Applications and Challenges of Implementing Artificial Intelligence in Medical Education: 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.

From static web to metaverse: reinventing medical education in the post-pandemic era

The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.

Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey

BACKGROUND

Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors.

OBJECTIVE

This study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt.

METHODS

An online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI.

RESULTS

There are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively).

CONCLUSION

The study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.

Application of artificial intelligence in medical education: A meta-ethnographic synthesis

With the advancement of Artificial Intelligence (AI), it has had a profound impact on medical education. Understanding the advantages and issues of AI in medical education, providing guidance for educators, and overcoming challenges in the implementation process is particularly important.

The objective of this study is to explore the current state of AI applications in medical education.

A systematic search was conducted across databases such as PsycINFO, CINAHL, Scopus, PubMed, and Web of Science to identify relevant studies. The Critical Appraisal Skills Programme (CASP) was employed for the quality assessment of these studies, followed by thematic synthesis to analyze the themes from the included research.

Ultimately, 21 studies were identified, establishing four themes: (1) Shaping the Future: Current Trends in AI within Medical Education; (2) Advancing Medical Instruction: The Transformative Power of AI; (3) Navigating the Ethical Landscape of AI in Medical Education; (4) Fostering Synergy: Integrating Artificial Intelligence in Medical Curriculum.

Artificial intelligence's role in medical education, while not yet extensive, is impactful and promising. Despite challenges, including ethical concerns over privacy, responsibility, and humanistic care, future efforts should focus on integrating AI through targeted courses to improve educational quality.

Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey

BACKGROUND

Artificial intelligence (AI) is transforming health profession education (HPE) through personalized learning technologies. HPE students must also learn about AI to understand its impact on healthcare delivery. We examined HPE students' AI-related knowledge and attitudes, and perceived challenges in integrating AI in HPE.

METHODS

This cross-sectional included medical, nursing, physiotherapy, and clinical nutrition students from four public universities in Jordan, the Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), and Egypt. Data were collected between February and October 2023 via an online survey that covered five main domains: benefits of AI in healthcare, negative impact on patient trust, negative impact on the future of healthcare professionals, inclusion of AI in HPE curricula, and challenges hindering integration of AI in HPE.

RESULTS

Of 642 participants, 66.4% reported low AI knowledge levels. The UAE had the largest proportion of students with low knowledge (72.7%). The majority (54.4%) of participants had learned about AI outside their curriculum, mainly through social media (66%). Overall, 51.2% expressed positive attitudes toward AI, with Egypt showing the largest proportion of positive attitudes (59.1%). Although most participants viewed AI in healthcare positively (91%), significant variations were observed in other domains. The majority (77.6%) supported integrating AI in HPE, especially in Egypt (82.3%). A perceived negative impact of AI on patient trust was expressed by 43.5% of participants, particularly in Egypt (54.7%). Only 18.1% of participants were concerned about the impact of AI on future healthcare professionals, with the largest proportion from Egypt (33.0%). Some participants (34.4%) perceived AI integration as challenging, notably in the UAE (47.6%). Common barriers included lack of expert training (53%), awareness (50%), and interest in AI (41%).

CONCLUSION

This study clarified key considerations when integrating AI in HPE. Enhancing students' awareness and fostering innovation in an AI-driven medical landscape are crucial for effectively incorporating AI in HPE curricula.

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.

Advancing optometry education through global frameworks and international collaborations

Global curriculum initiatives aim to enhance the education of optometrists across the world. This is done by establishing competencies and frameworks necessary for consistency in education and training. Through collaboration and knowledge exchange between educators and institutions, future optometrists can be equipped with the latest evidence-based knowledge and skills to deliver quality eye care, regardless of geographical location. This paper explores the concept of a global curriculum by investigating the global similarities and differences in definitions of optometry, regulation of the profession, assessment of competency, accreditation standards for education providers, curriculum frameworks, and scope of practice. Despite the challenges of advancing optometric education, there appear to be many opportunities to explore collaboration on an international scale. Three case studies are presented which demonstrate international collaborations among education providers to train local optometrists. Future technological advancements and the use of artificial intelligence may assist the development and delivery of a global curriculum.

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties

BACKGROUND

The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions?

METHODS

An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann-Whitney U-test, Kruskal-Wallis test, and Dunn-Bonferroni post hoc test.

RESULTS

The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3-4) and a desire for more AI teaching (median: 4, IQR: 4-5). However, they had limited AI knowledge (median: 2, IQR: 2-2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1-3). Subgroup analyses revealed significant differences between the Global North and South (r = 0.025 to 0.185, all P < .001) and across continents (r = 0.301 to 0.531, all P < .001), with generally small effect sizes.

CONCLUSIONS

This large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs.

TRIAL REGISTRATION

Not applicable (no clinical trial).

Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine

The digitization of medicine will play an increasingly significant role in future years. In particular, telemedicine, Virtual Reality (VR) and innovative Artificial Intelligence (AI) systems offer tremendous potential in imaging diagnostics and are expected to shape ultrasound diagnostics and teaching significantly. However, it is crucial to consider the advantages and disadvantages of employing these new technologies and how best to teach and manage their use. This paper provides an overview of telemedicine, VR and AI in student ultrasound education, presenting current perspectives and controversies.

Twelve tips for Natural Language Processing in medical education program evaluation

With the increasing application of Natural Language Processing (NLP) in Medicine at large, medical educators are urged to gain an understanding and implement NLP techniques within their own education programs to improve the workflow and make significant and rapid improvements in their programs. This paper aims to provide twelve essential tips inclusive of both conceptual and technical factors to facilitate the successful integration of NLP in medical education program evaluation. These twelve tips range from advising on various stages of planning the evaluation process, considerations for data collection, and reflections on preprocessing of data in preparation for analysis and interpretation of results. Using these twelve tips as a framework, medical researchers, educators, and administrators will have an understanding and reference to navigating applications of NLP and be able to unlock its potential for enhancing the evaluation of their own medical education programs.

Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?

Clinical decision-making regarding syncope poses challenges, with risk of physician error due to the elusive nature of syncope pathophysiology, diverse presentations, heterogeneity of risk factors, and limited therapeutic options. Artificial intelligence (AI)-based techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can uncover hidden and nonlinear connections among syncope risk factors, disease features, and clinical outcomes. ML, DL, and NLP models can analyze vast amounts of data effectively and assist physicians to help distinguish true syncope from other types of transient loss of consciousness. Additionally, short-term adverse events and length of hospital stay can be predicted by these models. In syncope research, AI-based models shift the focus from causality to correlation analysis between entities. This prompts the search for patterns rather than defining a hypothesis to be tested a priori. Furthermore, education of students, doctors, and health care providers engaged in continuing medical education may benefit from clinical cases of syncope interacting with NLP-based virtual patient simulators. Education may be of benefit to patients. This article explores potential strengths, weaknesses, and proposed solutions associated with utilization of ML and DL in syncope diagnosis and management. Three main topics regarding syncope are addressed: 1) clinical decision-making; 2) clinical research; and 3) education. Within each domain, we question whether "AI will be better than humans," seeking evidence to support our objective inquiry.

Artificial intelligence in dermatology: Bridging the gap in patient care and education

The application of artificial intelligence (AI) in education and clinical medicine has shown tremendous growth. The primary explanation for this application is AI's ability to integrate efficient and tailored methods for screening, using diagnostics, and enhancement of patient and medical education. AI's wide scope of utility can be seen through its ability to improve efficiency in clinical settings through scheduling, charting, diagnostics, and screening tools, ultimately allowing physicians to spend more focused time on patient care. AI has also had a tangible impact on promoting patient education through its ability to provide patients with preliminary information regarding their diagnoses before followup and to further discussion with their physician. AI's application in medical education is promising due to its ability to provide immediate and interactive feedback to the learner, which allows for meaningful reinforcement of knowledge. AI can therefore be recognized as a tool that can provide incredible enhancement in the areas of clinical medicine and education, with meaningful opportunities for integration and application.

The potential role and restrictions of artificial intelligence in medical school dermatology education

Artificial intelligence (AI) is a rapidly developing field with the potential to transform various aspects of health care and public health, including medical training. The use of AI is still being studied to understand better how to integrate its innumerable applications into modern medicine and how it is taught. Medical school dermatology education in particular stands to benefit from AI, especially when considering medical schools that lack dermatology curricula. In this review, we evaluate the integration of AI technology in the field of dermatology and how it can inform how dermatology is taught in medical schools across the United States.

Enhancing medical students critical thinking skills through ChatGPT: An empirical study with medical students

STUDY PURPOSE

This study aims to assess the effectiveness of ChatGPT in critical thinking skills among medical students.

METHODS

This cross-sectional survey study recruited 392 medical students from three public universities in Saudi Arabia. Participants completed an online questionnaire assessing perceptions of ChatGPT's impact on critical thinking skills. Data were analyzed using SPSS, employing descriptive statistics, t-tests, analysis of variance, and Cronbach's alpha to evaluate reliability.

RESULTS

Significant gender-based differences were found in perceptions of ChatGPT's efficacy, particularly in generating diverse perspectives (P = 0.0407*) and encouraging questioning (P = 0.0277*). Reflective practice perceptions varied significantly by age (P = 0.0302*), while academic backgrounds yielded significant differences across all factors assessed (P < 0.0001*). Overall, 92.6% believed integrating ChatGPT would benefit critical thinking skills. Most participants (N = 174) strongly agreed that ChatGPT improved critical thinking.

