Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation

With the development of language research and language teaching, people realize that grammatical competence is an important part of communicative competence. In foreign language teaching, grammar teaching is not only necessary but also the main way to achieve the goal of communicative competence. This article mainly studies the virtual reality technology college English immersive context teaching method based on artificial intelligence and machine learning. The purpose is to improve students’ English learning ability. Through the comparative teaching experiment of two classes of freshmen in a university, the experimental class conducted VR technology-based immersive virtual context teaching from the perspective of constructivism, while the control class adopted common multimedia equipment and traditional teaching methods. In the classroom, teachers occupy most of the time, students only passively receive a lot of information from teachers, they have little chance to participate in the exchange of information and express ideas in the target language, and most of the time they are “immersed” in the Chinese environment. The overall English level was also better than that of the control class, with an average score of 2.8 points higher. This shows that college English immersive context teaching combining constructivism theory and VR technology can indeed improve students’ English level.

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Best Practices for Artificial Intelligence in Life Sciences Research

10.31219/osf.io/eqm9j ◽

2020 ◽

Author(s):

Vladimir Makarov ◽

Terry Stouch ◽

Brandon Allgood ◽

Christopher Willis ◽

Nick Lynch

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Best Practices ◽

Life Sciences ◽

Organizational Management ◽

Biotechnology Research

We describe 11 best practices for the successful use of Artificial Intelligence and Machine Learning in the pharmaceutical and biotechnology research, on the data, technology, and organizational management levels.

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Virtual reality in interventional radiology education: a systematic review

Radiologia Brasileira ◽

10.1590/0100-3984.2020.0162 ◽

2021 ◽

Vol 54 (4) ◽

pp. 254-260

Author(s):

And Yara Particelli Gelmini ◽

Márcio Luís Duarte ◽

André Moreira de Assis ◽

Josias Bueno Guimarães Junior ◽

Francisco César Carnevale

Keyword(s):

Systematic Review ◽

Medical Education ◽

Virtual Reality ◽

Interventional Radiology ◽

Latin American ◽

Skill Acquisition ◽

Randomized Clinical Trials ◽

Quality Criteria ◽

Cochrane Library ◽

Virtual Reality Simulation

Abstract The aim of this study was to compare virtual reality simulation with other methods of teaching interventional radiology. We searched multiple databases-Cochrane Library; Medline (PubMed); Embase; Trip Medical; Education Resources Information Center; Cumulative Index to Nursing and Allied Health Literature; Scientific Electronic Library Online; and Latin-American and Caribbean Health Sciences Literature-for studies comparing virtual reality simulation and other methods of teaching interventional radiology. This systematic review was performed in accordance with the criteria established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Best Evidence Medical Education (BEME) Collaboration. Eligible studies were evaluated by using the quality indicators provided in the BEME Guide No. 11 and the Kirkpatrick model of training evaluation. After the eligibility and quality criteria had been applied, five randomized clinical trials were included in the review. The Kirkpatrick level of impact varied among the studies evaluated, three studies being classified as level 2B and two being classified as level 4B. Among the studies evaluated, there was a consensus that virtual reality aggregates concepts and is beneficial for the teaching of interventional radiology. Although the use of virtual reality has been shown to be effective for skill acquisition and learning in interventional radiology, there is still a lack of studies evaluating and standardizing the employment of this technology in relation to the numerous procedures that exist within the field of expertise.

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3D Animation and Virtual Reality Integrated Cognitive Computing for Teaching and Learning in Higher Education

10.3233/apc210252 ◽

2021 ◽

Author(s):

Abhishek Kumar ◽

Rini Dey ◽

G. Madhukar Rao ◽

Saravanan Pitchai ◽

K. Vengatesan ◽

...

Keyword(s):

Artificial Intelligence ◽

Medical Education ◽

Virtual Reality ◽

Teaching And Learning ◽

3D Animation ◽

Multimodal Approach ◽

Cognitive Computing ◽

Teaching Procedure

This paper proposes the and justify how we can enhance the quality of medical education through immersive learning and AI (Artificial Intelligence) use in education. A Multimodal Approach for Immersive Teaching and learning through Animation, AR (Augmented Reality) & VR (Virtual Reality) is aimed at providing specifically medical students with knowledge, skills, and understanding. It is important to understand the current challenge involved in medical education. This paper reports the findings of a novel study on the technology enable teaching with Animation, AR and VR by and MR impact. A case study was conducted involving 521 participants from different states of India. The data was analyzed by their feedback after using this Virtual reality-based teaching procedure in classroom. Recommendations from this paper that are expected to effectively improving the quality of medical education in faster way.

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Resuscitating Cardiopulmonary Resuscitation Training in a Virtual Reality: Prospective Interventional Study

Journal of Medical Internet Research ◽

10.2196/22920 ◽

2021 ◽

Vol 23 (7) ◽

pp. e22920

Author(s):

Janaya Elizabeth Perron ◽

Michael Jonathon Coffey ◽

Andrew Lovell-Simons ◽

Luis Dominguez ◽

Mark E King ◽

...

