Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review

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.

Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis

PURPOSE

Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface.

METHODS

Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface.

RESULTS

The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within a ± 10% range of the ground truth.

CONCLUSIONS

The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.

Leadership Dynamics in Driving AI Transformation in Healthcare: Insights from a Scoping Review

BACKGROUND

The leaders of healthcare institutions are grappling with rising expenses and surging demands for medical services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care processes, alleviate operational burdens, and efficiently improve healthcare quality.

OBJECTIVE

In this paper we will review the existing literature and synthesize insights on the role of leadership in driving AI transformation within the healthcare sector.

METHODS

We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the healthcare sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews).

RESULTS

A review of unique 2,813 abstracts led to the retrieval of 97 full-text articles for assessment, of which we included 22 articles for review. Our mapping of the literature reveals that leading AI transformation within the healthcare sector involves navigating a constantly changing landscape influenced by complex the various regulatory, technology and organization contexts. Technological, strategic, operational, and organizational leadership is required to drive AI transformation. Leadership across technical, adaptive, and interpersonal capacities is essential to navigate this transformation successfully.

CONCLUSIONS

In conclusion, this review provides insights into the functional domains of leadership, the necessary leadership capacities, and the contextual factors that shape leadership behaviors related to AI transformation. Future research on AI in health care should investigate leadership as a crucial factor and examine the interconnectedness of functional domains, leadership capacities and context through rigorous research methodologies to enhance the existing evidence base.

CLINICALTRIAL

The effectiveness of virtual reality technology in student nurse education: A systematic review and meta-analysis

AIM

The purpose of this study was to analyze the effectiveness of virtual reality technology in nursing education.

BACKGROUND

Virtual reality technology is regarded as one of the advanced and significant instructional tools in contemporary education. However, its effectiveness in nursing education remains a subject of debate, and there is currently limited comprehensive research discussing the impact of varying degrees of virtual technology on the educational effectiveness of nursing students.

DESIGN

Systematic review and meta-analysis.

METHODS

The present systematic review and meta-analysis were applied according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. The PubMed, Embase, CINAHL, ProQuest, Cochrane Library, Web of Science, and Scopus were searched for relevant articles in the English language. The methodologies of the studies evaluated were assessed using Cochrane Risk of Bias2 (ROB 2) tool and Joanna Briggs Institute (JBI) assessment tool. We took the learning satisfaction, knowledge, and skill performance of nursing students as the primary outcomes, and nursing students' self-efficacy, learning motivation, cognitive load, clinical reasoning, and communication ability were assessment as secondary outcomes. The meta-analysis was performed using R 4.3.2 software according to PRISMA guidelines. Heterogeneity was assessed by I2 and P statistics. Standardized mean difference (SMD) and 95 % confidence intervals (CIs) were used as effective indicators.

RESULTS

Twenty-six studies were reviewed, which involved 1815 nursing students. The results showed that virtual reality teaching, especially immersive virtual reality, was effective in improving nursing students' learning satisfaction (SMD: 0.82, 95%CI: 0.53-1.11, P < 0.001), knowledge (SMD: 0.56, 95%CI: 0.34-0.77, P < 0.001), skill performance (SMD: 1.13, 95 % CI: 0.68-1.57, P < 0.001), and self-efficacy (SMD: 0.64, 95%CI: 0.21,1.07, P < 0.001) compared to traditional teaching methods. However, the effects of virtual reality technology on nursing students' motivation, cognitive load, clinical reasoning, and communication ability were not significant and require further research.

CONCLUSIONS

The results of this study show that virtual reality technology has a positive impact on nursing students. Nonetheless, it is crucial not to underestimate the effectiveness of traditional education methods, and future research could analyze the impact of different populations on nursing education while improving virtual reality technology, to more comprehensively explore how to improve the quality of nursing education. Moreover, it is imperative to emphasize the integration of virtual education interventions with real-world experiences promptly. This integration is essential for bridging the gap between the virtual learning environment and real-life scenarios effectively.

REGISTRATION NUMBER

CRD42023420497 (https://www.crd.york.ac.uk/PROSPERO/#recordDetails).

Education and training programs for health professionals' competence in virtual consultations: a scoping review protocol

INTRODUCTION

Digital solutions, telemedicine, and technologies are increasingly becoming a part of the health system, requiring current and future health professionals to master skills in these domains.

OBJECTIVE

The objective of this scoping review is to explore, report, and map the evidence on education and training programs for current and future health professionals' competence in virtual consultations.

INCLUSION CRITERIA

This review will consider any studies on education and training programs designed to optimize current and future health professionals' competence in virtual consultations in any setting, such as faculties, universities, university colleges, hospitals, or community locations.

METHODS

This review will be guided by the JBI methodology for scoping reviews. Published and unpublished sources of information will be searched for in MEDLINE (PubMed), CINAHL Complete (EBSCOhost), and Scopus. Studies written in English, German, Danish, Swedish, and Norwegian will be considered, with no geographical or cultural limitations. Two independent reviewers will screen retrieved papers, and a standardized tool will be used to extract data from each included source. The results of the extracted data will be presented in tabular format, together with a narrative summary of the evidence.

DETAILS OF THE REVIEW CAN BE FOUND IN OPEN SCIENCE FRAMEWORK

https://doi.org/10.17605/OSF.IO/BSMUY.

Is geriatric medicine teaching homogeneous? The analysis of geriatric medicine courses at Polish undergraduate medical programmes

PURPOSE

We aimed to analyse the characteristics of geriatric courses offered to undergraduate medical students in higher educational institutions (HEIs) in Poland.

METHODS

We searched the official websites of the HEIs offering the medical degree programmes and directly contacted the dean's offices and HEIs representatives to retrieve the relevant information. The documents were analysed for course content, teaching methods, duration, and recommended texts. We also checked the obtained curricula for the reference to of the learning objectives related to geriatric medicine, selected from the currently endorsed Polish educational standards (ES) provided by the Ministry of Science and Higher Education.

