AI-RADS: An Artificial Intelligence Curriculum for Residents

The Roles of Artificial Intelligence in Teaching Anatomy: A Systematic Review

Anatomy education is a cornerstone of medical training and relies on cadaveric dissection and 2D illustrations. Technological advancements and integrated curricula have reduced the focus on detailed anatomy and challenged educators to engage Generation Z learners with interactive, tech-driven methods. Advanced imaging and artificial intelligence (AI) offer a solution, providing virtual dissection simulations and personalized learning tools that mimic 3D anatomy and adapt to individual student needs. Machine learning, a subset of AI, enhances this process by enabling predictive analytics, adaptive feedback, and tailored learning pathways based on performance data, significantly improving anatomical comprehension. Despite its benefits, AI integration raises concerns about over-reliance on technology, biases, and diminished human interaction in training. This review examines AI's transformative potential in anatomy education while emphasizing the need for balanced implementation and ethical oversight. A systematic review following PRISMA guidelines was conducted, utilizing PubMed and backward citation searches. The search yielded 56 studies, with 47 additional articles from citations, resulting in 61 included studies. These explored AI applications such as virtual dissection simulations, machine learning algorithms for adaptive feedback, and gamified learning experiences, which were shown to enhance engagement, personalize learning, and improve anatomical understanding. Concerns about over-reliance on AI and the loss of human interaction were also raised. AI has the potential to enhance anatomy education, but careful consideration of ethical and practical implications is essential. A balanced approach combining traditional methods with AI and robust oversight is crucial for effective integration.

Integrating AI into medical imaging curricula: Insights from UK HEIs

INTRODUCTION

With artificial intelligence (AI) becoming increasingly integrated into medical imaging, the Health and Care Professions Council (HCPC) updated its Standards of Proficiency for Radiographers in Autumn 2023. These changes require clinicians to be both competent and confident in operating AI and related technologies within their role. Responsibility for meeting these standards extends beyond individual clinicians to higher education institutions (HEIs), which play a crucial role in preparing future professionals. This study examines the current and planned provision of AI education for medical imaging students and staff, identifying potential challenges in its implementation.

METHODS

An electronic survey was developed and hosted on the Joint Information Systems Committee (JISC) platform. It was disseminated in April 2023 by the Society of Radiographers to UK HEIs offering medical imaging programmes.

RESULTS

24 HEIs responded, with representation from all four UK nations. Of these, 71 % (n = 17) had already integrated AI into their curriculum. Reported challenges included timetabling constraints and the need to upskill staff. 21 % (n = 5) indicated that AI would be incorporated following course revalidation in the 2024/25 academic year, while the remaining two HEIs were unaware of planned changes.

CONCLUSION

Most UK HEIs have begun integrating AI education into medical imaging programmes. However, significant disparities exist in the depth and scope of AI content across institutions. Further efforts are needed to develop a comprehensive and standardised AI curriculum for medical imaging in the UK.

IMPLICATIONS FOR PRACTICE

This study highlights key areas for improvement in AI education within medical imaging programmes. Further research into content and delivery methods is essential to ensure radiography professionals adequately equipped to navigate the evolving clinical environment.

Integration of artificial intelligence in radiology education: a requirements survey and recommendations from faculty radiologists, residents, and medical students

BACKGROUND

To investigate the perspectives and expectations of faculty radiologists, residents, and medical students regarding the integration of artificial intelligence (AI) in radiology education, a survey was conducted to collect their opinions and attitudes on implementing AI in radiology education.

METHODS

An online questionnaire was used for this survey, and the participant anonymity was ensured. In total, 41 faculty radiologists, 38 residents, and 120 medical students from the authors' institution completed the questionnaire.

RESULTS

Most residents and students experience different levels of psychological stress during the initial stage of clinical practice, and this stress mainly stems from tight schedules, heavy workloads, apprehensions about making mistakes in diagnostic report writing, as well as academic or employment pressures. Although most of the respondents were not familiar with how AI is applied in radiology education, a substantial proportion of them expressed eagerness and enthusiasm for the integration of AI into radiology education. Especially among radiologists and residents, they showed a desire to utilize an AI-driven online platform for practicing radiology skills, including reading medical images and writing diagnostic reports, before engaging in clinical practice. Furthermore, faculty radiologists demonstrated strong enthusiasm for the notion that AI training platforms can enhance training efficiency and boost learners' confidence. Notably, only approximately half of the residents and medical students shared the instructors' optimism, with the remainder expressing neutrality or concern, emphasizing the need for robust AI feedback systems and user-centered designs. Moreover, the authors' team has developed a preliminary framework for an AI-driven radiology education training platform, consisting of four key components: imaging case sets, intelligent interactive learning, self-quiz, and online exam.

CONCLUSIONS

The integration of AI technology in radiology education has the potential to revolutionize the field by providing innovative solutions for enhancing competency levels and optimizing learning outcomes.

