Introducing Artificial Intelligence Training in Medical Education.
Ontology highlight
ABSTRACT:
SUBMITTER: Paranjape K
PROVIDER: S-EPMC6918207 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature
Publications
Introducing Artificial Intelligence Training in Medical Education.
Paranjape Ketan K Schinkel Michiel M Nannan Panday Rishi R Car Josip J Nanayakkara Prabath P
JMIR medical education 20191203 2
Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this ...[more]
Similar Datasets
Project description:BackgroundSince 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.ObjectiveThis study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education.MethodsMedline (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.ResultsA 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.ConclusionsThe 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.
| S-EPMC6598417 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has affected our day-to-day in a great extent. Healthcare industry is one of the mainstream fields among those and produced a noticeable change in treatment and education. Medical students must comprehend well why AI technologies mediate and frame their decisions on medical issues. Formalizing of instruction on AI concepts can facilitate learners to grasp AI outcomes in association with their sensory perceptions and thinking in the dynamic and ambiguous reality of daily medical practice. The purpose of this study is to provide consensus on the competencies required by medical graduates to be ready for artificial intelligence technologies and possible applications in medicine and reporting the results.Materials and methodsA three-round e-Delphi survey was conducted between February 2020 and November 2020. The Delphi panel accorporated experts from different backgrounds; (i) healthcare professionals/ academicians; (ii) computer and data science professionals/ academics; (iii) law and ethics professionals/ academics; and (iv) medical students. Round 1 in the Delphi survey began with exploratory open-ended questions. Responses received in the first round evaluated and refined to a 27-item questionnaire which then sent to the experts to be rated using a 7-point Likert type scale (1: Strongly Disagree-7: Strongly Agree). Similar to the second round, the participants repeated their assessments in the third round by using the second-round analysis. The agreement level and strength of the consensus was decided based on third phase results. Median scores was used to calculate the agreement level and the interquartile range (IQR) was used for determining the strength of the consensus.ResultsAmong 128 invitees, a total of 94 agreed to become members of the expert panel. Of them 75 (79.8%) completed the Round 1 questionnaire, 69/75 (92.0%) completed the Round 2 and 60/69 (87.0%) responded to the Round 3. There was a strong agreement on the 23 items and weak agreement on the 4 items.ConclusionsThis study has provided a consensus list of the competencies required by the medical graduates to be ready for AI implications that would bring new perspectives to medical education curricula. The unique feature of the current research is providing a guiding role in integrating AI into curriculum processes, syllabus content and training of medical students.
| S-EPMC9302857 | biostudies-literature
Project description:BACKGROUND:The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. OBJECTIVE:The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. METHODS:A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. RESULTS:This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. CONCLUSIONS:To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
| S-EPMC7367541 | biostudies-literature
Project description:Student tutorials are now firmly anchored in medical education. However, to date there have only been isolated efforts to establish structured teacher training for peer tutors in medicine. To close this gap, a centralized tutor training program for students, culminating in an academic certificate, was implemented at Heidelberg University Medical School. The program also counts within the scope of the post-graduate Baden-Württemberg Certificate in Academic Teaching (Baden-Württemberg Zertifikat für Hochschuldidaktik).Based on a needs assessment, a modular program comprised of four modules and a total of 200 curricular units was developed in cooperation with the Department for Key Competencies and Higher Education at Heidelberg University and implemented during the 2010 summer semester. This program covers not only topic-specific training sessions, but also independent teaching and an integrated evaluation of the learning process that is communicated to the graduates in the form of structured feedback. In addition, to evaluate the overall concept, semi-structured interviews (N=18) were conducted with the program graduates.To date, 495 tutors have been trained in the basic module on teaching medicine, which is rated with a mean overall grade of 1.7 (SW: 0.6) and has served as Module I of the program since 2010. A total of 17% (N=83) of these tutors have gone on to enroll in the subsequent training modules of the program; 27 of them (m=12, f=15) have already successfully completed them. Based on qualitative analyses, it is evident that the training program certificate and its applicability toward the advanced teacher training for university instructors pose a major incentive for the graduates. For successful program realization, central coordination, extensive coordination within the medical school, and the evaluation of the attained skills have proven to be of particular importance.The training program contributes sustainably to both quality assurance and professionalism, as well as to solving the issue of resources in medical education. The introduction and continued development of similar programs is desirable.
| S-EPMC4027804 | biostudies-literature
Project description:ObjectivesThe use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum.MethodsAn anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree.ResultsA total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools.ConclusionsThe findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.