RESULTS

Geriatric medicine courses were obligatory at all included HEIs (n = 19), but the courses differed in structure and content. The courses varied in duration from 11 to 60 h and were primarily lecture based. Simulation was utilized at only one HEI and e-learning at two institutions. Out of 315 learning objectives, we acknowledged only 9 as geriatric. They were not always found in all curricula. Two HEIs included self-described learning objectives in their curricula. Across all HEIs, a total of 29 recommended texts (published between 1995 and 2021) were identified, including 2 English-language texts.

CONCLUSION

Geriatric medicine was a mandatory subject for medical students of the included HEIs. However, there was a lack of uniformity in the offered courses. This leaves room for the development of a unified undergraduate geriatrics curriculum to effectively address diverse geriatric issues across Europe. The importance of this matter is highlighted by demographic trends and workforce challenges.

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Background

The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education.

Objective

This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students.

Methods

The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software.

Results

Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure.

Conclusions

The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.

Evaluating the readiness of healthcare administration students to utilize AI for sustainable leadership: a survey study

PURPOSE

This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educational curricula and suggests enhancements to better prepare future healthcare professionals for the evolving demands of AI-driven healthcare environments.

DESIGN/METHODOLOGY/APPROACH

This study utilized a cross-sectional survey design to understand healthcare administration students' perceptions regarding integrating AI in healthcare leadership. An online questionnaire, developed from an extensive literature review covering fundamental AI knowledge and its role in sustainable leadership, was distributed to students majoring and minoring in healthcare administration. This methodological approach garnered participation from 62 students, providing insights and perspectives crucial for the study's objectives.

FINDINGS

The research revealed that while a significant majority of healthcare administration students (70%) recognize the potential of AI in fostering sustainable leadership in healthcare, only 30% feel adequately prepared to work in AI-integrated environments. Additionally, students were interested in learning more about AI applications in healthcare and the role of AI in sustainable leadership, underscoring the need for comprehensive AI-focused education in their curriculum.

RESEARCH LIMITATIONS/IMPLICATIONS

The research is limited by its focus on a single academic institution, which may not fully represent the diversity of perspectives in healthcare administration.

PRACTICAL IMPLICATIONS

This study highlights the need for healthcare administration curricula to incorporate AI education, aligning theoretical knowledge with practical applications, to effectively prepare future professionals for the evolving demands of AI-integrated healthcare environments.

ORIGINALITY/VALUE

This research paper presents insights into healthcare administration students' readiness and perspectives toward AI integration in healthcare leadership, filling a critical gap in understanding the educational needs in the evolving landscape of AI-driven healthcare.

Leveraging User-Friendly Mobile Medical Devices to Facilitate Early Hospital Discharges in a Pediatric Setting: A Randomized Trial Study Protocol

BACKGROUND

Mobile technology is increasingly prevalent in healthcare, serving various purposes, including remote health monitoring and patient self-management, which could prove beneficial to early hospital discharges.

AIMS

This study investigates the transitional care program experience facilitating early discharges in a pediatric setting through the use of an easy-to-use mobile medical device (TytoCare™, TytoCare Ltd., Natanya, Israel).

OUTCOMES

This study aims to assess the effectiveness of telehomecare in achieving complete resolution of diseases without readmission, compare the length of stay between intervention and standard care groups, and gather user and professional experiences.

METHODS

A randomized open-label, controlled pilot study enrolled 102 children, randomly assigned to the telehomecare (TELE) group (n = 51, adopting early hospital discharge with continued home monitoring) or the standard-of-care (STAND) group (n = 51). Primary outcomes include complete disease resolution without readmission. Secondary objectives include recording a shorter length of stay in the intervention group. Surveys on user and professional experiences were conducted. A group of 51 children declining telemedicine services (NO-TELE) was also included.

RESULTS

In the TELE group, 100% of children achieved complete disease resolution without readmission, with a median duration of stay of 4 days, significantly shorter than the 7 days in the STAND group (p = 0.01). The telemedicine system demonstrated efficient performance and high satisfaction levels. The NO-TELE group showed no significant differences in demographics or digital technology competence. Perceived benefits of telemedicine included time and cost savings, reduced hospital stays, and technology utility and usability.

CONCLUSIONS

This study demonstrates that user-friendly mobile medical devices effectively facilitate early hospital discharges in a pediatric setting. These devices serve as a bridge between home and hospital, optimizing care pathways.

Resident-as-Teacher: Can It Be Done With an E-Learning Module?

Background Resident-as-teacher initiatives are traditionally specialty-specific and performed in-person, limiting ability to disseminate essential teaching skills to all residents. Objective The aim of this study was to develop, implement, and evaluate a resident-as-teacher interactive e-learning module on growth mindset and coaching. Methods The module was designed and implemented between August 2022 and March 2023. It was distributed to postgraduate year (PGY) 1 residents in all specialties at a large academic institution. Completion rates, Likert ratings, and answers to 2 open-ended questions were used for assessment. Descriptive statistics and 1-way analysis of variance with Sîdák correction for multiple comparisons were performed on Likert ratings. Responses to open-ended questions were evaluated using content analysis. Results The module was completed by all 277 PGY-1 residents (100%), with the evaluation completed by 276 of 277 (99.6%) residents. Mean rating of the module's relevance to the role of resident teacher was 4.06±0.90 (5-point Likert scale), with general surgery residents rating the module less favorably compared to all specialties (3.28±1.06; P<.01; 95% CI 0.26-1.30). Open-ended comments revealed that residents most liked the delivery of relevant teaching strategies and the interactive design of the module. The most common area for suggested improvement was the addition of content such as teaching in challenging situations. Time needed for design, implementation, and evaluation was 80 hours total. Conclusions An e-learning module offers an interactive platform for teaching skills and was found to be an acceptable method of instruction for residents.

Exploring 40 years of Korean medical education conference themes

PURPOSE

The Korean Society of Medical Education (KSME) was founded in 1983 and celebrated its 40th anniversary in 2023. This study examines the evolution of topics discussed at KSME conferences from 1971 through 2023, highlighting shifts in the focus of medical education.

METHODS

We analyzed 90 KSME conferences over 5 decades (1970s, 1980s, 1990s, 2000s, and 2010s), categorizing the topics into three eras based on emerging themes and continuity.