CONCLUSION

Integrating ChatGPT into medical education could offer valuable opportunities for fostering critical thinking abilities, albeit with the need for addressing associated challenges and ensuring inclusivity.

Leadership for AI Transformation in Health Care Organization: Scoping Review

BACKGROUND

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

OBJECTIVE

In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.

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 health care 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) guidelines.

RESULTS

A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors.

CONCLUSIONS

In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.

Generative artificial intelligence and the personalization of health professional education: A narrative review

This narrative review examined the intersection of generative artificial intelligence (GAI) and the personalization of health professional education (PHE). This review aims to the elucidate the current condition of GAI technologies and their particular uses in the field of PHE. Data were extracted and analyzed from studies focusing on the demographics and professional development preferences of healthcare workers, the competencies required for personalized precision medicine, and the current and potential applications of artificial intelligence (AI) in PHE. The review also addressed the ethical implications of AI implementation in this context. Findings indicated a gender-balanced healthcare workforce with a predisposition toward continuous professional development and digital tool utilization. A need for a comprehensive educational framework was identified to include a spectrum of skills crucial for precision medicine, emphasizing the importance of patient involvement and bioethics. AI was found to enhance educational experiences and research in PHE, with an increasing trend in AI applications, particularly in surgical education since 2018. Ethical challenges associated with AI integration in PHE were highlighted, with an emphasis on the need for ethical design and diverse development teams. Core concepts in AI research were established, with a spotlight on emerging areas such as data science and learning analytics. The application of AI in PHE was recognized for its current benefits and potential for future advancements, with a call for ethical vigilance. GAI holds significant promise for personalizing PHE, with an identified need for ethical frameworks and diverse developer teams to address bias and equity in educational AI applications.

Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology

BACKGROUND

Clinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning).

OBJECTIVES

This study aims to develop a multi-expert knowledge-aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital.

METHODS

The MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire.

RESULTS

Both UPGY (z = -3.976, P < 0.001) and ENT-R (z = -2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = -1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = -4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales.

CONCLUSIONS

The MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.

Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study

INTRODUCTION

The purpose of this study was to evaluate the perspectives of aspiring orthopaedic surgeons on artificial intelligence (AI), analysing how gender, AI knowledge, and technical inclination influence views on AI. Additionally, the extent to which recent AI advancements sway career decisions was assessed.

MATERIALS AND METHODS

A digital survey was distributed to student members of orthopaedic societies across Germany, Switzerland, and Austria. Subgroup analyses explored how gender, AI knowledge, and technical inclination shape attitudes towards AI.

RESULTS

Of 174 total respondents, 86.2% (n = 150) intended to pursue a career in orthopaedic surgery and were included in the analysis. The majority (74.5%) reported 'basic' or 'no' knowledge about AI. Approximately 29.3% believed AI would significantly impact orthopaedics within 5 years, with another 35.3% projecting 5-10 years. AI was predominantly seen as an assistive tool (77.8%), without significant fear of job displacement. The most valued AI applications were identified as preoperative implant planning (85.3%), administrative tasks (84%), and image analysis (81.3%). Concerns arose regarding skill atrophy due to overreliance (69.3%), liability (68%), and diminished patient interaction (56%). The majority maintained a 'neutral' view on AI (53%), though 32.9% were 'enthusiastic'. A stronger focus on AI in medical education was requested by 81.9%. Most participants (72.8%) felt recent AI advancements did not alter their career decisions towards or away from the orthopaedic specialty. Statistical analysis revealed a significant association between AI literacy (p = 0.015) and technical inclination (p = 0.003). AI literacy did not increase significantly during medical education (p = 0.091).

CONCLUSIONS

Future orthopaedic surgeons exhibit a favourable outlook on AI, foreseeing its significant influence in the near future. AI literacy remains relatively low and showed no improvement during medical school. There is notable demand for improved AI-related education. The choice of orthopaedics as a specialty appears to be robust against the sway of recent AI advancements.

LEVEL OF EVIDENCE

Cross-sectional survey study; level IV.

Bridging the Artificial Intelligence (AI) Divide: Do Postgraduate Medical Students Outshine Undergraduate Medical Students in AI Readiness?

INTRODUCTION

As artificial intelligence (AI) transforms healthcare, medical education must adapt to equip future physicians with the necessary competencies. However, little is known about the differences in AI knowledge, attitudes, and practices between undergraduate and postgraduate medical students. This study aims to assess and compare AI knowledge, attitudes, and practices among undergraduate and postgraduate medical students, and to explore the associated factors and qualitative themes.