Applications of Artificial Intelligence in Medical Education: A Systematic Review

Artificial intelligence (AI) models, like Chat Generative Pre-Trained Transformer (OpenAI, San Francisco, CA), have recently gained significant popularity due to their ability to make autonomous decisions and engage in complex interactions. To fully harness the potential of these learning machines, users must understand their strengths and limitations. As AI tools become increasingly prevalent in our daily lives, it is essential to explore how this technology has been used so far in healthcare and medical education, as well as the areas of medicine where it can be applied. This paper systematically reviews the published literature on the PubMed database from its inception up to June 6, 2024, focusing on studies that used AI at some level in medical education, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Several papers identified where AI was used to generate medical exam questions, produce clinical scripts for diseases, improve the diagnostic and clinical skills of students and clinicians, serve as a learning aid, and automate analysis tasks such as screening residency applications. AI shows promise at various levels and in different areas of medical education, and our paper highlights some of these areas. This review also emphasizes the importance of educators and students understanding AI's principles, capabilities, and limitations before integration. In conclusion, AI has potential in medical education, but more research needs to be done to fully explore additional areas of applications, address the current gaps in knowledge, and its future potential in training healthcare professionals.

Artificial intelligence education in medical imaging: A scoping review

BACKGROUND

The rise of Artificial intelligence (AI) is reshaping healthcare, particularly in medical imaging. In this emerging field, clinical imaging personnel need proper training. However, formal AI education is lacking in medical curricula, coupled with a shortage of studies synthesising the availability of AI curricula tailored for clinical imaging personnel. This study therefore addresses the question "what are the current AI training programs or curricula for clinical imaging personnel?"

METHODS

This review follows Arksey & O'Malley's framework and the PRISMA Extension for Scoping Reviews checklist. Six electronic databases were searched between June and September 2023 and the screening process comprised two stages. Data extraction was performed using a standardised charting form. Data was summarised in table format and thematically.

RESULTS

Twenty-two studies were included in this review. The goals of the curriculum include enhancing AI knowledge through the delivery of educational content and encouraging practical application and skills development in AI. The learning objectives comprise technical proficiency and model development, foundational knowledge and understanding, literature review and information utilisation, and practical application and problem-solving skills. Course content spanned nine areas, from fundamentals of AI to imaging informatics. Most curricula adopted an online mode of delivery, and the program duration varied significantly. All programs utilised didactic presentations, with several incorporating additional teaching methods and activities to fulfil curriculum goals. The target audiences and participants primarily involved radiology residents, while the creators and instructors comprised a multidisciplinary team of radiology and AI personnel. Various tools and resources were utilised, encompassing online courses and cloud-based notebooks. The curricula were well-received by participants, and time constraint emerged as a major challenge.

CONCLUSION

This scoping review provides an overview of the AI educational programs from existing literature to guide future developments in AI educational curricula. Future education efforts should prioritise evidence-based curriculum design, expand training offerings to radiographers, increase content offerings in imaging informatics, and effectively utilise different teaching strategies and training tools and resources in the curriculum.

Factors affecting medical artificial intelligence (AI) readiness among medical students: taking stock and looking forward

BACKGROUND

Measuring artificial intelligence (AI) readiness among medical students is essential to assess how prepared future doctors are to work with AI technology. Therefore, this study aimed to examine the factors influencing AI readiness among medical students at Kermanshah University of Medical Sciences, both by evaluating the current situation and considering future developments.

METHODS

This was a cross-sectional descriptive-analytical study. The statistical population consisted of 800 first- to fifth-year medical students selected through convenient sampling at Kermanshah University of Medical Sciences from November to March 2023. The data collection tools were demographic checklists and Persian version questionnaire of the medical artificial intelligence readiness scale for medical students (MAIRS-MS). The data were analyzed at a significance level of P < 0.05 using independent t-test, and analysis of variance (ANOVA) tests through SPSS-24 software.

RESULTS

Most of the students were male (56.13%). The overall score for medical AI readiness was 70.59 ± 19.24 out of a maximum possible score of 110. Students had the highest mean score of 9.73 ± 2.96 out of 15 in vision and the lowest mean score of 25.74 ± 7.52 out of 40 in ability. The overall mean of AI readiness (71.84 ± 18.27) was higher in females than males (69.62 ± 19.93), but this difference was not significant (p = 0.106). Furthermore, the mean total score of AI readiness increased with the increasing age of the students.

CONCLUSION

Our findings underscore the need to prepare students to work with AI technologies and to provide them with the essential knowledge and skills across different areas of AI. Accordingly, the Kermanshah University of Medical Sciences student's education unit should set up more AI training centers to provide and introduce basic artificial intelligence courses. Moreover, universities should identify the needs of students based on scientific evidence, and the medical education system should design AI training programs in its educational framework in the same direction.

The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review

OBJECTIVE

This narrative review aims to explore the current applications and future prospects of AI within the subfields of oral and maxillofacial surgery (OMS), emphasizing its potential benefits and anticipated challenges.

METHODS

A detailed review of the literature was conducted to evaluate the role of AI in oral and maxillofacial surgery. All domains within OMS were reviewed with a focus on diagnostic, therapeutic and prognostic interventions.

RESULTS

AI has been successfully integrated into surgical specialties to enhance clinical outcomes. In OMS, AI demonstrates potential to improve clinical and administrative workflows in both ambulatory and hospital-based settings. Notable applications include more accurate risk prediction, minimally invasive surgical techniques, and optimized postoperative management.