RESULTS

Consequently, 37 topics covered at the conference were categorized. Ten topics continuously appeared from the 1970s to the 2010s, including future directions of medical education, teaching methods, faculty development, and curriculum. The topics from the 1970s to the 1990s included 14 areas, such as medical education evaluation, non-undergraduate curriculum, community-related, and research. Thirteen new topics emerged after the 2000s, such as social accountability, student support, professionalism, and quality improvements. The most common topics under innovations in medical education, a case of curriculum innovation at universities that began after 2000, were clinical clerkship, curriculum development, and medical humanities.

CONCLUSION

KSME's selection of conference topics has been strategically aligned with societal needs and the evolving landscape of medical education. Future topics should continue to address relevant societal and educational challenges.

Current and future applications of artificial intelligence in surgery: implications for clinical practice and research

Surgeons are skilled at making complex decisions over invasive procedures that can save lives and alleviate pain and avoid complications in patients. The knowledge to make these decisions is accumulated over years of schooling and practice. Their experience is in turn shared with others, also via peer-reviewed articles, which get published in larger and larger amounts every year. In this work, we review the literature related to the use of Artificial Intelligence (AI) in surgery. We focus on what is currently available and what is likely to come in the near future in both clinical care and research. We show that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain. We also address concerns about AI technology, including the inability for users to interpret algorithms as well as incorrect predictions. A better understanding of AI will allow surgeons to use new tools wisely for the benefit of their patients.

[Interactive electronic visualization formats in student teaching]

BACKGROUND

In the context of contact restrictions, conventional teaching is currently in need of optimization and expansion. The range of digital teaching formats in student training is very heterogeneous and their effectiveness uncertain. This study aims to investigate the extent to which an electronic ward round can be used as an alternative to the conventional ENT attendance practical course, and whether the use of electronic teaching formats has an influence on the quality of teaching.

MATERIALS AND METHODS

Instead of regular attendance practicals, bedside teaching took place once a week in real time as a video stream via tablet. A total of 43 students in the seventh semester (winter semester 2020/2021) were included in the prospective study. Evaluation forms were used to examine the subjective didactic value of different visualization formats for the students. Examination results from previous years were used for comparison.

RESULTS

The majority of students reported knowledge gain from the electronic rounds (93.02%) and that they were a good alternative to the traditional attendance clerkship (69.77%). The quality of the video and audio transmission as well as the comprehensibility of the case studies presented were consistently rated as good to very good. The students' examination results tended to be slightly worse in the test group compared to the control students of previous years.

CONCLUSION

Integration of innovative interactive visualization options into teaching shows promising prospects as a supplement to conventional face-to-face teaching. The results of this study can contribute to the further expansion of digital teaching. Scaling up this model could be considered especially in countries with limited availability of face-to-face teaching.

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

The impact of empathy on medical students: an integrative review

INTRODUCTION

Empathy is considered the ability to understand or feel others emotions or experiences. As an important part of medical education, empathy can affect medical students in many ways. It is still lacking a comprehensive evaluation of the existing articles on empathy's impact on medical students, despite the existence of many articles on the topic.

OBJECTIVES

To summarize the impact of empathy on medical students during medical education from four perspectives: mental health, academic performance, clinical competence, and specialty preference.

METHODS

The search terms used for retrieval were "empathy", "medical student", "mental health", "depression", "anxiety", "burnout", "examinations", "academic performance", "clinical competence", "specialty preference" on PubMed, EBSCO, and Web of Science before January 2024. The search was carried out by two reviewers. Titles and abstracts were screened independently and reviewed based on inclusion/exclusion criteria. A consensus was drawn on which articles were included.

RESULTS

Our results indicated that high empathy was a positive factor for mental health, However, students with high affective empathy were more likely to suffer from depression, anxiety, and burnout. Empathy was found to be unrelated to academic performance, but positively correlated with clinical competence, particularly in terms of communication skills. Medical students with high levels of empathy tended to prefer people-oriented majors.

CONCLUSIONS

Medical students who score higher on the self-reported empathy scales often have better mental health, better communication skills, and tend to choose people-oriented specialties. But empathy is not related to academic performance. Additionally, the different dimensions of empathy have different impacts on medical students. It is necessary to design targeted courses and training for medical students to enhance their empathy.

Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis

Background

Digital health has been taught at medical faculties for a few years. However, in general, the teaching of digital competencies in medical education and training is still underrepresented.

Objective

This study aims to analyze the objective acquisition of digital competencies through the implementation of a transdisciplinary digital health curriculum as a compulsory elective subject at a German university. The main subject areas of digital leadership and management, digital learning and didactics, digital communication, robotics, and generative artificial intelligence were developed and taught in a transdisciplinary manner over a period of 1 semester.

Methods

The participants evaluated the relevant content of the curriculum regarding the competencies already taught in advance during the study, using a Likert scale. The participants' increase in digital competencies were examined with a pre-post test consisting of 12 questions. Statistical analysis was performed using an unpaired 2-tailed Student t test. A P value of <.05 was considered statistically significant. Furthermore, an analysis of the acceptance of the transdisciplinary approach as well as the application of an alternative examination method (term paper instead of a test with closed and open questions) was carried out.

Results

In the first year after the introduction of the compulsory elective subject, students of human medicine (n=15), dentistry (n=3), and medical biotechnology (n=2) participated in the curriculum. In total, 13 participants were women (7 men), and 61.1% (n=11) of the participants in human medicine and dentistry were in the preclinical study stage (clinical: n=7, 38.9%). All the aforementioned learning objectives were largely absent in all study sections (preclinical: mean 4.2; clinical: mean 4.4; P=.02). The pre-post test comparison revealed a significant increase of 106% in knowledge (P<.001) among the participants.

Conclusions

The transdisciplinary teaching of a digital health curriculum, including digital teaching methods, considers perspectives and skills from different disciplines. Our new curriculum facilitates an objective increase in knowledge regarding the complex challenges of the digital transformation of our health care system. Of the 16 student term papers arising from the course, robotics and artificial intelligence attracted the most interest, accounting for 9 of the submissions.

