Artificial intelligence for precision education in radiology - experiences in radiology teaching from a UK foundation doctor

Application of artificial intelligence to the public health education

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Analysis of AI Precision Education Strategy for Small Private Online Courses

In recent years, the learning efficacy of online to offline (O2O) teaching methods seems to outperform traditional teaching methods in the field of education. Students can use a small private online course (SPOC) teaching platform to preview class-related materials, learn basic knowledge, and enhance the practical experience of system development in offline courses. The research team applied an artificial intelligence (AI) precision education strategy to design a teaching experiment that evaluated whether this approach may lead to better learning outcomes. In addition to questionnaire surveys to ascertain students' attitudes toward and their satisfaction with learning, this study employed in-depth interviews to understand a potential influence on changes in teachers' curriculum design and teaching approaches when SPOCs was integrated into the traditional university classroom, as well as the impact of the AI precision education model. The results showed that the AI precision education model may facilitate students' learning experience and enhance student achievement.

Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care

Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.

Comparison of Multiple Quantitative Evaluation Indices of Theoretical Knowledge and Clinical Practice Skills and Training of Medical Interns in Cardiovascular Imaging Using Blended Teaching and the Case Resource Network Platform (CRNP)

Background

This study aimed to compare multiple quantitative evaluation indices of levels of theoretical knowledge and clinical practice skills in training medical interns in cardiovascular imaging based on the use of the blended teaching (BT) online artificial intelligence (AI) case resource network platform (CRNP), including time and frequency indices and effectiveness of the CRNP.

Material/Methods

The study included 110 medical interns who were divided into the routine teaching (RT) group (n=55) and the blended teaching (BT) group (n=55). The two were assessed using the mini-clinical evaluation exercise (mini-CEX) that assessed clinical skills, attitudes, and behaviors and using an objective written questionnaire. The following four indices were compared between the RT and BT groups: the X-ray score (XS), the computed tomography angiography (CTA) score (CS), the cardiac magnetic resonance imaging (CMRI) score (MS), and the average score (AS). Seven assessment indicators included: the imaging description (ID), the qualitative diagnosis (QD), the differential diagnosis (DD), examination preparation (EP), interview skill (IS), position display (PD), and human care (HC). Indicators of CRNP use included: number of times (TN), average duration (AD), single maximum duration (SMD), and total duration (TD).

Results

AS significantly correlated with AD (rad=0.761) and TD (rtd=0.754), and showed moderate correlation with TN (rtn=0.595), but weak correlation with SMD (rsmd=0.404).

Conclusions

Levels of theoretical knowledge and clinical practice skills during medical intern training in cardiovascular imaging based on BT using the CRNP teaching technology improved theoretical knowledge and practical skills.