CONCLUSION

OMS stands to benefit enormously from the integration of AI. However, significant roadblocks, such as ethical concerns, data security, and integration challenges, must be addressed to ensure effective adoption. Further research and innovation are needed to fully realize the potential of AI in this specialty.

Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study

Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development

OBJECTIVES

Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry perspectives, this paper provides recommendations for medical imaging undergraduate education, including curriculum revision and re-alignment.

KEY FINDINGS

A proposed modular framework is outlined to assist course providers in integrating AI into university programs. An example course design includes modules on data science fundamentals, machine learning, AI ethics and patient safety, governance and regulation, AI tool evaluation, and clinical applications. A proposal to embed these longitudinally in the curriculum combined with hands-on experience and work-integrated learning will help develop the necessary knowledge of AI and its real-world impacts. Authentic assessment examples reinforce learning, such as critically appraising published research and reflecting on current technologies. Maintenance of an up-to-date curriculum will require a collaborative, multidisciplinary approach involving educators, clinicians, and industry professionals.

CONCLUSION

Integrating AI education into undergraduate medical imaging programs equips future radiographers in an evolving technological landscape. A strategic approach to embedding AI modules throughout degree programs assures students a comprehensive understanding of AI principles, skills in utilising AI tools effectively, and the ability to critically evaluate their implications.

IMPLICATIONS FOR PRACTICE

The practical implementation of undergraduate AI education will prepare radiographers to incorporate these technologies while assuring patient care.

Developing a Canadian artificial intelligence medical curriculum using a Delphi study

The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.

[Characteristics of the new internal resident physicians from Madrid Region, their opinions regarding family and community medicine]

AIM

To evaluate the opinions of the new internal resident physicians (IRP) on family and community medicine (FCM) and professional and personal issues.

METHODS

Anonymous survey of 837 new IRPs in the Madrid Region.

RESULTS

Mean age was 25.6±3.5 years, 525 (62.7%) had a specific subject of FCM during medical school, 799 (95.5%) did FCM practices during their medical degree, and 606 (72.4%) considered relevant to be some months in FCM during their medical residence. Only 103 (12.3%) consider becoming parents during residency, 416 (49.7%) have suffered from anxiety, 99 (11.8%) from depression, and 19 (2.3%) had previous suicidal thoughts. Although 638 (76.2%) have received training in ethical decisions, 345 (41.2%) did not know how to implement these decisions, 120 (14.3%) had studied artificial intelligence and 744 (88.9%) have a positive view of the College of Physicians. CONCLUSIóN: Most new medical residents of Madrid consider a Primary Care rotation relevant during their training, but only 63% have completed specific training in FCM as an undergraduate. A total of 12% reported previous depression and half anxiety.

Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022)

Background

Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted.

Objective

This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education.

Methods

Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer.

Results

A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers.

Conclusions

The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

United States newspaper and online media coverage of artificial intelligence and radiology from 1998 to 2023

OBJECTIVE

To evaluate the frequency and content of media coverage pertaining to artificial intelligence (AI) and radiology in the United States from 1998 to 2023.

METHODS

The ProQuest US Newsstream database was queried for print and online articles mentioning AI and radiology published between January 1, 1998, and March 30, 2023. A Boolean search using terms related to radiology and AI was used to retrieve full text and publication information. One of 9 readers with radiology expertise independently reviewed randomly assigned articles using a standardized scoring system.

RESULTS

379 articles met inclusion criteria, of which 290 were unique and 89 were syndicated articles. Most had a positive sentiment (74 %) towards AI, while negative sentiment was far less common (9 %). Frequency of positive sentiment was highest in articles with a focus on AI and radiology (86 %) and lowest in articles focusing on AI and non-medical topics (55 %). The net impact of AI on radiology was most commonly presented as positive (60 %). Benefits of AI were more frequently mentioned (76 %) than potential harms (46 %). Radiologists were interviewed or quoted in less than one-third of all articles.

CONCLUSION

Portrayal of the impact of AI on radiology in US media coverage was mostly positive, and advantages of AI were more frequently discussed than potential risks. However, articles with a general non-medical focus were more likely to have a negative sentiment regarding the impact of AI on radiology than articles with a more specific focus on medicine and radiology. Radiologists were infrequently interviewed or quoted in media coverage.

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review

BACKGROUND

The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.

OBJECTIVE

The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.

METHODS

We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.

RESULTS

Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.

CONCLUSIONS

This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.11124/JBIES-22-00374.

A review of ophthalmology education in the era of generative artificial intelligence

PURPOSE

To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.

DESIGN

A literature review and analysis of current AI applications and educational programs in ophthalmology.

METHODS

Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.

RESULTS

Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students' education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.

CONCLUSIONS

Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.