Impact assessment of integrated-pathy on cancer-related fatigue in cancer patients: an observational study

BACKGROUND

Integrated-pathy aims to integrate modern medicine with traditional systems via applying the holistic approach of Ayurveda, Yoga, and natural medicine. This is important for addressing the challenges surrounding the delivery of long-term palliative care for chronic ailments including cancer. The prime intent of this study was to substantiate the underlying hypothesis behind the differential and integrative approach having a positive impact on Quality of Life of cancer patients.

STUDY DESIGN

Cross-sectional Observational study.

METHODS

A standardized questionnaire was developed and used, after obtaining written informed consent from patients to assess the impact of Integrated-pathy on patients (n = 103) diagnosed with cancer receiving care at Patanjali Yoggram. The research was carried out over 8 months. All participants received a uniform treatment protocol as prescribed by Patanjali. For the sample size determination and validation, α and 1-β was calculated and for the significance of the pre- and post-treatment QoL ratings, Shapiro wilk test and other descriptive statistics techniques were explored.

RESULTS

A total of 103 patients seeking cancer special-healthcare were interviewed, out of which 39 (37.86%) remained finally based on the inclusion/exclusion criteria with age (25-65 years), types of cancers (Carcinoma and Sarcoma), chemotherapy/radiotherapy received or not, before opting Integrated-pathy. Follow-ups revealed a significant increase in the QoL (17.91%) after receiving the integrated therapy over a course of at least 1 month. Further, a significant reduction in cancer-related pain followed by an increase in QoL index was reported in the patients. Shapiro-wilk test revealed significant pairing (p < 0.001) with validation of the model using test.

CONCLUSIONS

To bolster evidence-based backing for Integrated-pathy, there is a need for clearly delineated clinical indicators that are measurable and trackable over time. Clinical investigators are encouraged to incorporate Integrated-pathy into their proposed interventions and conduct analogous studies to yield sustained advantages in the long run.

Maturity model for assessing the medical humanities: a Delphi study

BACKGROUND

Becoming a first-level discipline in China means access to more educational resources. The development of medical humanities in China has been going on for more than 40 years, and some medical schools have set up master's and doctoral programs in medical humanities. The demand for medical humanities-related knowledge in China is also growing after COVID-19. However, medical humanities is only a second-level discipline and receives limited resources to meet the needs of society. This study aims to establish a system of indicators that can assess whether the medical humanities has a first-level discipline and provide a basis for its upgrading to a first-level.

METHODS

A Delphi technique was used, with the panel of expert expressing their views in a series of two questionnaires. A coefficient of variation of less than 0.2 indicates expert agreement.

RESULT

A total of 25 experts participated in this Delphi study. Consensus was reached on 11 first-grade indices and 48 s-grade indices. The authoritative coefficient(Cr) of the experts was 0.804, which indicates that the experts have a high level of reliability.

CONCLUSION

This study provides a reliable foundation for the evaluation of medical humanities maturity.

Medical education and physician training in the era of artificial intelligence

ABSTRACT

With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition. We introduce 'AI-assisted' and 'AI-integrated' paradigms for medical education and physician training, targeting a more universal, accessible, high-quality and interconnected educational journey. We differentiate between essential knowledge for all physicians, specialised insights for clinician-scientists and mastery-level proficiency for clinician-computer scientists. With the transformative potential of AI in healthcare and service delivery, it is poised to reshape the pedagogy of medical education and residency training.

Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program

As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.

Challenges Faced by Medical Trainees in Outpatient Management Education in Acute Care Hospitals: A Thematic Analysis

Introduction As societies age globally, medical education faces the challenge of adapting to the evolving healthcare needs of an aging population. This study focuses on the education of medical residents in outpatient departments in Japan, a country with a rapidly aging society. The research aims to understand the perceptions and challenges medical residents face in outpatient management, highlighting the areas for potential improvement in their educational framework. Method This study involved first-year medical residents at Fuchu Hospital in Osaka, using thematic analysis based on relativist ontology and constructivist epistemology. Data were collected through field notes and reflection sheets, documenting residents' interactions with patients, learning difficulties, and personal reflections. Semi-structured interviews were conducted to gain profound insights into their experiences and views on outpatient management education. Results Three main themes emerged from the analysis: The experience of continuity of care, the view regarding comprehensive management, and the gap between purposes and learning content. Residents expressed concerns about the limited opportunities for continuous patient care, leading to challenges in managing chronic diseases effectively. The focus on organ-specific specialties in acute care hospitals resulted in a fragmented understanding of patient care, particularly for elderly patients with multimorbidity. Furthermore, the study identified a discrepancy between the educational goals of outpatient management and the actual content delivered, highlighting the need for more observational experiences and practical guidance in outpatient settings. Conclusion The findings suggest a pressing need for a more structured, comprehensive, and personalized approach to outpatient management education for medical residents. As aging populations continue to grow, it is vital to equip medical professionals with the skills and knowledge to manage a wide range of patient conditions effectively. Improving the educational framework in outpatient departments can enhance patient care quality and prepare medical residents to meet the challenges of an aging society. This study contributes valuable insights into improving medical education in outpatient settings, particularly in aging societies like Japan.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

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.

Artificial intelligence and urology: ethical considerations for urologists and patients

The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enabled optimization of patient workflow, increased diagnostic accuracy and enhanced computer analysis of radiological and pathological images. However, before further use of AI is undertaken, possible ethical issues need to be evaluated to improve understanding of this technology and to protect patients and providers. Possible ethical issues that require consideration when applying AI in clinical practice include patient safety, cybersecurity, transparency and interpretability of the data, inclusivity and equity, fostering responsibility and accountability, and the preservation of providers' decision-making and autonomy. Ethical principles for the application of AI to health care and in urology are proposed to guide urologists, patients and regulators to improve use of AI technologies and guide policy-making.

Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots

Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.

Strategies to improve surgical technical competency: a systematic review

BACKGROUND

A cornerstone of surgical residency training is an educational program that produces highly skilled and effective surgeons. Training structures are constantly being revised due to evolving program structures, shifting workforces, and variability in the clinical environment. This has resulted in significant heterogeneity in all surgical resident education, training tools utilized, and measures of training efficacy.

METHODS

We systematically reviewed educational interventions for technical skills in neurosurgery published across PubMed, Embase, and Web of Science over four decades. We extracted general characteristics of each surgical training tool while categorizing educational interventions by modality and neurosurgical application.