Keyword(s):

Medical Education ◽

Virtual Reality ◽

Medical Students ◽

Cardiopulmonary Resuscitation ◽

Testing Session ◽

Virtual Reality Simulation ◽

Game Play ◽

Participant Satisfaction ◽

Educational Efficacy ◽

Beta Testing

Background Simulation-based technologies are emerging to enhance medical education in the digital era. However, there is limited data for the use of virtual reality simulation in pediatric medical education. We developed Virtual Doc as a highly immersive virtual reality simulation to teach pediatric cardiopulmonary resuscitation skills to medical students. Objective The primary objectives of this study were to evaluate participant satisfaction and perceived educational efficacy of Virtual Doc. The secondary aim of this study was to assess the game play features of Virtual Doc. Methods We conducted a prospective closed beta-testing study at the University of New South Wales (Sydney, Australia) in 2018. All medical students from the 6-year undergraduate program were eligible to participate and were recruited through voluntary convenience sampling. Participants attended a 1-hour testing session and attempted at least one full resuscitation case using the virtual reality simulator. Following this, participants were asked to complete an anonymous postsession questionnaire. Responses were analyzed using descriptive statistics. Results A total of 26 participants were recruited, consented to participate in this study, and attended a 1-hour in-person closed beta-testing session, and 88% (23/26) of participants completed the anonymous questionnaire and were included in this study. Regarding participant satisfaction, Virtual Doc was enjoyed by 91% (21/23) of participants, with 74% (17/23) intending to recommend the simulation to a colleague and 66% (15/23) intending to recommend the simulation to a friend. In assessment of the perceived educational value of Virtual Doc, 70% (16/23) of participants agreed they had an improved understanding of cardiopulmonary resuscitation, and 78% (18/23) agreed that Virtual Doc will help prepare for and deal with real-life clinical scenarios. Furthermore, 91% (21/23) of participants agreed with the development of additional Virtual Doc cases as beneficial for learning. An evaluation of the game play features as our secondary objective revealed that 70% (16/23) of participants agreed with ease in understanding how to use Virtual Doc, and 74% (17/23) found the game play elements useful in understanding cardiopulmonary resuscitation. One-third (7/23, 30%) found it easy to work with the interactive elements. In addition, 74% (17/23) were interested in interacting with other students within the simulation. Conclusions Our study demonstrates a positive response regarding trainee satisfaction and perceived educational efficacy of Virtual Doc. The simulation was widely accepted by the majority of users and may have the potential to improve educational learning objectives.

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Multiomics, virtual reality and artificial intelligence in heart failure

Future Cardiology ◽

10.2217/fca-2020-0225 ◽

2021 ◽

Author(s):

Patrick A Gladding ◽

Suzanne Loader ◽

Kevin Smith ◽

Erica Zarate ◽

Saras Green ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Heart Failure ◽

Virtual Reality ◽

Mental Stress ◽

Longitudinal Strain ◽

Stress Test ◽

Global Longitudinal Strain ◽

Area Under The Curve ◽

Left Ventricular

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography–mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85–0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

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Best practices for authors of healthcare-related artificial intelligence manuscripts

npj Digital Medicine ◽

10.1038/s41746-020-00336-w ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Sujay Kakarmath ◽

Andre Esteva ◽

Rima Arnaout ◽

Hugh Harvey ◽

Santosh Kumar ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Best Practices ◽

Training Data ◽

Health State ◽

Fitness Level ◽

The Past ◽

Digital Medicine ◽

Disproportionate Number ◽

Limited Generalizability

Abstract Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learning, convoluted neural networks) and the use of these algorithms to make predictions that often surpassed prevailing benchmarks. As the discipline has matured, individuals have called attention to aberrancies in the output of these algorithms. In particular, criticisms have been widely circulated that algorithmically developed models may have limited generalizability due to overfitting to the training data and may systematically perpetuate various forms of biases inherent in the training data, including race, gender, age, and health state or fitness level (Challen et al. BMJ Qual. Saf. 28:231–237, 2019; O’neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Broadway Book, 2016). Given our interest in publishing the highest quality papers and the growing volume of submissions using AI algorithms, we offer a list of criteria that authors should consider before submitting papers to npj Digital Medicine.

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Virtual reality simulation: a paradigm shift for therapy and medical education

British Journal of Hospital Medicine ◽

10.12968/hmed.2018.79.12.666 ◽

2018 ◽

Vol 79 (12) ◽

pp. 666-667 ◽

Cited By ~ 3

Author(s):

Branavan Rudran ◽

Kartik Logishetty

Keyword(s):

Medical Education ◽

Virtual Reality ◽

Paradigm Shift ◽

Virtual Reality Simulation

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Machine intelligence today: applications, methodology, and technology

Informatik-Spektrum ◽

10.1007/s00287-021-01343-1 ◽

2021 ◽

Vol 44 (2) ◽

pp. 104-114

Author(s):

Bernhard G. Humm ◽

Hermann Bense ◽

Michael Fuchs ◽

Benjamin Gernhardt ◽

Matthias Hemmje ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Best Practices ◽

Machine Intelligence ◽

Strong Impact ◽

Industrial Manufacturing ◽

Knowledge Based ◽

Current Trends ◽

Culture Education

AbstractMachine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.

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