Artificial intelligence and clinical anatomical education: Promises and perils

Anatomy educators are often at the forefront of adopting innovative and advanced technologies for teaching, such as artificial intelligence (AI). While AI offers potential new opportunities for anatomical education, hard lessons learned from the deployment of AI tools in other domains (e.g., criminal justice, healthcare, and finance) suggest that these opportunities are likely to be tempered by disadvantages for at least some learners and within certain educational contexts. From the perspectives of an anatomy educator, public health researcher, medical ethicist, and an educational technology expert, this article examines five tensions between the promises and the perils of integrating AI into anatomy education. These tensions highlight the ways in which AI is currently ill-suited for incorporating the uncertainties intrinsic to anatomy education in the areas of (1) human variations, (2) healthcare practice, (3) diversity and social justice, (4) student support, and (5) student learning. Practical recommendations for a considered approach to working alongside AI in the contemporary (and future) anatomy education learning environment are provided, including enhanced transparency about how AI is integrated, AI developer diversity, inclusion of uncertainty and anatomical variations within deployed AI, provisions made for educator awareness of AI benefits and limitations, building in curricular "AI-free" time, and engaging AI to extend human capacities. These recommendations serve as a guiding framework for how the clinical anatomy discipline, and anatomy educators, can work alongside AI, and develop a more nuanced and considered approach to the role of AI in healthcare education.

A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff

INTRODUCTION

Medical imaging is arguably the most technologically advanced field in healthcare, encompassing a range of technologies which continually evolve as computing power and human knowledge expand. Artificial Intelligence (AI) is the next frontier which medical imaging is pioneering. The rapid development and implementation of AI has the potential to revolutionise healthcare, however, to do so, staff must be competent and confident in its application, hence AI readiness is an important precursor to AI adoption. Research to ascertain the best way to deliver this AI-enabled healthcare training is in its infancy. The aim of this scoping review is to compare existing studies which investigate and evaluate the efficacy of AI educational interventions for medical imaging staff.

METHODS

Following the creation of a search strategy and keyword searches, screening was conducted to determine study eligibility. This consisted of a title and abstract scan, then subsequently a full-text review. Articles were included if they were empirical studies wherein an educational intervention on AI for medical imaging staff was created, delivered, and evaluated.

RESULTS

Of the initial 1309 records returned, n = 5 (∼0.4 %) of studies met the eligibility criteria of the review. The curricula and delivery in each of the five studies shared similar aims and a 'flipped classroom' delivery was the most utilised method. However, the depth of content covered in the curricula of each varied and measured outcomes differed greatly.

CONCLUSION

The findings of this review will provide insights into the evaluation of existing AI educational interventions, which will be valuable when planning AI education for healthcare staff.

IMPLICATIONS FOR PRACTICE

This review highlights the need for standardised and comprehensive AI training programs for imaging staff.

AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study

BACKGROUND

Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty.

OBJECTIVE

We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption.

METHODS

This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected.

RESULTS

This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential.

CONCLUSIONS

Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.

Effect of virtual reality training to enhance laparoscopic assistance skills

BACKGROUND

While laparoscopic assistance is often entrusted to less experienced individuals, such as residents, medical students, and operating room nurses, it is important to note that they typically receive little to no formal laparoscopic training. This deficiency can lead to poor visibility during minimally invasive surgery, thus increasing the risk of errors. Moreover, operating room nurses and medical students are currently not included as key users in structured laparoscopic training programs.

OBJECTIVES

The aim of this study is to evaluate the laparoscopic skills of OR nurses, clinical medical postgraduate students, and residents before and after undergoing virtual reality training. Additionally, it aimed to compare the differences in the laparoscopic skills among different groups (OR nurses/Students/Residents) both before and after virtual reality training.

METHODS

Operating room nurses, clinical medical postgraduate students and residents from a tertiary Grade A hospital in China in March 2022 were selected as participants. All participants were required to complete a laparoscopic simulation training course in 6 consecutive weeks. One task from each of the four training modules was selected as an evaluation indicator. A before-and-after self-control study was used to compare the basic laparoscopic skills of participants, and laparoscopic skill competency was compared between the groups of operating room nurses, clinical medical postgraduate students, and residents.

RESULTS

Twenty-seven operating room nurses, 31 clinical medical postgraduate students, and 16 residents were included. The training course scores for the navigation training module, task training module, coordination training module, and surgical skills training module between different groups (operating room nurses/clinical medical postgraduate/residents) before laparoscopic simulation training was statistically significant (p < 0.05). After laparoscopic simulation training, there was no statistically significant difference in the training course scores between the different groups. The surgical level scores before and after the training course were compared between the operating room nurses, clinical medical postgraduate students, and residents and showed significant increases (p < 0.05).

CONCLUSION

Our findings show a significant improvement in laparoscopic skills following virtual surgery simulation training across all participant groups. The integration of virtual surgery simulation technology in surgical training holds promise for bridging the gap in laparoscopic skill development among health care professionals.

An evidence-based approach to artificial intelligence education for medical students: A systematic review

The exponential growth of artificial intelligence (AI) in the last two decades has been recognized by many as an opportunity to improve the quality of patient care. However, medical education systems have been slow to adapt to the age of AI, resulting in a paucity of AI-specific education in medical schools. The purpose of this systematic review is to evaluate the current evidence-based recommendations for the inclusion of an AI education curriculum in undergraduate medicine. Six databases were searched from inception to April 23, 2022 for cross sectional and cohort studies of fair quality or higher on the Newcastle-Ottawa scale, systematic, scoping, and integrative reviews, randomized controlled trials, and Delphi studies about AI education in undergraduate medical programs. The search yielded 991 results, of which 27 met all the criteria and seven more were included using reference mining. Despite the limitations of a high degree of heterogeneity among the study types and a lack of follow-up studies evaluating the impacts of current AI strategies, a thematic analysis of the key AI principles identified six themes needed for a successful implementation of AI in medical school curricula. These themes include ethics, theory and application, communication, collaboration, quality improvement, and perception and attitude. The themes of ethics, theory and application, and communication were further divided into subthemes, including patient-centric and data-centric ethics; knowledge for practice and knowledge for communication; and communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination. Based on the survey studies, medical professionals and students, who generally have a low baseline knowledge of AI, have been strong supporters of adding formal AI education into medical curricula, suggesting more research needs to be done to push this agenda forward.