METHODS

A mixed-methods study was conducted, involving 605 medical students (404 undergraduates, 201 postgraduates) from a tertiary care center. Participants completed a survey assessing AI knowledge, attitudes, and practices. Semi-structured interviews and focus group discussions were conducted to explore qualitative themes. Quantitative data were analyzed using descriptive statistics, t-tests, chi-square tests, and regression analyses. Qualitative data underwent thematic analysis.

RESULTS

Postgraduate students demonstrated significantly higher AI knowledge scores than undergraduates (38.9±4.9 vs. 29.6±6.8, p<0.001). Both groups held positive attitudes, but postgraduates showed greater confidence in AI's potential (p<0.001). Postgraduates reported more extensive AI-related practices (p<0.001). Key qualitative themes included excitement about AI's potential, concerns about job security, and the need for AI education. AI knowledge, attitudes, and practices were positively correlated (p<0.01).

CONCLUSIONS

This study reveals a significant AI knowledge gap between undergraduate and postgraduate medical students, highlighting the need for targeted AI education. The findings can inform curriculum development and policies to prepare medical students for the AI-driven future of healthcare. Further research should explore the long-term impact of AI education on clinical practice.

Knowledge, attitude, and practice of artificial intelligence applications in medicine among physicians in Sudan: a national cross-sectional survey

Background and aims

Artificial intelligence (AI) has emerged as a rapidly developing tool within the medical landscape, globally aiding in diagnosis and healthcare management. However, its integration within healthcare systems remains varied across different regions. In Sudan, there exists a burgeoning interest in AI potential applications within medicine. This study aims to evaluate the knowledge, attitudes, and practices of AI applications in medicine among physicians in Sudan.

Methods

The authors conducted a web-based survey cross-sectional analytical study using an online questionnaire-based survey regarding demographic details, knowledge, attitudes, and practice of AI distributing through various e-mail listings and social media platforms. A sample of 825 Physicians including doctors in Sudan with different ranks and specialties were selected using the convenient non-probability sampling technique.

Result

Out of 825 Physicians, 666 (80.7%) of Physicians have previous knowledge about AI. However, only a small number 123 (14.9%) were taught about AI during their time in medical school, even fewer, just 120 (14.5%) had AI-related lessons in their training program. Regarding attitude, 675 (81.8%) agree that AI is very important in medicine, almost the same number, 681 (82.6%) support the idea of teaching AI in medical schools. Practically, 535 (64.8%) of doctors, think that should get special training in using AI tools in healthcare. Excitingly 651 (78.9%) of physicians are interested in working with AI in future. Based on different ranks of doctors toward AI; Medical Officers exhibited the highest proportion at (32.7%) of knowledge and understanding of AI concepts, followed by House Officers at (16.7%) (p=0.076); regarding attitude, Medical Officers demonstrated the highest (31.6%) favorable attitude, followed by House Officers at (17.5%) (p=0.229); In practice also, Medical Officer showed the highest portion (28.0%) among participants (p=0.129).

Conclusion

While there is a positive attitude and some level of AI practice, there remains a considerable gap in knowledge that needs addressing.

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.

The rise of ChatGPT: Exploring its potential in medical education

The integration of artificial intelligence (AI) into medical education has the potential to revolutionize the way students learn about biomedical sciences. Large language models, such as ChatGPT, can serve as virtual teaching assistants, providing students with detailed and relevant information and perhaps eventually interactive simulations. ChatGPT has the potential to increase student engagement and enhance student learning, though research is needed to confirm this. The challenges and limitations of ChatGPT must also be considered, including ethical issues and potentially harmful effects. It is crucial for medical educators to keep pace with technology's rapidly changing landscape and consider the implications for curriculum design, assessment strategies, and teaching methods. Continued research and evaluation are necessary to ensure the optimal integration of AI-based learning tools into medical education.

Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study

Introduction

In this cross-sectional study, the authors explored the knowledge, attitudes, and practices related to artificial intelligence (AI) among medical students in Sudan. With AI increasingly impacting healthcare, understanding its integration into medical education is crucial. This study aimed to assess the current state of AI awareness, perceptions, and practical experiences among medical students in Sudan. The authors aimed to evaluate the extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine. Additionally, this study seeks to identify the factors influencing knowledge levels and explore the practical implementation of AI in the medical field.

Method

A web-based survey was distributed to medical students in Sudan via social media platforms and e-mail during October 2023. The survey included questions on demographic information, knowledge of AI, attitudes toward its applications, and practical experiences. The descriptive statistics, χ2 tests, logistic regression, and correlations were analyzed using SPSS version 26.0.

Results

Out of the 762 participants, the majority exhibited a basic understanding of AI, but detailed knowledge of its applications was limited. Positive attitudes toward the importance of AI in diagnosis, radiology, and pathology were prevalent. However, practical application of these methods was infrequent, with only a minority of the participants having hands-on experience. Factors influencing knowledge included the lack of a formal curriculum and gender disparities.

