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Mathew R, Ramakrishnan N, Boland F, Pawlikowska T, Holland JC. Learning cardiac embryology on YouTube-What videos are there to view? ANATOMICAL SCIENCES EDUCATION 2024. [PMID: 39073248 DOI: 10.1002/ase.2467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 12/12/2023] [Accepted: 05/20/2024] [Indexed: 07/30/2024]
Abstract
In modern medical curricula, embryology is typically taught through lectures, with a few institutions providing tutorials. The use of 3-D videos or animations enables students to study these embryological structures and how they change with time. The aim of this study was to assess the quality of cardiac embryology videos available on YouTube. A systematic literature review regarding the use of YouTube in teaching or learning cardiac embryology identified no papers that examined this specific question, and next, a systematic search of YouTube was performed. A total of 1200 cardiac embryology videos were retrieved using 12 specific search terms, with 370 videos retrieved under two or more search terms and excluded. A further 511 videos were excluded under additional, specific criteria. The remaining 319 videos were evaluated with the YouTube Video Assessment Criteria (UTvAC), with 121 rated as "useful." Videos on YouTube are uploaded with a wide audience in mind, from children to cardiologists, and content control is imperfect. Multiple videos were identified as duplicates of videos from original channels, typically without attribution. While 49 videos showed operations or human material, none contained an ethical statement regarding consent, and only 10 of these included an age restriction or graphical advisory. While there are useful videos for medical students studying cardiac embryology on YouTube, intuitive search strategies will also identify many with irrelevant content and of variable quality. Digital competence and search strategies are not innate skills, so educators should teach students to assess information so as to avoid overload or "filter failure."
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Affiliation(s)
- Ruth Mathew
- Department of Obstetrics & Gynaecology, Cork University Maternity Hospital, Cork, Ireland
| | - Niveta Ramakrishnan
- Medical Graduate, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Fiona Boland
- Data Science Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Teresa Pawlikowska
- Health Professions Education Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Jane C Holland
- Department of Anatomy and Regenerative Medicine, RCSI University of Medicine and Health Sciences, Dublin, Ireland
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Sarangi PK, Narayan RK, Mohakud S, Vats A, Sahani D, Mondal H. Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes. Indian J Radiol Imaging 2024; 34:276-282. [PMID: 38549897 PMCID: PMC10972658 DOI: 10.1055/s-0043-1777746] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
Background The field of radiology relies on accurate interpretation of medical images for effective diagnosis and patient care. Recent advancements in artificial intelligence (AI) and natural language processing have sparked interest in exploring the potential of AI models in assisting radiologists. However, limited research has been conducted to assess the performance of AI models in radiology case interpretation, particularly in comparison to human experts. Objective This study aimed to evaluate the performance of ChatGPT, Google Bard, and Bing in solving radiology case vignettes (Fellowship of the Royal College of Radiologists 2A [FRCR2A] examination style questions) by comparing their responses to those provided by two radiology residents. Methods A total of 120 multiple-choice questions based on radiology case vignettes were formulated according to the pattern of FRCR2A examination. The questions were presented to ChatGPT, Google Bard, and Bing. Two residents wrote the examination with the same questions in 3 hours. The responses generated by the AI models were collected and compared to the answer keys and explanation of the answers was rated by the two radiologists. A cutoff of 60% was set as the passing score. Results The two residents (63.33 and 57.5%) outperformed the three AI models: Bard (44.17%), Bing (53.33%), and ChatGPT (45%), but only one resident passed the examination. The response patterns among the five respondents were significantly different ( p = 0.0117). In addition, the agreement among the generative AI models was significant (intraclass correlation coefficient [ICC] = 0.628), but there was no agreement between the residents (Kappa = -0.376). The explanation of generative AI models in support of answer was 44.72% accurate. Conclusion Humans exhibited superior accuracy compared to the AI models, showcasing a stronger comprehension of the subject matter. All three AI models included in the study could not achieve the minimum percentage needed to pass an FRCR2A examination. However, generative AI models showed significant agreement in their answers where the residents exhibited low agreement, highlighting a lack of consistency in their responses.
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Affiliation(s)
- Pradosh Kumar Sarangi
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
| | - Ravi Kant Narayan
- Department of Anatomy, ESIC Medical College & Hospital, Bihta, Patna, Bihar, India
| | - Sudipta Mohakud
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Aditi Vats
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Debabrata Sahani
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Himel Mondal
- Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
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Brown W, Afshari S, Zhou M, Lythgoe J, Walsh R, Hielscher AC. Living and post-mortem CT scans in the gross anatomy lab: A study investigating differences in first-year medical students' exam performance and perceptions. ANATOMICAL SCIENCES EDUCATION 2024; 17:468-482. [PMID: 38213130 DOI: 10.1002/ase.2371] [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: 07/13/2023] [Revised: 12/07/2023] [Accepted: 12/09/2023] [Indexed: 01/13/2024]
Abstract
Basic competency in radiological imaging is essential for physicians to identify and manage diseases. An optimal place in which to include imaging in the medical curriculum is during anatomy as students can correlate the 3D anatomy from their body donors with the 2D cross-sectional anatomy. The goal of this project was to enhance first-year medical students' knowledge of cross-sectional imaging in the gross anatomy lab and to investigate whether there are benefits to learning cross sectional imaging via scans from body donors versus living individuals. Student participant performance was evaluated on laboratory practical examinations, CT image questions and spatial anatomical knowledge in the thorax and abdomen sections of gross anatomy. Students learned the cross-sectional imaging during dissections where they accessed the images relevant to their study on Pacsbin, a web-based Digital Imaging and Communication in Medicine viewer, via iPads. Results showed no statistically significant differences in practical examination scores, spatial anatomical knowledge, or identification of anatomical structures on CT image questions between participants who learned from images on body donors versus living individuals. In a questionnaire given at the end of the course, participants cited that the CT images improved their anatomical and imaging knowledge and that they felt better prepared to use imaging software and interpret diagnostic imaging results upon entering clerkships. While there were no differences in academic performance between the groups, positive outcomes regarding student perceptions of anatomical and imaging knowledge and preparedness for use of imaging software were identified in this study.