Medical Education in the Era of Advancing Technology

Technology is developing rapidly and affecting the field of medicine in two main areas- medical education and health care. As a rapidly evolving field with the need and ability to constantly incorporate newer technologies, medical education must be able to prepare future doctors as per changing trends in practice patterns, the role of medicine in disease diagnosis and treatment, and innovations, and advances in medical science. In this article, we discuss the various digital learning tools introduced into medical education, as well as their advantages and disadvantages. We also try to understand how the shift to artificial intelligence may affect medical education and practice and how we can make technology efficient without losing the human dimension in doctor-patient relationships.

Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum focused on pediatric radiology is needed to offer targeted education material on AI model development and performance evaluation. There are inherent differences between pediatric and adult practice patterns, which may hinder the application of adult AI models in pediatric cohorts. Such differences include the different imaging modality utilization, imaging acquisition parameters, lower radiation doses, the rapid growth of children and changes in their body composition, and the presence of unique pathologies and diseases, which differ in prevalence from adults. Thus, to enhance radiologists' knowledge of the applications of AI models in pediatric patients, curricula should be structured keeping in mind the unique pediatric setting and its challenges, along with methods to overcome these challenges, and pediatric-specific data governance and ethical considerations. In this report, the authors highlight the salient aspects of pediatric radiology that are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation.

Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students

BACKGROUND

Artificial intelligence's advancement in medicine and its worldwide implementation will be one of the main elements of medical education in the coming years. This study aimed to translate and psychometric evaluation of the Persian version of the medical artificial intelligence readiness scale for medical students.

METHODS

The questionnaire was translated according to a backward-forward translation procedure. Reliability was assessed by calculating Cronbach's alpha coefficient. Confirmatory Factor Analysis was conducted on 302 medical students. Content validity was evaluated using the Content Validity Index and Content Validity Ratio.

RESULTS

The Cronbach's alpha coefficient for the whole scale was found to be 0.94. The Content Validity Index was 0.92 and the Content Validity Ratio was 0.75. Confirmatory factor analysis revealed a fair fit for four factors: cognition, ability, vision, and ethics.

CONCLUSION

The Persian version of the medical artificial intelligence readiness scale for medical students consisting of four factors including cognition, ability, vision, and ethics appears to be an almost valid and reliable instrument for the evaluation of medical artificial intelligence readiness.

Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education

RATIONALE AND OBJECTIVES

To evaluate the effectiveness of an artificial intelligence (AI) in radiology literacy course on participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States.

MATERIALS AND METHODS

A week-long AI in radiology course was developed and included participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States. Ten 30 minutes lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by four different subspecialties, and special topics lectures on the economics of AI, ethics of AI, algorithm bias, and medicolegal implications of AI in medicine. A proctored hands-on clinical AI session allowed participants to directly use an FDA cleared AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants' knowledge of AI terminology and applications and interest in AI education.

RESULTS

There were an average of 75 participants each day of the course (range: 50-120). Nearly all participants reported a lack of sufficient exposure to AI in their radiology training (96.7%, 90/93). Mean participant score on the pre-course AI knowledge evaluation was 8.3/15, with a statistically significant increase to 10.1/15 on the post-course evaluation (p= 0.04). A majority of participants reported an interest in continued AI in radiology education in the future (78.6%, 22/28).

CONCLUSION

A multi-institutional AI in radiology literacy course successfully improved AI education of participants, with the majority of participants reporting a continued interest in AI in radiology education in the future.

The Impact of Emerging Technologies on Residency Selection by Medical Students in 2017 and 2021, With a Focus on Diagnostic Radiology

RATIONALE AND OBJECTIVES

We sought to determine the perceived impact of artificial intelligence (AI) and other emerging technologies (ET) on various specialties by medical students in both 2017 and 2021 and how this might affect their residency selections.

MATERIALS AND METHODS

We conducted a brief, anonymous survey of all medical students at a single institution in 2017 and 2021. Survey questions evaluated (1) incentives motivating residency selection and career path, (2) degree of interest in each specialty, (3) perceived effect that ET will have on job prospects for each specialty, and (4) those specialties that students would not consider because of concerns regarding ET.

RESULTS

A total of 72% (384/532) and 54% (321/598) of medical students participated in the survey in 2017 and 2021, respectively, and results were largely stable. Students perceived ET would reduce job prospects for pathology, diagnostic radiology, and anesthesiology, and enhance prospects for all other specialties (p < 0.01) except dermatology. For both surveys, 23% of students would NOT consider diagnostic radiology because ET would make it obsolete, higher than all other specialties (p < 0.01). Regarding the one student class that was surveyed twice, 50% felt ET would reduce job prospects for radiology in 2017, increasing to 71% in 2021 (p < 0.01), and similar percentages-20% in 2017 and 23% in 2021-said they explicitly would not consider radiology because of concerns levied by ET.