Conclusion

This study highlights the need for comprehensive AI education in medical training programs in Sudan. While participants displayed positive attitudes, there was a notable gap in practical experience. Addressing these gaps through targeted educational interventions is crucial for preparing future healthcare professionals to navigate the evolving landscape of AI in medicine.

Recommendations

Policy efforts should focus on integrating AI education into the medical curriculum to ensure readiness for the technological advancements shaping the future of healthcare.

Integration of Artificial Intelligence in Pediatric Education: Perspectives from Pediatric Medical Educators and Residents

OBJECTIVES

The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum.

METHODS

An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree.

RESULTS

A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools.

CONCLUSIONS

The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.

Enhancing clinical skills in pediatric trainees: a comparative study of ChatGPT-assisted and traditional teaching methods

BACKGROUND

As artificial intelligence (AI) increasingly integrates into medical education, its specific impact on the development of clinical skills among pediatric trainees needs detailed investigation. Pediatric training presents unique challenges which AI tools like ChatGPT may be well-suited to address.

OBJECTIVE

This study evaluates the effectiveness of ChatGPT-assisted instruction versus traditional teaching methods on pediatric trainees' clinical skills performance.

METHODS

A cohort of pediatric trainees (n = 77) was randomly assigned to two groups; one underwent ChatGPT-assisted training, while the other received conventional instruction over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises (Mini-CEX), with particular attention to professional conduct, clinical judgment, patient communication, and overall clinical skills. Trainees' acceptance and satisfaction with the AI-assisted method were evaluated through a structured survey.

RESULTS

Both groups performed similarly in theoretical exams, indicating no significant difference (p > 0.05). However, the ChatGPT-assisted group showed a statistically significant improvement in Mini-CEX scores (p < 0.05), particularly in patient communication and clinical judgment. The AI-teaching approach received positive feedback from the majority of trainees, highlighting the perceived benefits in interactive learning and skill acquisition.

CONCLUSION

ChatGPT-assisted instruction did not affect theoretical knowledge acquisition but did enhance practical clinical skills among pediatric trainees. The positive reception of the AI-based method suggests that it has the potential to complement and augment traditional training approaches in pediatric education. These promising results warrant further exploration into the broader applications of AI in medical education scenarios.

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.

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.

Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.

An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum

OBJECTIVE

This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area.

METHODS

AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity.

RESULTS

There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum.

CONCLUSION

This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

GPT-4/4V's performance on the Japanese National Medical Licensing Examination

BACKGROUND

Recent advances in Artificial Intelligence (AI) are changing the medical world, and AI will likely replace many of the actions performed by medical professionals. The overall clinical ability of the AI has been evaluated by its ability to answer a text-based national medical examination. This study uniquely assesses the performance of Open AI's ChatGPT against all Japanese National Medical Licensing Examination (NMLE), including images, illustrations, and pictures.

METHODS

We obtained the questions of the past six years of the NMLE (112th to 117th) from the Japanese Ministry of Health, Labour and Welfare website. We converted them to JavaScript Object Notation (JSON) format. We created an application programming interface (API) to output correct answers using GPT-4 for questions without images and GPT4-V(ision) or GPT4 console for questions with images.

RESULTS

The percentage of image questions was 723/2400 (30.1%) over the past six years. In all years, GPT-4/4V exceeded the minimum score the examinee should score. In total, over the six years, the percentage of correct answers for basic medical knowledge questions was 665/905 (73.5%); for clinical knowledge questions, 1143/1531 (74.7%); and for image questions 497/723 (68.7%), respectively.

CONCLUSIONS

Regarding medical knowledge, GPT-4/4V met the minimum criteria regardless of whether the questions included images, illustrations, and pictures. Our study sheds light on the potential utility of AI in medical education.

Medical students' AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments

BACKGROUND

Artificial intelligence (AI) is becoming increasingly important in healthcare. It is therefore crucial that today's medical students have certain basic AI skills that enable them to use AI applications successfully. These basic skills are often referred to as "AI literacy". Previous research projects that aimed to investigate medical students' AI literacy and attitudes towards AI have not used reliable and validated assessment instruments.

METHODS

We used two validated self-assessment scales to measure AI literacy (31 Likert-type items) and attitudes towards AI (5 Likert-type items) at two German medical schools. The scales were distributed to the medical students through an online questionnaire. The final sample consisted of a total of 377 medical students. We conducted a confirmatory factor analysis and calculated the internal consistency of the scales to check whether the scales were sufficiently reliable to be used in our sample. In addition, we calculated t-tests to determine group differences and Pearson's and Kendall's correlation coefficients to examine associations between individual variables.