RESULTS

We identified 626 studies which developed surgical training tools across eight different training modalities: textbooks and literature (11), online resources (53), didactic teaching and one-on-one instruction (7), laboratory courses (50), cadaveric models (63), animal models (47), mixed reality (166), and physical models (229). While publication volume has grown exponentially, a majority of studies were cited with relatively low frequency. Most training programs were published in the development and validation phase with only 2.1% of tools implemented long-term. Each training modality expressed unique strengths and limitations, with limited data reported on the educational impact connected to each training tool.

CONCLUSIONS

Numerous surgical training tools have been developed and implemented across residency training programs. Though many creative and cutting-edge tools have been devised, evidence supporting educational efficacy and long-term application is lacking. Increased utilization of novel surgical training tools will require validation of metrics used to assess the training outcomes and optimized integration with clinical practice.

Enhancing the Comprehensive Integration of General Medicine Education in Rural Japan: A Thematic Analysis

Introduction The dynamism inherent in general medicine, particularly since its recognition as a distinct specialty in 2019, necessitates constant revision and refinement of the curriculum. As general medicine programs proliferate throughout Japan, understanding the revision processes, especially concerning the pivotal concept of lateral integrations, becomes critical. Lateral integrations, which pertain to the interconnectedness between learning contents and contexts, ensure a cohesive learning experience for medical students. In this study, we sought to explore the intricacies and experiences of revising these integrations within the general medicine curriculum. Methods A qualitative thematic analysis rooted in relativist ontology and constructivist epistemology was conducted. The research was carried out at the Unnan City Hospital, Shimane Prefecture, focusing on trainees transitioning between diverse medical settings. Semi-structured interviews were employed to gauge perceptions regarding these transitions, and thematic analysis was used to interpret the data. Reflexivity was ensured by the diverse expertise of the research team, with rigorous discussions to mitigate biases. Results The following four themes emerged from the analysis: (1) confusion due to the transition from acute to chronic clinical settings, with trainees feeling overwhelmed and resistant to focus solely on chronic care; (2) monotony due to the loss of some clinical experiences, indicating challenges in maintaining motivation after transitioning to clinics; (3) disconnection between learning contexts, where participants desired stronger links to their primary training hospitals; and (4) anxiety as community leaders, highlighting the need for instilling leadership skills and a deeper understanding of diverse community healthcare professions. Conclusion This study shed light on the tangible challenges faced by general medicine trainees during transitions between different learning environments. These insights are valuable for educators in refining curriculum structures, ensuring smooth transitions, and enhancing lateral integrations. Addressing these challenges will bolster the quality and relevance of general medicine education in Japan, fostering the creation of adaptable, well-rounded physicians who are attuned to the multifaceted needs of their communities.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

ChatGPT Is Equivalent to First-Year Plastic Surgery Residents: Evaluation of ChatGPT on the Plastic Surgery In-Service Examination

BACKGROUND

ChatGPT is an artificial intelligence language model developed and released by OpenAI (San Francisco, CA) in late 2022.

OBJECTIVES

The aim of this study was to evaluate the performance of ChatGPT on the Plastic Surgery In-Service Examination and to compare it to residents' performance nationally.

METHODS

The Plastic Surgery In-Service Examinations from 2018 to 2022 were used as a question source. For each question, the stem and all multiple-choice options were imported into ChatGPT. The 2022 examination was used to compare the performance of ChatGPT to plastic surgery residents nationally.

RESULTS

In total, 1129 questions were included in the final analysis and ChatGPT answered 630 (55.8%) of these correctly. ChatGPT scored the highest on the 2021 exam (60.1%) and on the comprehensive section (58.7%). There were no significant differences regarding questions answered correctly among exam years or among the different exam sections. ChatGPT answered 57% of questions correctly on the 2022 exam. When compared to the performance of plastic surgery residents in 2022, ChatGPT would rank in the 49th percentile for first-year integrated plastic surgery residents, 13th percentile for second-year residents, 5th percentile for third- and fourth-year residents, and 0th percentile for fifth- and sixth-year residents.

CONCLUSIONS

ChatGPT performs at the level of a first-year resident on the Plastic Surgery In-Service Examination. However, it performed poorly when compared with residents in more advanced years of training. Although ChatGPT has many undeniable benefits and potential uses in the field of healthcare and medical education, it will require additional research to assess its efficacy.

AI in medical education: medical student perception, curriculum recommendations and design suggestions

Medical AI has transformed modern medicine and created a new environment for future doctors. However, medical education has failed to keep pace with these advances, and it is essential to provide systematic education on medical AI to current medical undergraduate and postgraduate students. To address this issue, our study utilized the Unified Theory of Acceptance and Use of Technology model to identify key factors that influence the acceptance and intention to use medical AI. We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI. Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students. The intention to use medical AI is positively associated with factors such as performance expectancy, habit, hedonic motivation, and trust. Therefore, future medical education should prioritize promoting students' performance in training, and courses should be designed to be both easy to learn and engaging, ensuring that students are equipped with the necessary skills to succeed in their future medical careers.

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.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Healthcare Profession Students' Motivations for Learning About Community Organizing: A Thematic Analysis

Background Medical care now emphasizes community health, prevention, health promotion, and collaboration. Integrating medical students into community health initiatives enhances their community health and student skills. In an aging multicultural population, the involvement of healthcare professionals in community health management is vital. However, medical education in Japan lacks sufficient exposure to community health issues. A program in Shimane Prefecture aimed to address this gap by revolutionizing medical education through community organizations. Methods This study employed a reflexive thematic analysis based on relativist ontology and constructivist epistemology. Participants aspiring to be healthcare professionals from Japanese high schools and universities were recruited from rural Shimane Prefecture. Computer-based questionnaires were used to collect data on participants' reasons, motivations, and visions for community-organizing education. The thematic analysis followed Braun and Clarke's approach and involved systematic coding, theme identification, and refinement. Results Three themes emerged from the analysis. In expanding hopes for unknown potential, participants sought improved communication skills, real-world understandings, and fresh perspectives and aimed to promote personal growth through community engagement. In acquiring activeness and new perspectives through connections with peers, hands-on learning and collaboration with peers with shared interests were motivating factors. Participants sought to generate inquiries and discover their activities. Regarding the desire to connect with diverse individuals driven by a strong curiosity about the community, participants aimed to learn community engagement techniques, understand community involvement methods, and explore the relationship between social issues and health. Conclusion Community-organizing education plays a pivotal role in shaping future healthcare professionals. Our analysis underscores the need for curriculum reform, including experiential learning and peer interaction, to facilitate a comprehensive understanding of health and community dynamics. Future studies should assess the long-term impacts of these experiences on students' careers and community health to contribute to advancements in medical education and community-oriented healthcare professionalism.