CONCLUSIONS

Current perceptions of ET likely affect residency selection for a large proportion of medical students and may impact the future of various specialties, particularly diagnostic radiology.

Artificial intelligence in radiology: trainees want more

AIM

To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education.

MATERIALS AND METHODS

A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes.

RESULTS

The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues.

CONCLUSION

Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.

Artificial Intelligence Curriculum Needs Assessment for a Pediatric Radiology Fellowship Program: What, How, and Why?

RATIONALE AND OBJECTIVES

Artificial intelligence (AI) holds enormous potential for improvements in patient care, efficiency, and innovation in pediatric radiology practice. Although there is a pressing need for a radiology-specific training curriculum and formalized AI teaching, few resources are available. The purpose of our study was to perform a needs assessment for the development of an AI curriculum during pediatric radiology training and continuing education.

MATERIALS AND METHODS

A focus group study using a semistructured moderator-guided interview was conducted with radiology trainees' and attending radiologists' perceptions of AI, perceived competence in interpretation of AI literature, and perceived expectations from radiology AI educational programs. The focus group was audio-recorded, transcribed, and thematic analysis was performed.

RESULTS

The focus group was held virtually with seven participants. The following themes we identified: (1) AI knowledge, (2) previous training, (3) learning preferences, (4) AI expectations, and (5) AI concerns. The participants had no previous formal training in AI and variability in perceived needs and interests. Most preferred a case-based approach to teaching AI. They expressed incomplete understanding of AI hindered its clinical applicability and reiterated a need for improved training in the interpretation and application of AI literature in their practice.

CONCLUSION

We found heterogeneity in perspectives about AI; thus, a curriculum must account for the wide range of these interests and needs. Teaching the interpretation of AI research methods, literature critique, and quality control through implementation of specific scenarios could engage a variety of trainees from different backgrounds and interest levels while ensuring a baseline level of competency in AI.

AI-RADS: Successes and challenges of a novel artificial intelligence curriculum for radiologists across different delivery formats

Introduction

Artificial intelligence and data-driven predictive modeling have become increasingly common tools integrated in clinical practice, heralding a new chapter of medicine in the digital era. While these techniques are poised to affect nearly all aspects of medicine, medical education as an institution has languished behind; this has raised concerns that the current training infrastructure is not adequately preparing future physicians for this changing clinical landscape. Our institution attempted to ameliorate this by implementing a novel artificial intelligence in radiology curriculum, "AI-RADS," in two different educational formats: a 7-month lecture series and a one-day workshop intensive.

Methods

The curriculum was structured around foundational algorithms within artificial intelligence. As most residents have little computer science training, algorithms were initially presented as a series of simple observations around a relatable problem (e.g., fraud detection, movie recommendations, etc.). These observations were later re-framed to illustrate how a machine could apply the underlying concepts to perform clinically relevant tasks in the practice of radiology. Secondary lessons in basic computing, such as data representation/abstraction, were integrated as well. The lessons were ordered such that these algorithms were logical extensions of each other. The 7-month curriculum consisted of seven lectures paired with seven journal clubs, resulting in an AI-focused session every two weeks. The workshop consisted of six hours of content modified for the condensed format, with a final integrative activity.

Results

Both formats of the AI-RADS curriculum were well received by learners, with the 7-month version and workshop garnering 9.8/10 and 4.3/5 ratings, respectively, for overall satisfaction. In both, there were increases in perceived understanding of artificial intelligence. In the 7-lecture course, 6/7 lectures achieved statistically significant (P < 0.02) differences, with the final lecture approaching significance (P = 0.07). In the one-day workshop, there was a significant increase in perceived understanding (P = 0.03).

Conclusion

As artificial intelligence becomes further enmeshed in clinical practice, it will become critical for physicians to have a basic understanding of how these tools work. Our AI-RADS curriculum demonstrates that it is successful in increasing learner perceived understanding in both an extended and condensed format.

Teaching artificial intelligence as a fundamental toolset of medicine

Artificial intelligence (AI) is transforming the practice of medicine. Systems assessing chest radiographs, pathology slides, and early warning systems embedded in electronic health records (EHRs) are becoming ubiquitous in medical practice. Despite this, medical students have minimal exposure to the concepts necessary to utilize and evaluate AI systems, leaving them under prepared for future clinical practice. We must work quickly to bolster undergraduate medical education around AI to remedy this. In this commentary, we propose that medical educators treat AI as a critical component of medical practice that is introduced early and integrated with the other core components of medical school curricula. Equipping graduating medical students with this knowledge will ensure they have the skills to solve challenges arising at the confluence of AI and medicine.