RESULTS

The model fit and internal consistency of the scales were satisfactory. Within the concept of AI literacy, we found that medical students at both medical schools rated their technical understanding of AI significantly lower (MMS1 = 2.85 and MMS2 = 2.50) than their ability to critically appraise (MMS1 = 4.99 and MMS2 = 4.83) or practically use AI (MMS1 = 4.52 and MMS2 = 4.32), which reveals a discrepancy of skills. In addition, female medical students rated their overall AI literacy significantly lower than male medical students, t(217.96) = -3.65, p <.001. Students in both samples seemed to be more accepting of AI than fearful of the technology, t(745.42) = 11.72, p <.001. Furthermore, we discovered a strong positive correlation between AI literacy and positive attitudes towards AI and a weak negative correlation between AI literacy and negative attitudes. Finally, we found that prior AI education and interest in AI is positively correlated with medical students' AI literacy.

CONCLUSIONS

Courses to increase the AI literacy of medical students should focus more on technical aspects. There also appears to be a correlation between AI literacy and attitudes towards AI, which should be considered when planning AI courses.

Perceptions of Faculty and Students About Use of Artificial Intelligence in Medical Education: A Qualitative Study

BACKGROUND

Artificial intelligence (AI) implies using a computer to model intelligent behavior with minimal human intervention. With the advances of AI use in healthcare comes the need to reform medical education to produce doctors competent in AI use. Therefore, this qualitative study was conducted to explore faculty and students' perspectives on AI, their use of AI applications, and their perspective on its value and impact on medical education at a Saudi faculty of medicine.

METHODS

This qualitative study was conducted at the Faculty of Medicine, Jazan University in Saudi Arabia. A direct interview was held with 11 faculty members, and six focus group discussions were conducted with students from the second to sixth year (34 students). Data were collected using semi-structured open-ended interview questions based on relevant literature.

FINDINGS

Most respondents (91.11%) believed AI systems would positively impact medical education, especially in research, knowledge gain, assessment, and simulation. However, ethical concerns were raised about threats to academic integrity, plagiarism, privacy/confidentiality issues, and AI's lacking cultural sensitivity. Faculty and students felt a need for training on AI use (80%) and that the curriculum could adapt to integrate AI (64.44%), though resources were seen as currently needing to be improved.

CONCLUSION

AI's potential to enhance medical education is generally viewed positively in the study, but ethical concerns must be addressed. Integrating AI into medical education programs requires adequate resources, training, and curriculum adaptation. There is still a need for further research in this area to develop comprehensive strategies.

The Role of Artificial Intelligence in Surgery: What do General Surgery Residents Think?

BACKGROUND

Artificial intelligence (AI) holds significant potential in medical education and patient care, but its rapid emergence presents ethical and practical challenges. This study explored the perspectives of surgical residents on AI's role in medicine.

METHODS

We performed a cross-sectional study surveying general surgery residents at a university-affiliated teaching hospital about their views on AI in medicine and surgical training. The survey covered demographics, residents' understanding of AI, its integration into medical practice, and use of AI tools like ChatGPT. The survey design was inspired by a recent national survey and underwent pretesting before deployment.

RESULTS

Of the 31 participants surveyed, 24% identified diagnostics as AI's top application, 12% favored its use in identifying anatomical structures in surgeries, and 20% endorsed AI integration into EMRs for predictive models. Attitudes toward AI varied based on its intended application: 77.41% expressed concern about AI making life decisions and 70.97% felt excited about its application for repetitive tasks. A significant 67.74% believed AI could enhance the understanding of medical knowledge. Perception of AI integration varied with AI familiarity (P = .01), with more knowledgeable respondents expressing more positivity. Moreover, familiarity influenced the perceived academic use of ChatGPT (P = .039) and attitudes toward AI in operating rooms (P = .032). Conclusion: This study provides insights into surgery residents' perceptions of AI in medical practice and training. These findings can inform future research, shape policy decisions, and guide AI development, promoting a harmonious collaboration between AI and surgeons to improve both training and patient care.

Incorporating ChatGPT in Medical Informatics Education: Mixed Methods Study on Student Perceptions and Experiential Integration Proposals

BACKGROUND

The integration of artificial intelligence (AI) technologies, such as ChatGPT, in the educational landscape has the potential to enhance the learning experience of medical informatics students and prepare them for using AI in professional settings. The incorporation of AI in classes aims to develop critical thinking by encouraging students to interact with ChatGPT and critically analyze the responses generated by the chatbot. This approach also helps students develop important skills in the field of biomedical and health informatics to enhance their interaction with AI tools.

OBJECTIVE

The aim of the study is to explore the perceptions of students regarding the use of ChatGPT as a learning tool in their educational context and provide professors with examples of prompts for incorporating ChatGPT into their teaching and learning activities, thereby enhancing the educational experience for students in medical informatics courses.