| S-EPMC11333820 | biostudies-literature
Project description:IntroductionOver the past decade, medical students have witnessed a decline in the opportunities to perform technical skills during their clinical years. Ultrasound-guided central venous access (USG-CVA) is a critical procedure commonly performed by emergency medicine, anesthesia, and general surgery residents, often during their first month of residency. However, the acquisition of skills required to safely perform this procedure is often deficient upon graduation from medical school. To ameliorate this lack of technical proficiency, ultrasound simulation models have been introduced into undergraduate medical education to train venous access skills. Criticisms of simulation models are the innate lack of realistic tactile qualities, as well as the lack of anatomical variances when compared to living patients. The purpose of our investigation was to design and evaluate a life-like and reproducible training model for USG-CVA using a fresh cadaver.MethodsThis was a cross-sectional study at an urban academic medical center. An 18-point procedural knowledge tool and an 18-point procedural skill evaluation tool were administered during a cadaver lab at the beginning and end of the surgical clerkship. During the fresh cadaver lab, procedure naïve third-year medical students were trained on how to perform ultrasound-guided central venous access of the femoral and internal jugular vessels. Preparation of the fresh cadaver model involved placement of a thin-walled latex tubing in the anatomic location of the femoral and internal jugular vein respectively.ResultsFifty-six third-year medical students participated in this study during their surgical clerkship. The fresh cadaver model provided high quality and lifelike ultrasound images despite numerous cannulation attempts. Technical skill scores improved from an average score of 3 to 12 (p<0.001) and procedural knowledge scores improved from an average score of 4 to 8 (p<0.001).ConclusionThe use of this novel cadaver model prevented extravasation of fluid, maintained ultrasound-imaging quality, and proved to be an effective educational model allowing third-year medical students to improve and maintain their technical skills.
| S-EPMC4899071 | biostudies-literature
Project description:Residency training in medicine lays the foundation for future medical doctors. In real-world settings, training centers face challenges in trying to create balanced residency programs, with cases encountered by residents not always being fairly distributed among them. In recent years, there has been a tremendous advancement in developing artificial intelligence (AI)-based algorithms with human expert guidance for medical imaging segmentation, classification, and prediction. In this paper, we turned our attention from training machines to letting them train us and developed an AI framework for personalised case-based ophthalmology residency training. The framework is built on two components: (1) a deep learning (DL) model and (2) an expert-system-powered case allocation algorithm. The DL model is trained on publicly available datasets by means of contrastive learning and can classify retinal diseases from color fundus photographs (CFPs). Patients visiting the retina clinic will have a CFP performed and afterward, the image will be interpreted by the DL model, which will give a presumptive diagnosis. This diagnosis is then passed to a case allocation algorithm which selects the resident who would most benefit from the specific case, based on their case history and performance. At the end of each case, the attending expert physician assesses the resident's performance based on standardised examination files, and the results are immediately updated in their portfolio. Our approach provides a structure for future precision medical education in ophthalmology.
| S-EPMC10002549 | biostudies-literature
Project description: Not available
| S-EPMC7327358 | biostudies-literature
Project description:PurposeDeveloping robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-chosen data that can prove challenging to collect because of privacy concerns, disease rarity, or diagnostic label quality. Collecting image-based datasets for retinopathy of prematurity (ROP), a potentially blinding disease, suffers from these challenges. Progressively growing generative adversarial networks (PGANs) may help, because they can synthesize highly realistic images that may increase both the size and diversity of medical datasets.DesignDiagnostic validation study of convolutional neural networks (CNNs) for plus disease detection, a component of severe ROP, using synthetic data.ParticipantsFive thousand eight hundred forty-two retinal fundus images (RFIs) collected from 963 preterm infants.MethodsRetinal vessel maps (RVMs) were segmented from RFIs. PGANs were trained to synthesize RVMs with normal, pre-plus, or plus disease vasculature. Convolutional neural networks were trained, using real or synthetic RVMs, to detect plus disease from 2 real RVM test datasets.Main outcome measuresFeatures of real and synthetic RVMs were evaluated using uniform manifold approximation and projection (UMAP). Similarities were evaluated at the dataset and feature level using Fréchet inception distance and Euclidean distance, respectively. CNN performance was assessed via area under the receiver operating characteristic curve (AUC); AUCs were compared via bootstrapping and Delong's test for correlated receiver operating characteristic curves. Confusion matrices were compared using McNemar's chi-square test and Cohen's κ value.ResultsThe CNN trained on synthetic RVMs showed a significantly higher AUC (0.971; P = 0.006 and P = 0.004) and classified plus disease more similarly to a set of 8 international experts (κ = 0.922) than the CNN trained on real RVMs (AUC = 0.934; κ = 0.701). Real and synthetic RVMs overlapped, by plus disease diagnosis, on the UMAP manifold, showing that synthetic images spanned the disease severity spectrum. Fréchet inception distance and Euclidean distances suggested that real and synthetic RVMs were more dissimilar to one another than real RVMs were to one another, further suggesting that synthetic RVMs were distinct from the training data with respect to privacy considerations.ConclusionsSynthetic datasets may be useful for training robust medical AI models. Furthermore, PGANs may be able to synthesize realistic data for use without protected health information concerns.
| S-EPMC9560638 | biostudies-literature
Project description:Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze "training sets" using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved, recent applications and research efforts implementing AI have demonstrated great promise in predicting future injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI.
| S-EPMC7441013 | biostudies-literature