Finally, machine learning, deep learning, and performance classification were used to analyse these methods. These methods of surgical skill assessment were used in the operating room and simulated environments. The average Medical Education Research Study Quality Instrument score across all studies was 10.86 (maximum score of 18). CONCLUSIONS Automated methods for technical skill assessment is a growing field in surgical education. We found quality studies evaluating these techniques across many environments and surgeries. More research must be done to ensure these techniques are further verified and implemented in surgical curricula.
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Affiliation(s)
- Marc Levin
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada.
| | - Tyler McKechnie
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Shuja Khalid
- Surgical Safety Technologies, Li Ka Shing International Knowledge Institute, Toronto, Ontario, Canada
| | - Teodor P Grantcharov
- Surgical Safety Technologies, Li Ka Shing International Knowledge Institute, Toronto, Ontario, Canada; Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Mitchell Goldenberg
- Surgical Safety Technologies, Li Ka Shing International Knowledge Institute, Toronto, Ontario, Canada; Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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Papandria D, Fisher JG, Kenney BD, Dykes M, Nelson A, Diefenbach KA. Orientation in Perpetuity: An Online Clinical Decision Support System for Surgical Residents. J Surg Res 2019; 245:649-655. [PMID: 31542695 DOI: 10.1016/j.jss.2019.05.063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/17/2019] [Accepted: 05/30/2019] [Indexed: 11/15/2022]
Abstract
BACKGROUND Limiting variability is an essential element to improving quality of care. Frequent resident turnover represents a significant barrier to clinical standardization. Trainees joining new surgical services must familiarize themselves with the guidelines and protocols that direct patient care as well as their learning objectives and expectations. A clinical decision support system (CDSS) is a dynamic, searchable electronic resource intended for use at the point of care. The CDSS can provide convenient and timely access to relevant information for residents, allowing them to incorporate the most up-to-date protocols and guidelines in their daily care of patients. The objective of this quality improvement intervention was to determine the objective rate of CDSS utilization and its subjective value to residents. MATERIALS AND METHODS An internally developed, web-based CDSS including essential, clinically useful documents was created for use by trainees on a busy pediatric surgery service. A standardized orientation was provided to each resident and fellow on joining the service, complemented by a summary card to be attached to the trainee's ID badge. CDSS usage was monitored using web analytics. Trainees who rotated before and after the CDSS launch were surveyed regarding attitudes toward clinical resources and confidence in patient management. RESULTS Documents published to the CDSS included 33 clinical guideline documents and 207 additional educational and support files including reference materials from service orientation were made available to trainees and staff. Goals for resident usage were established by evaluation and adaptation of early traffic patterns. Analysis of web traffic collected over 14 consecutive months revealed utilization above target levels, with 4.0 average weekly page views per trainee (IQR: 1.6-5.6). A total of 60 survey responses were received (54% of trainees invited); majorities of rotating trainees and survey respondents were trainees in general surgery and most were interns. Mean composite scores reflected a trend toward improved satisfaction when seeking CDSM (before intervention 3.18 [SD 0.73], after intervention 3.92 [SD 0.70], range 1-5) which was statistically significant (P = 0.005). Mean scores also improved across five of six components of the composite score (mean improvement 0.75, range: 0.53-0.92), four of which were statistically significant (P = 0.001-0.038). Most (59%) respondents reported that they used the CDSS frequently. CONCLUSIONS Convenient access to a CDSS resulted in greater than expected utilization as well as higher resident satisfaction with and confidence in materials provided. A CDSS is a promising tool offering quick access to high-quality information in challenging trainee environments.