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Affiliation(s)
- William Brown
- Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Sam Afshari
- Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Megan Zhou
- Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Jacob Lythgoe
- Department of Radiology, University of Vermont, Burlington, Vermont, USA
| | - Ryan Walsh
- Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
- Department of Radiology, University of Vermont, Burlington, Vermont, USA
| | - Abigail C Hielscher
- Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
- Department of Neurological Sciences, University of Vermont, Burlington, Vermont, USA
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Xiao J. Improving student learning and performance through a continuous flipped anatomy teaching model. ANATOMICAL SCIENCES EDUCATION 2024; 17:219-226. [PMID: 37916536 DOI: 10.1002/ase.2351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023]
Abstract
The flipped classroom teaching model has been widely used in anatomy education to embrace a blended learning strategy. However, the impact that a continuous flipped classroom teaching model exerts on student learning of human topographic anatomy remains unclear. To address this question, student learning experience and performance were compared between third-year undergraduate students who participated in a continuous flipped anatomy classroom teaching since their first year (n = 65, experimental group) and those under the flipped anatomy teaching model only in their third year (n = 45, control group). A five-point Likert scale learning and teaching survey was conducted to evaluate students' perceptions of the flipped classroom model. Students under a continuous flipped classroom teaching model rated significantly higher on the impact of the flipped model on learning difficult anatomy concepts than the control group (*p = 0.0346). The rating on independent learning remained not statistically different between the two groups, although a trend difference was detected (p = 0.0604). Analysis of learning performance revealed that the experimental group significantly outperformed the control group when answering questions focusing on regional anatomy (*p = 0.0207). No significant difference was identified in students' marks of case-based studies between the two groups. In summary, the results of this study indicate that the flipped classroom model implemented over a long term continues to advance students' learning experience and performance in anatomy, indicating a "dose" effect on active learning. The findings of this study will assist the best practices of the flipped anatomy classroom and develop evidence-informed approaches to advance anatomy education in the future.
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Affiliation(s)
- Junhua Xiao
- Department of Health Sciences and Biostatistics, School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
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Mason A, Jang C, Khatskevich K, George Z, Streitmatter C, McGlawn-McGrane B, Dominguez Rieg J. Effectiveness of Near-Peer-Taught Case Reviews on Students' Confidence in National Board of Medical Examiners (NBME) Exams. Cureus 2023; 15:e43661. [PMID: 37719592 PMCID: PMC10505073 DOI: 10.7759/cureus.43661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction A key element to a first-year medical student's (MS1) education is guidance and practice in applying anatomy, pathophysiology, diagnosis, and treatment concepts to clinical vignettes. One potential solution to providing effective clinical reasoning training is the involvement of second-year medical students (MS2s) in small group sessions as teachers to provide more personalized instruction via case-based learning sessions. Near-peer teaching has been shown to benefit students' confidence in learning and improve test scores. Similarly, case-based learning is heavily associated with an improved understanding of complex topics. As such, this study assessed the efficacy of near-peer teaching with concomitant case-based style presentations on improving the comfort of MS1s with their understanding of the curriculum content and their comfort with applying their knowledge to clinical scenarios. Methods This randomized controlled crossover trial included several small-group study sessions, each consisting of five MS1s and led by an MS2 who reviewed selected clinical cases in a standardized slide decks. The control arm was provided the same slide decks but did not participate in the MS2-led sessions. During the first course, students were assigned to either the control or intervention group and then crossed over to the opposite group (control to intervention and vice versa) in every subsequent course. Comfort with the curriculum material was then assessed through pre and post surveys, with the post surveys administered after the MS1s took their final NBME examination for that course. Results The study was cut short due to COVID-19 precautions limiting in-person sessions. Nevertheless, the post survey demonstrated an increased understanding of pathological concepts for the intervention group compared to the control group. Conclusions Future work on near-peer group study sessions should enroll a larger sample size with measures to improve the response rate to better test whether near-peer-led case reviews had a significant effect on students' understanding of anatomical concepts and confidence during NBME examinations.
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Affiliation(s)
- Ashley Mason
- Medical School, University of South Florida Morsani College of Medicine, Tampa, USA
| | - Charles Jang
- Department of Emergency Medicine, Wright State University, Dayton, USA
| | - Katsiaryna Khatskevich
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, USA
| | - Zeegan George
- Medical School, University of South Florida Morsani College of Medicine, Tampa, USA
| | - Caleb Streitmatter
- Medical School, University of South Florida Morsani College of Medicine, Tampa, USA
| | | | - Jessica Dominguez Rieg
- Department of Molecular Pharmacology and Physiology, University of South Florida Morsani College of Medicine, Tampa, USA
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