Assessing the Utilization of Large Language Models in Medical Education: Insights From Undergraduate Medical Students

Background Artificial intelligence (AI) has the potential to be integrated into medical education. Among AI-based technology, large language models (LLMs) such as ChatGPT, Google Bard, Microsoft Bing, and Perplexity have emerged as powerful tools with capabilities in natural language processing. With this background, this study investigates the knowledge, attitude, and practice of undergraduate medical students regarding the utilization of LLMs in medical education in a medical college in Jharkhand, India. Methods A cross-sectional online survey was sent to 370 undergraduate medical students on Google Forms. The questionnaire comprised the following three domains: knowledge, attitude, and practice, each containing six questions. Cronbach's alphas for knowledge, attitude, and practice domains were 0.703, 0.707, and 0.809, respectively. Intraclass correlation coefficients for knowledge, attitude, and practice domains were 0.82, 0.87, and 0.78, respectively. The average scores in the three domains were compared using ANOVA. Results A total of 172 students participated in the study (response rate: 46.49%). The majority of the students (45.93%) rarely used the LLMs for their teaching-learning purposes (chi-square (3) = 41.44, p < 0.0001). The overall score of knowledge (3.21±0.55), attitude (3.47±0.54), and practice (3.26±0.61) were statistically significantly different (ANOVA F (2, 513) = 10.2, p < 0.0001), with the highest score in attitude and lowest in knowledge. Conclusion While there is a generally positive attitude toward the incorporation of LLMs in medical education, concerns about overreliance and potential inaccuracies are evident. LLMs offer the potential to enhance learning resources and provide accessible education, but their integration requires further planning. Further studies are required to explore the long-term impact of LLMs in diverse educational contexts.

The impact of Chat Generative Pre-trained Transformer (ChatGPT) on medical education

Artificial intelligence (AI) in medicine is developing rapidly. The advent of Chat Generative Pre-trained Transformer (ChatGPT) has taken the world by storm with its potential uses and efficiencies. However, technology leaders, researchers, educators, and policy makers have also sounded the alarm on its potential harms and unintended consequences. AI will increasingly find its way into medicine and is a force of both disruption and innovation. We discuss the potential benefits and limitations of this new league of technology and how medical educators have to develop skills and curricula to best harness this innovative power.

Faculty perceptions regarding an individually tailored, flexible length, outcomes-based curriculum for undergraduate medical students

PURPOSE

The perception of faculty members about an individually tailored, flexible-length, outcomes-based curriculum for undergraduate medical students was studied. Their opinion about the advantages, disadvantages, and challenges was also noted. This study was done to help educational institutions identify academic and social support and resources required to ensure that graduate competencies are not compromised by a flexible education pathway.

METHODS

The study was done at the International Medical University, Malaysia, and the University of Lahore, Pakistan. Semi-structured interviews were conducted from 1st August 2021 to 17th March 2022. Demographic information was noted. Themes were identified, and a summary of the information under each theme was created.

RESULTS

A total of 24 (14 from Malaysia and 10 from Pakistan) faculty participated. Most agreed that undergraduate medical students can progress (at a differential rate) if they attain the required competencies. Among the major advantages mentioned were that students may graduate faster, learn at a pace comfortable to them, and develop an individualized learning pathway. Several logistical challenges must be overcome. Providing assessments on demand will be difficult. Significant regulatory hurdles were anticipated. Artificial intelligence (AI) can play an important role in creating an individualized learning pathway and supporting time-independent progression. The course may be (slightly) cheaper than a traditional one.

CONCLUSION

This study provides a foundation to further develop and strengthen flexible-length competency-based medical education modules. Further studies are required among educators at other medical schools and in other countries. Online learning and AI will play an important role.

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

INTRODUCTION

There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results.

METHODS

In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance.

RESULTS

Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively.

CONCLUSIONS

The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.

The #ObGynInternChallenge: Reach, Adoption, Implementation, and Effectiveness of a Microlearning SMS-Distributed Curriculum

PROBLEM

While many medical schools have implemented bootcamps or specialized curricula to prepare medical students for residency, these programs are neither universal nor consistent in their content.

APPROACH

The authors created an electronic, multimodal, short messaging service (SMS)-distributed curriculum, called the #ObGynInternChallenge, to improve learners' medical knowledge, based on the Council on Resident Education in Obstetrics and Gynecology educational objectives. The curriculum was open to all fourth-year medical students who matched into obstetrics and gynecology (Ob/Gyn). Daily messages were delivered to participants' mobile devices via SMS for 25 consecutive weekdays, May 3-June 4, 2021. Each day's message included an introduction with key facts, an infographic, a website link with a podcast and additional reference materials, and at least one question. The authors assessed its reach, adoption, implementation, and effectiveness.

OUTCOMES

For reach and adoption, total enrollment for the curriculum was 1,057 (72.0%) of 1,469 filled Ob/Gyn residency positions in the 2021 Match. The total cost of the intervention was $2,503.20 or $2.37 per participant. For implementation, all participants who signed up for the course received the daily messages, and 858/1,057 (81.2%) completed the course. Participants felt the curriculum was an excellent resource for studying (391/426, 91.8%) and the course was enjoyable to use (395/424, 93.2%). For effectiveness, mean score improvement was 11.6% (pre-test: 62.4%, post-test: 74.0%; P < .001). In the multivariate linear regression analysis, high podcast ( P = .02) and website use ( P = .002) were associated with greater score improvement. High social media use was associated with less improvement ( P = .02).