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program

Background: Artificial intelligence (AI) has been successfully applied to the screening tasks of fundus diseases. However, few studies focused on the potential of AI to aid medical teaching in the residency training program. This study aimed to evaluate the effectiveness of the AI-based pathologic myopia (PM) identification system in the ophthalmology residency training program and assess the residents' feedback on this system. Materials and Methods: Ninety residents in the ophthalmology department at the Second Affiliated Hospital of Zhejiang University were randomly assigned to three groups. In group A, residents learned PM through an AI-based PM identification system. In group B and group C, residents learned PM through a traditional lecture given by two senior specialists independently. The improvement in resident performance was evaluated by comparing the pre-and post-lecture scores of a specifically designed test using a paired t-test. The difference among the three groups was evaluated by one-way ANOVA. Residents' evaluations of the AI-based PM identification system were measured by a 17-item questionnaire. Results: The post-lecture scores were significantly higher than the pre-lecture scores in group A (p < 0.0001). However, there was no difference between pre-and post-lecture scores in group B (p = 0.628) and group C (p = 0.158). Overall, all participants were satisfied and agreed that the AI-based PM identification system was effective and helpful to acquire PM identification, myopic maculopathy (MM) classification, and "Plus" lesion localization. Conclusion: It is still difficult for ophthalmic residents to promptly grasp the knowledge of identification of PM through a single traditional lecture, while the AI-based PM identification system effectively improved residents' performance in PM identification and received satisfactory feedback from residents. The application of the AI-based PM identification system showed advantages in promoting the efficiency of the ophthalmology residency training program.

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study

BACKGROUND

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine.

OBJECTIVE

We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives.

METHODS

A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence.

RESULTS

We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training.

CONCLUSIONS

The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

Code and Data Sharing Practices in the Radiology Artificial Intelligence Literature: A Meta-Research Study

PURPOSE

To evaluate code and data sharing practices in original artificial intelligence (AI) scientific manuscripts published in the Radiological Society of North America (RSNA) journals suite from 2017 through 2021.

MATERIALS AND METHODS

A retrospective meta-research study was conducted of articles published in the RSNA journals suite from January 1, 2017, through December 31, 2021. A total of 218 articles were included and evaluated for code sharing practices, reproducibility of shared code, and data sharing practices. Categorical comparisons were conducted using Fisher exact tests with respect to year and journal of publication, author affiliation(s), and type of algorithm used.

RESULTS

Of the 218 included articles, 73 (34%) shared code, with 24 (33% of code sharing articles and 11% of all articles) sharing reproducible code. Radiology and Radiology: Artificial Intelligence published the most code sharing articles (48 [66%] and 21 [29%], respectively). Twenty-nine articles (13%) shared data, and 12 of these articles (41% of data sharing articles) shared complete experimental data by using only public domain datasets. Four of the 218 articles (2%) shared both code and complete experimental data. Code sharing rates were statistically higher in 2020 and 2021 compared with earlier years (P < .01) and were higher in Radiology and Radiology: Artificial Intelligence compared with other journals (P < .01).

CONCLUSION

Original AI scientific articles in the RSNA journals suite had low rates of code and data sharing, emphasizing the need for open-source code and data to achieve transparent and reproducible science.Keywords: Meta-Analysis, AI in Education, Machine LearningSupplemental material is available for this article.© RSNA, 2022.

Strengthening education in rehabilitation: Assessment technology and digitalization

Rehabilitation is a discipline increasingly growing around the world due to several reasons, but probably the most important one is aging population and chronicity. A need to harmonize education has been identified, and although several International organizations such as the European Union of Medical Specialists (UEMS) and the International Society of Physical Medicine and Rehabilitation (ISPRM) have defined standards, given the quick growth of new evidence and assessment methods an urge to establish new ones arises. Functional assessment and tools used to do so are key in rehabilitation processes. This comprises self-reported questionnaires, conventional clinical evaluation but more notably high technology assessment methods, such as movement analysis systems, posturography, different types of dynamometers and kinesiologic electromyography among others. More recently, a wide range of wearable systems has been introduced in patient assessment. This is generating many published protocols as well as reliability and validity studies. The objective of this narrative review is to present main assessment technologies relevant to rehabilitation, its situation of this specific area in pre-graduate and post-graduate rehabilitation educational programs, and to elaborate a formative proposal including technological foundations of assessment and also highlighting the importance of solid reliability and validity of assessment methods comprehension. The main objective of this proposal is to provide basic knowledge about rehabilitation and methodologies for outcomes evaluation, including new technologies, to all health professionals, but especially to those who work or will work in the field of Rehabilitation.

Academic Radiology Departments Should Lead Artificial Intelligence Initiatives

RATIONALE AND OBJECTIVES

With a track record of innovation and unique access to digital data, radiologists are distinctly positioned to usher in a new medical era of artificial intelligence (AI).

MATERIALS AND METHODS

In this Perspective piece, we summarize AI initiatives that academic radiology departments should consider related to the traditional pillars of education, research, and clinical excellence, while also introducing a new opportunity for engagement with industry.

RESULTS

We provide early successful examples of each as well as suggestions to guide departments towards future success.

CONCLUSION

Our goal is to assist academic radiology leaders in bringing their departments into the AI era and realizing its full potential in our field.

Radiographers' knowledge, attitudes and expectations of artificial intelligence in medical imaging

INTRODUCTION

Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integrating AI into medical imaging amongst a sample of radiographers and determine the current state of AI education within the community.