METHODS

This study used a mixed methods approach to gain insights from students regarding the use of ChatGPT in education. To accomplish this, a structured questionnaire was applied to evaluate students' familiarity with ChatGPT, gauge their perceptions of its use, and understand their attitudes toward its use in academic and learning tasks. Learning outcomes of 2 courses were analyzed to propose ChatGPT's incorporation in master's programs in medicine and medical informatics.

RESULTS

The majority of students expressed satisfaction with the use of ChatGPT in education, finding it beneficial for various purposes, including generating academic content, brainstorming ideas, and rewriting text. While some participants raised concerns about potential biases and the need for informed use, the overall perception was positive. Additionally, the study proposed integrating ChatGPT into 2 specific courses in the master's programs in medicine and medical informatics. The incorporation of ChatGPT was envisioned to enhance student learning experiences and assist in project planning, programming code generation, examination preparation, workflow exploration, and technical interview preparation, thus advancing medical informatics education. In medical teaching, it will be used as an assistant for simplifying the explanation of concepts and solving complex problems, as well as for generating clinical narratives and patient simulators.

CONCLUSIONS

The study's valuable insights into medical faculty students' perspectives and integration proposals for ChatGPT serve as an informative guide for professors aiming to enhance medical informatics education. The research delves into the potential of ChatGPT, emphasizes the necessity of collaboration in academic environments, identifies subject areas with discernible benefits, and underscores its transformative role in fostering innovative and engaging learning experiences. The envisaged proposals hold promise in empowering future health care professionals to work in the rapidly evolving era of digital health care.

Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence

Importance

The lack of standardized genetics training in pediatrics residencies, along with a shortage of medical geneticists, necessitates innovative educational approaches.

Objective

To compare pediatric resident recognition of Kabuki syndrome (KS) and Noonan syndrome (NS) after 1 of 4 educational interventions, including generative artificial intelligence (AI) methods.

Design, Setting, and Participants

This comparative effectiveness study used generative AI to create images of children with KS and NS. From October 1, 2022, to February 28, 2023, US pediatric residents were provided images through a web-based survey to assess whether these images helped them recognize genetic conditions.

Interventions

Participants categorized 20 images after exposure to 1 of 4 educational interventions (text-only descriptions, real images, and 2 types of images created by generative AI).

Main Outcomes and Measures

Associations between educational interventions with accuracy and self-reported confidence.

Results

Of 2515 contacted pediatric residents, 106 and 102 completed the KS and NS surveys, respectively. For KS, the sensitivity of text description was 48.5% (128 of 264), which was not significantly different from random guessing (odds ratio [OR], 0.94; 95% CI, 0.69-1.29; P = .71). Sensitivity was thus compared for real images vs random guessing (60.3% [188 of 312]; OR, 1.52; 95% CI, 1.15-2.00; P = .003) and 2 types of generative AI images vs random guessing (57.0% [212 of 372]; OR, 1.32; 95% CI, 1.04-1.69; P = .02 and 59.6% [193 of 324]; OR, 1.47; 95% CI, 1.12-1.94; P = .006) (denominators differ according to survey responses). The sensitivity of the NS text-only description was 65.3% (196 of 300). Compared with text-only, the sensitivity of the real images was 74.3% (205 of 276; OR, 1.53; 95% CI, 1.08-2.18; P = .02), and the sensitivity of the 2 types of images created by generative AI was 68.0% (204 of 300; OR, 1.13; 95% CI, 0.77-1.66; P = .54) and 71.0% (247 of 328; OR, 1.30; 95% CI, 0.92-1.83; P = .14). For specificity, no intervention was statistically different from text only. After the interventions, the number of participants who reported being unsure about important diagnostic facial features decreased from 56 (52.8%) to 5 (7.6%) for KS (P < .001) and 25 (24.5%) to 4 (4.7%) for NS (P < .001). There was a significant association between confidence level and sensitivity for real and generated images.

Conclusions and Relevance

In this study, real and generated images helped participants recognize KS and NS; real images appeared most helpful. Generated images were noninferior to real images and could serve an adjunctive role, particularly for rare conditions.

Evaluating ChatGPT responses in the context of a 53-year-old male with a femoral neck fracture: a qualitative analysis

PURPOSE

The integration of artificial intelligence (AI) tools, such as ChatGPT, in clinical medicine and medical education has gained significant attention due to their potential to support decision-making and improve patient care. However, there is a need to evaluate the benefits and limitations of these tools in specific clinical scenarios.

METHODS

This study used a case study approach within the field of orthopaedic surgery. A clinical case report featuring a 53-year-old male with a femoral neck fracture was used as the basis for evaluation. ChatGPT, a large language model, was asked to respond to clinical questions related to the case. The responses generated by ChatGPT were evaluated qualitatively, considering their relevance, justification, and alignment with the responses of real clinicians. Alternative dialogue protocols were also employed to assess the impact of additional prompts and contextual information on ChatGPT responses.