NEXT STEPS

This study suggests promise for a low-cost, largely satisfying SMS-distributed curriculum in terms of offering some benefit for short-term knowledge gain. Next steps include expanding such a curriculum to meet standard learning objectives for all fourth-year medical students entering residency.

The Role of E-Content Development in Medical Teaching: How Far Have We Come?

With the advancements in technology, medical educators are now able to create and deliver content to students through digital platforms. Electronic content (e-content) development has allowed educators to incorporate multimedia, animations, simulations, and interactive elements which support verbal instruction, such as improved expression and comprehension, into their teaching materials. E-content development is a relatively new field, but it is growing very rapidly. Recent findings have indicated that the e-learning sector will likely experience a huge surge in the upcoming years. The Indian government has launched various initiatives for e-content development in medical education. E-content development has great potential and can be used in various learning scenarios. While it initially gained popularity in higher education, it has since been applied to many other sectors, including healthcare. It allows educators to create highly engaging learning experiences that are accessible by all students. Challenges in e-content development include availability of the internet, creating content that is engaging and relevant to a wide range of learners, and access. Still, it is expected that the use of e-content in medical teaching will continue to increase in the future. The future of e-content development in medical teaching is likely to see continued growth and innovation as technology advances and more educators and learners recognize the benefits of online and digital resources.

Assessing the effectiveness of massive open online courses on improving clinical skills in medical education in China: A meta-analysis

Massive Open Online Courses (MOOCs) are a new phenomenon in education worldwide. In China, MOOCs have been widely used in medical courses. However, the effects of MOOCs on improving clinical skills are controversial. Therefore, we conducted the study to verify whether the application of MOOCs in medical courses can improve participants' clinical skills in China. A systematic literature search was carried out using the PubMed, Embase, Web of Science, CNKI and Wanfang databases according to the predetermined criteria. The Hedges' g and its corresponding 95% confidence interval were selected to assess the effects of MOOCs on participants' clinical skills. Subgroup analyses, sensitivity analysis and publication bias test were performed in the study. A total of thirty-two records (thirty-two studies) with 3422 participants were identified in our study. There was a significant improvement in clinical skill scores of participants in the MOOC group compared with the control group. Subgroup analyses showed similar results in different student groups. Our study supported the notion that the MOOC-based teaching method appeared to be a more effective method than the conventional teaching technique for the improvement of participants' clinical skills in China.

The impacts of altruism levels on the job preferences of medical students: a cross-sectional study in China

BACKGROUND

Rational allocation of human resources for health is crucial for ensuring public welfare and equitable access to health services. Understanding medical students' job preferences could help develop effective strategies for the recruitment and retention of the health workforce. Most studies explore the relationship between extrinsic incentives and job choices through discrete choice experiments (DCEs). Little attention has been paid to the influence of intrinsic altruism on job choice. This study aimed to explore the heterogeneous preferences of medical students with different levels of altruism regarding extrinsic job attributes.

METHODS

We conducted an online survey with 925 medical students from six hospitals in Beijing from July to September 2021. The survey combined job-choice scenarios through DCEs and a simulation of a laboratory experiment on medical decision-making behavior. Behavioral data were used to quantify altruism levels by estimating altruistic parameters based on a utility function. We fit mixed logit models to estimate the effects of altruism on job preference.

RESULTS

All attribute levels had the expected effect on job preferences, among which monthly income (importance weight was 30.46%, 95% CI 29.25%-31.67%) and work location (importance weight was 22.39%, 95% CI 21.14%-23.64%) were the most salient factors. The mean altruistic parameter was 0.84 (s.d. 0.19), indicating that medical students' altruism was generally high. The subgroup analysis showed that individuals with higher altruism levels had a greater preference for non-financial incentives such as an excellent work environment, sufficient training and career development opportunities, and a light workload. The change in the rate of the uptake of a rural position by individuals with lower levels of altruism is sensitive to changes in financial incentives.

CONCLUSIONS

Medical students' altruism was generally high, and those with higher altruism paid more attention to non-financial incentives. This suggests that policymakers and hospital managers should further focus on nonfinancial incentives to better motivate altruistic physicians, in addition to appropriate economic incentive when designing recruitment and retention interventions. Medical school administrations could attach importance to the promotion of altruistic values in medical education.

Artificial intelligence for healthcare and medical education: a systematic review

BACKGROUND

Human society has entered the age of artificial intelligence, medical practice and medical education are undergoing profound changes. Artificial intelligence (AI) is now applied in many industries, particularly in healthcare and medical education, where it deeply intersects. The purpose of this paper is to overview the current situation and problems of "AI+medicine/medical" education and to provide our own perspective on the current predicament.

METHODS

We searched PubMed, Embase, Cochrane and CNKI databases to assess the literature on AI+medical/medical education from 2017 to July 2022. The main inclusion criteria include literature describing the current situation or predicament of "AI+medical/medical education".

RESULTS

Studies have shown that the current application of AI in medical education is focused on clinical specialty training and continuing education, with the main application areas being radiology, diagnostics, surgery, cardiology, and dentistry. The main role is to assist physicians to improve their efficiency and accuracy. In addition, the field of combining AI with medicine/medical education is steadily expanding, and the most urgent need is for policy makers, experts in the medical field, AI and education, and experts in other fields to come together to reach consensus on ethical issues and develop regulatory standards. Our study also found that most medical students are positive about adding AI-related courses to the existing medical curriculum. Finally, the quality of research on "AI+medical/medical education" is poor.

CONCLUSION

In the context of the COVID-19 pandemic, our study provides an innovative systematic review of the latest "AI+medicine/medical curriculum". Since the AI+medicine curriculum is not yet regulated, we have made some suggestions.

Health-Related Quality of Life and Treatment Satisfaction of Patients with Blood Cancer in Kazakhstan: A Cross-Sectional Study

Blood cancer is the most prevalent health problem associated with poorer health-related quality of life (HRQoL). Associations between HRQoL and its determinants including physical, emotional, and functional domains are insufficiently investigated among blood cancer patients of Kazakhstan. We aimed to assess HRQoL and treatment satisfaction of blood cancer patients in Kazakhstan.