METHODS

A cross-sectional online quantitative study targeting radiographers based in Europe was conducted over ten weeks. Captured data included demographical information, participants' perceptions and understanding of AI, expectations of AI and AI-related educational backgrounds. Both descriptive and inferential statistical techniques were used to analyse the obtained data.

RESULTS

A total of 96 valid responses were collected. Of these, 64% correctly identified the true definition of AI from a range of options, but fewer (37%) fully understood the difference between AI, machine learning and deep learning. The majority of participants (83%) agreed they were excited about the advancement of AI, though a level of apprehensiveness remained amongst 29%. A severe lack of education on AI was noted, with only 8% of participants having received AI teachings in their pre-registration qualification.

CONCLUSION

Overall positive attitudes towards AI implementation were observed. The slight apprehension may stem from the lack of technical understanding of AI technologies and AI training within the community. Greater educational programs focusing on AI principles are required to help increase European radiography workforce engagement and involvement in AI technologies.

IMPLICATIONS FOR PRACTICE

This study offers insight into the current perspectives of European based radiographers on AI in radiography to help facilitate the embracement of AI technology and convey the need for AI-focused education within the profession.

Research on the Impacts of Cognitive Style and Computational Thinking on College Students in a Visual Artificial Intelligence Course

Visual programming language is a crucial part of learning programming. On this basis, it is essential to use visual programming to lower the learning threshold for students to learn about artificial intelligence (AI) to meet current demands in higher education. Therefore, a 3-h AI course with an RGB-to-HSL learning task was implemented; the results of which were used to analyze university students from two different disciplines. Valid data were collected for 65 students (55 men, 10 women) in the Science (Sci)-student group and 39 students (20 men, 19 women) in the Humanities (Hum)-student group. Independent sample t-tests were conducted to analyze the difference between cognitive styles and computational thinking. No significant differences in either cognitive style or computational thinking ability were found after the AI course, indicating that taking visual AI courses lowers the learning threshold for students and makes it possible for them to take more difficult AI courses, which in turn effectively helping them acquire AI knowledge, which is crucial for cultivating talent in the field of AI.

The current state of knowledge on imaging informatics: a survey among Spanish radiologists

Background

There is growing concern about the impact of artificial intelligence (AI) on radiology and the future of the profession. The aim of this study is to evaluate general knowledge and concerns about trends on imaging informatics among radiologists working in Spain (residents and attending physicians). For this purpose, an online survey among radiologists working in Spain was conducted with questions related to: knowledge about terminology and technologies, need for a regulated academic training on AI and concerns about the implications of the use of these technologies.

Results

A total of 223 radiologists answered the survey, of whom 76.7% were attending physicians and 23.3% residents. General terms such as AI and algorithm had been heard of or read in at least 75.8% and 57.4% of the cases, respectively, while more specific terms were scarcely known. All the respondents consider that they should pursue academic training in medical informatics and new technologies, and 92.9% of them reckon this preparation should be incorporated in the training program of the specialty. Patient safety was found to be the main concern for 54.2% of the respondents. Job loss was not seen as a peril by 45.7% of the participants.

Conclusions

Although there is a lack of knowledge about AI among Spanish radiologists, there is a will to explore such topics and a general belief that radiologists should be trained in these matters. Based on the results, a consensus is needed to change the current training curriculum to better prepare future radiologists.

Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers

Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice.

Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem.

Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term “AI” was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®.

Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported.

Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.

Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective

Artificial intelligence (AI) can enhance the efficiency of medical imaging quality control and clinical documentation, provide clinical decision support, and increase image acquisition and processing quality. A clear understanding of the basic tenets of these technologies and their impact will enable nuclear medicine technologists to train for performing advanced imaging tasks. AI-enabled medical devices' anticipated role and impact on routine nuclear medicine workflow (scheduling, quality control, check-in, radiotracer injection, waiting room, image planning, image acquisition, image post-processing) is reviewed in this article. With the assistance of AI, newly compiled patient imaging data can be customized to encompass personalized risk assessments of patients' disease burden, along with the development of individualized treatment plans. Nuclear medicine technologists will continue to play a crucial role on the medical team, collaborating with patients and radiologists to improve each patient's imaging experience and supervising the performance of integrated AI applications.

AI for Doctors-A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging

Successful adoption of artificial intelligence (AI) in medical imaging requires medical professionals to understand underlying principles and techniques. However, educational offerings tailored to the need of medical professionals are scarce. To fill this gap, we created the course "AI for Doctors: Medical Imaging". An analysis of participants' opinions on AI and self-perceived skills rated on a five-point Likert scale was conducted before and after the course. The participants' attitude towards AI in medical imaging was very optimistic before and after the course. However, deeper knowledge of AI and the process for validating and deploying it resulted in significantly less overoptimism with respect to perceivable patient benefits through AI (p = 0.020). Self-assessed skill ratings significantly improved after the course, and the appreciation of the course content was very positive. However, we observed a substantial drop-out rate, mostly attributed to the lack of time of medical professionals. There is a high demand for educational offerings regarding AI in medical imaging among medical professionals, and better education may lead to a more realistic appreciation of clinical adoption. However, time constraints imposed by a busy clinical schedule need to be taken into account for successful education of medical professionals.