RESULTS

ChatGPT generally provided clinically appropriate responses to the questions posed in the clinical case report. However, the level of justification and explanation varied across the generated responses. Occasionally, clinically inappropriate responses and inconsistencies were observed in the generated responses across different dialogue protocols and on separate days.

CONCLUSIONS

The findings of this study highlight both the potential and limitations of using ChatGPT in clinical practice. While ChatGPT demonstrated the ability to provide relevant clinical information, the lack of consistent justification and occasional clinically inappropriate responses raise concerns about its reliability. These results underscore the importance of careful consideration and validation when using AI tools in healthcare. Further research and clinician training are necessary to effectively integrate AI tools like ChatGPT, ensuring their safe and reliable use in clinical decision-making.

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.

Validity evidence supporting clinical skills assessment by artificial intelligence compared with trained clinician raters

BACKGROUND

Artificial intelligence (AI) is becoming increasingly used in medical education, but our understanding of the validity of AI-based assessments (AIBA) as compared with traditional clinical expert-based assessments (EBA) is limited. In this study, the authors aimed to compare and contrast the validity evidence for the assessment of a complex clinical skill based on scores generated from an AI and trained clinical experts, respectively.

METHODS

The study was conducted between September 2020 to October 2022. The authors used Kane's validity framework to prioritise and organise their evidence according to the four inferences: scoring, generalisation, extrapolation and implications. The context of the study was chorionic villus sampling performed within the simulated setting. AIBA and EBA were used to evaluate performances of experts, intermediates and novice based on video recordings. The clinical experts used a scoring instrument developed in a previous international consensus study. The AI used convolutional neural networks for capturing features on video recordings, motion tracking and eye movements to arrive at a final composite score.

RESULTS

A total of 45 individuals participated in the study (22 novices, 12 intermediates and 11 experts). The authors demonstrated validity evidence for scoring, generalisation, extrapolation and implications for both EBA and AIBA. The plausibility of assumptions related to scoring, evidence of reproducibility and relation to different training levels was examined. Issues relating to construct underrepresentation, lack of explainability, and threats to robustness were identified as potential weak links in the AIBA validity argument compared with the EBA validity argument.

CONCLUSION

There were weak links in the use of AIBA compared with EBA, mainly in their representation of the underlying construct but also regarding their explainability and ability to transfer to other datasets. However, combining AI and clinical expert-based assessments may offer complementary benefits, which is a promising subject for future research.

A scoping review of artificial intelligence within pharmacy education

OBJECTIVES

This scoping review aimed to summarize the available literature on the use of artificial intelligence (AI) in pharmacy education and identify gaps where additional research is needed.

FINDINGS

Seven studies specifically addressing the use of AI in pharmacy education were identified. Of these 7 studies, 5 focused on AI use in the context of teaching and learning, 1 on the prediction of academic performance for admissions, and the final study focused on using AI text generation to elucidate the benefits and limitations of ChatGPT use in pharmacy education.

SUMMARY

There are currently a limited number of available publications that describe AI use in pharmacy education. Several challenges exist regarding the use of AI in pharmacy education, including the need for faculty expertise and time, limited generalizability of tools, limited outcomes data, and several legal and ethical concerns. As AI use increases and implementation becomes more standardized, opportunities will be created for the inclusion of AI in pharmacy education.

Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence

The proliferation of generative artificial intelligence (AI) and its extensive potential for integration into many aspects of health care signal a transformational shift within the health care environment. In this context, medical education must evolve to ensure that medical trainees are adequately prepared to navigate the rapidly changing health care landscape. Medical education has moved toward a competency-based education paradigm, leading the Association of American Medical Colleges (AAMC) to define a set of Entrustable Professional Activities (EPAs) as its practical operational framework in undergraduate medical education. The AAMC's 13 core EPAs for entering residencies have been implemented with varying levels of success across medical schools. In this paper, we critically assess the existing core EPAs in the context of rapid AI integration in medicine. We identify EPAs that require refinement, redefinition, or comprehensive change to align with the emerging trends in health care. Moreover, this perspective proposes a set of "emerging" EPAs, informed by the changing landscape and capabilities presented by generative AI technologies. We provide a practical evaluation of the EPAs, alongside actionable recommendations on how medical education, viewed through the lens of the AAMC EPAs, can adapt and remain relevant amid rapid technological advancements. By leveraging the transformative potential of AI, we can reshape medical education to align with an AI-integrated future of medicine. This approach will help equip future health care professionals with technological competence and adaptive skills to meet the dynamic and evolving demands in health care.

Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan

INTRODUCTION

The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions.

METHODS

An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores.

RESULTS

Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps.

CONCLUSIONS

This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.

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.