METHODS

This was a cross-sectional study, conducted from November 2022 to December 2022, which enrolled all adult blood cancer patients registered at the healthcare facilities of Semey. This study involved 87 respondents. A questionnaire of the authors' design and the SF-36 questionnaire were used to obtain the data, which was validated.

RESULTS

Out of 87 patients, 47 (54,0%) were males whose mean age was 35,72 ± 1,64 years and 40 (46,0%) were females with the mean age of 45,83 ± 1,57 years. None of the patients were very satisfied with their current clinical management and status monitoring and the overall rate of patient dissatisfied or somewhat dissatisfied was 48.9%. The two questions of "How long have you been seen by a hematologist?" (p=0,019) and "How do you evaluate the organization of medical care in the field of hematology?" (p=0,000) were predictors of patient satisfaction in multiple linear regression analysis. There was a significant difference in the individual SF-36 dimensions and overall QOL scored in different age group participants.

CONCLUSIONS

Overall, the study found that the five determinates affect QOL revealed significant differences between individual age groups and identified key determinants of patient dissatisfaction. Also, it is the first attempt to understand the experience of blood cancer patients in the healthcare system in Kazakhstan, and the results may contribute to a discussion between healthcare professionals and patients on initiatives that need to be taken to improve the quality of healthcare services provided.

Revitalizing General Practice: The Critical Role of Medical Schools in Addressing the Primary Care Physician Shortage

The shortage of primary care physicians is a growing crisis that threatens the stability and effectiveness of healthcare systems. This paper explores a multi-pronged approach to addressing this issue by focusing on the modernization of medical curricula, the establishment of new medical schools, fostering collaboration between institutions, and implementing policy innovations. The cases of South Tyrol, Italy, and Tyrol, Austria, are examined to highlight the challenges faced in establishing new medical schools. This paper proposes that a comprehensive strategy, including the incorporation of general practice content and experience in medical education, is crucial for preparing future physicians for careers in primary care. Furthermore, intensifying efforts to establish new medical schools, particularly in regions such as South Tyrol, which lack native-language medical university education, can provide additional benefits in addressing regional needs and augmenting the number of graduates. Collaboration between existing and new medical schools, regional partnerships, and policy innovations are essential to support the establishment of institutions with a particular focus on general practice and the modernization of curricula at existing universities. By embracing this approach, stakeholders can collectively shape the medical education landscape and address the growing crisis of physician shortages in primary care.

Which lecturers' characteristics facilitate the learning process? A qualitative study on students' perceptions in the rehabilitation sciences

BACKGROUND

In education, lecturers play a crucial role in facilitating students' learning process. However, only a few studies explored which lecturers' characteristics can facilitate this process in higher education for rehabilitation healthcare professionals. Starting from students' perspectives, our qualitative study investigated the lecturers' characteristics that facilitate students' learning process in the rehabilitation sciences.

METHODS

A qualitative interview study. We enrolled students attending the 2nd year of the Master of Science (MSc) degree in 'Rehabilitation Sciences of Healthcare Professions'. Different themes were generated following a 'Reflexive Thematic Analysis'.

RESULTS

Thirteen students completed the interviews. From their analysis, we generated five themes. Specifically, a lecturer that facilitates students' learning process should be: 1) 'A Performer who Interacts with the Classroom', 2) A Flexible Planner who Adopts Innovative Teaching Skills', 3) 'A Motivator who Embraces Transformational Leadership', 4) 'A Facilitator Who Encourages a Constructive Learning Context' and 5) 'A Coach who Devises Strategies to Reach Shared Learning Goals'.

CONCLUSIONS

The results of this study underscore the importance for lecturers in rehabilitation to cultivate a diverse set of skills drawn from the arts and performance, education, team building and leadership to facilitate students' learning process. By developing these skills, lecturers can design lessons that are worth attending not only for their relevant content but also for their value in human experience.

ChatGPT goes to the operating room: evaluating GPT-4 performance and its potential in surgical education and training in the era of large language models

Purpose

This study aimed to assess the performance of ChatGPT, specifically the GPT-3.5 and GPT-4 models, in understanding complex surgical clinical information and its potential implications for surgical education and training.

Methods

The dataset comprised 280 questions from the Korean general surgery board exams conducted between 2020 and 2022. Both GPT-3.5 and GPT-4 models were evaluated, and their performances were compared using McNemar test.

Results

GPT-3.5 achieved an overall accuracy of 46.8%, while GPT-4 demonstrated a significant improvement with an overall accuracy of 76.4%, indicating a notable difference in performance between the models (P < 0.001). GPT-4 also exhibited consistent performance across all subspecialties, with accuracy rates ranging from 63.6% to 83.3%.

Conclusion

ChatGPT, particularly GPT-4, demonstrates a remarkable ability to understand complex surgical clinical information, achieving an accuracy rate of 76.4% on the Korean general surgery board exam. However, it is important to recognize the limitations of large language models and ensure that they are used in conjunction with human expertise and judgment.

Evaluation of medical school faculty members' educational performance in Korea in 2022 through analysis of the promotion regulations: a mixed methods study

PURPOSE

To ensure faculty members' active participation in education in response to growing demand, medical schools should clearly describe educational activities in their promotion regulations. This study analyzed the status of how medical education activities are evaluated in promotion regulations in 2022, in Korea.

METHODS

Data were collected from promotion regulations retrieved by searching the websites of 22 medical schools/universities in August 2022. To categorize educational activities and evaluation methods, the Association of American Medical Colleges framework for educational activities was utilized. Correlations between medical schools' characteristics and the evaluation of medical educational activities were analyzed.

RESULTS

We defined 6 categories, including teaching, development of education products, education administration and service, scholarship in education, student affairs, and others, and 20 activities with 57 sub-activities. The average number of included activities was highest in the development of education products category and lowest in the scholarship in education category. The weight adjustment factors of medical educational activities were the characteristics of the target subjects and faculty members, the number of involved faculty members, and the difficulty of activities. Private medical schools tended to have more educational activities in the regulations than public medical schools. The greater the number of faculty members, the greater the number of educational activities in the education administration and service categories.

CONCLUSION

Medical schools included various medical education activities and their evaluation methods in promotion regulations in Korea. This study provides basic data for improving the rewarding system for efforts of medical faculty members in education.