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Affiliation(s)
- Dominic Papandria
- Department of Surgery, Emory University School of Medicine, Atlanta, Georgia
| | - Jeremy G Fisher
- Department of Surgery, Nationwide Children's Hospital, Columbus, Ohio
| | - Brian D Kenney
- Department of Surgery, Nationwide Children's Hospital, Columbus, Ohio
| | - Michael Dykes
- Department of Surgery, Nationwide Children's Hospital, Columbus, Ohio
| | - Abigail Nelson
- Department of Surgery, Nationwide Children's Hospital, Columbus, Ohio
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Nguyen XA, Ljuhar D, Pacilli M, Nataraja RM, Chauhan S. Surgical skill levels: Classification and analysis using deep neural network model and motion signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:1-8. [PMID: 31319938 DOI: 10.1016/j.cmpb.2019.05.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 04/11/2019] [Accepted: 05/11/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectively identify the actual skills level of a junior trainee is highly desirable. This study aims to design an automated surgical skills evaluation system. METHODS We propose to use a deep neural network model that can analyze raw surgical motion data with minimal preprocessing. A platform with inertial measurement unit sensors was developed and participants with different levels of surgical experience were recruited to perform core open surgical skills tasks. JIGSAWS a publicly available robot based surgical training dataset was used to evaluate the generalization of our deep network model. 15 participants (4 experts, 4 intermediates and 7 novices) were recruited into the study. RESULTS The proposed deep model achieved an accuracy of 98.2%. With comparison to JIGSAWS; our method outperformed some existing approaches with an accuracy of 98.4%, 98.4% and 94.7% for suturing, needle-passing, and knot-tying, respectively. The experimental results demonstrated the applicability of this method in both open surgery and robot-assisted minimally invasive surgery. CONCLUSIONS This study demonstrated the potential ability of the proposed deep network model to learn the discriminative features between different surgical skills levels.
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Affiliation(s)
- Xuan Anh Nguyen
- Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, 3800, Australia
| | - Damir Ljuhar
- Department of Surgical Simulation, Monash Children's Hospital, Melbourne, Australia; Department of Paediatrics, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Maurizio Pacilli
- Department of Surgical Simulation, Monash Children's Hospital, Melbourne, Australia; Department of Paediatrics, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Ramesh Mark Nataraja
- Department of Surgical Simulation, Monash Children's Hospital, Melbourne, Australia; Department of Paediatrics, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Sunita Chauhan
- Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, 3800, Australia.
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Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR MEDICAL EDUCATION 2019; 5:e13930. [PMID: 31199295 PMCID: PMC6598417 DOI: 10.2196/13930] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 01/08/2023]
Abstract
Background Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. Objective This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. Methods Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. Results A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students’ learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. Conclusions The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.
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Affiliation(s)
- Kai Siang Chan
- Medical Education Scholarship and Research Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Nabil Zary
- Medical Education Scholarship and Research Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
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Gavrilovic B, Fahy AS, Carrillo B, Nasr A, Gerstle JT, Azzie G. Development of an Open-Source Laparoscopic Simulator Capable of Motion and Force Assessment: High Tech at Low Cost. J Laparoendosc Adv Surg Tech A 2018; 28:1253-1260. [PMID: 29969383 DOI: 10.1089/lap.2018.0126] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Laparoscopic simulators help improve surgical skills in an ex vivo setting. New simulators incorporate force and motion assessment, but often at high financial cost. Our goal is to establish global access to a laparoscopic simulator, which offers both traditional summative assessment (time to task completion and precision) as well as advanced formative assessment (force and motion sensing capabilities) so that educators anywhere may be able to create simulators with increased educational value. DESIGN A low-cost laparoscopic simulator incorporating an off-the-shelf optical sensor, inertial measurement unit, holders, and a housing unit for a microcontroller was integrated into a plastic box with a high-definition digital camera and a three-dimensional mouse. Open source software was developed to offer real-time feedback in force and motion. The system was calibrated for accuracy and consistency. RESULTS The simulator was assembled from off-the-shelf components and open-source software. Total estimated cost was $350 United States Dollars. The mouse was calibrated by applying known forces in known directions. Linear forces measured in all axes showed linear output trends with r2-values of between 0.988 and 0.999. Accuracy in motion evaluation was evaluated and this demonstrated low average errors in the motion sensors of 5.4% to 6.8%. CONCLUSIONS This low-cost, off-the-shelf, open-access laparoscopic simulator provides accurate and consistent measures of force and motion. We believe that collaborative efforts between surgeons and engineers can allow the creation of these surgical teaching devices at a reasonable cost such that they can be used in resource-rich and resource-limited settings.
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Affiliation(s)
- Bojan Gavrilovic
- 1 Institute of Biomaterials and Biomedical Engineering, University of Toronto , Toronto, Canada
| | - Aodhnait S Fahy
- 2 Division of General and Thoracic Surgery, The Hospital for Sick Children , Toronto, Canada
| | | | - Ahmed Nasr
- 3 Department of Surgery, Children's Hospital of Eastern Ontario, University of Ottawa , Ottawa, Canada
| | - Justin T Gerstle
- 2 Division of General and Thoracic Surgery, The Hospital for Sick Children , Toronto, Canada
| | - Georges Azzie
- 2 Division of General and Thoracic Surgery, The Hospital for Sick Children , Toronto, Canada
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