What do medical students actually need to know about artificial intelligence?

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.

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools

PURPOSE

Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea.

METHOD

An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competencies from the optional ones.

RESULTS

The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional.

CONCLUSIONS

This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

A scoping review of artificial intelligence in medical education: BEME Guide No. 84

BACKGROUND

Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research.

METHODS

This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape.

RESULTS

The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.

CONCLUSION

The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

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.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education

The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.

Awareness and level of digital literacy among students receiving health-based education

BACKGROUND

Being digitally literate allows health-based science students to access reliable, up-to-date information efficiently and expands the capacity for continuous learning. Digital literacy facilitates effective communication and collaboration among other healthcare providers. It helps to navigate the ethical implications of using digital technologies and aids the use of digital tools in managing healthcare processes. Our aim in this study is to determine the digital literacy level and awareness of our students receiving health-based education in our university and to pave the way for supporting the current curriculum with courses on digital literacy when necessary.

METHOD

Students from Acibadem University who were registered undergraduate education for at least four years of health-based education, School of Medicine, Nutrition and Dietetics, Nursing, Physiotherapy and Rehabilitation, Psychology, Biomedical Engineering, Molecular Biology, and Genetics were included. The questionnaire consisted of 24 queries evaluating digital literacy in 7 fields: software and multimedia, hardware and technical problem solving, network and communication/collaboration, ethics, security, artificial intelligence (A.I.), and interest/knowledge. Two student groups representing all departments were invited for interviews according to the Delphi method.

RESULTS

The survey was completed by 476 students. Female students had less computer knowledge and previous coding education. Spearman correlation test showed that there were weak positive correlations between the years and the "software and multimedia," "ethics," "interest and knowledge" domains, and the average score. The students from Nursing scored lowest in the query after those from the Nutrition and Dietetics department. The highest scores were obtained by Biomedical Engineering students, followed by the School of Medicine. Participants scored the highest in "network" and "A.I." and lowest in "interest-knowledge" domains.

CONCLUSION

It is necessary to define the level of computer skills who start health-based education and shape the curriculum by determining which domains are weak. Creating an educational environment that fosters females' digital knowledge is recommended. Elective courses across faculties may be offered to enable students to progress and discuss various digital literacy topics. The extent to which students benefit from the digital literacy-supported curriculum may be evaluated. Thus, health-based university students are encouraged to acquire the computer skills required by today's clinical settings.

REGISTRATION

This study was approved by Acıbadem University and Acıbadem Healthcare Institutions Medical Research Ethics Committee (ATADEK) (11 November 2022, ATADEK registration: 2022-17-138) All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from the participants.

Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects

BACKGROUND

The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present.

OBJECTIVE

This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT.

METHODS

This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate.

RESULTS

Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant.

CONCLUSIONS

This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.

Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics

OBJECTIVES

Digital health (DH) is a revolution driven by digital technologies to improve health. Despite the importance of DH, curricular updates in healthcare university programs are scarce, and DH remains undervalued. Therefore, this report describes the first Junior Scientific Committee (JSC) focusing on DH at a nationwide congress, with the aim of affirming its importance for promoting DH in universities.

METHODS

The scientific committee of the Brazilian Congress of Health Informatics (CBIS) extended invitations to students engaged in health-related fields, who were tasked with organizing a warm-up event and a 4-hour session at CBIS. Additionally, they were encouraged to take an active role in a workshop alongside distinguished experts to map out the current state of DH in Brazil.

RESULTS

The warm-up event focused on the topic "Artificial intelligence in healthcare: is a new concept of health about to arise?" and featured remote discussions by three professionals from diverse disciplines. At CBIS, the JSC's inaugural presentation concentrated on delineating the present state of DH education in Brazil, while the second presentation offered strategies to advance DH, incorporating viewpoints from within and beyond the academic sphere. During the workshop, participants deliberated on the most crucial competencies for future professionals in the DH domain.

CONCLUSIONS

Forming a JSC proved to be a valuable tool to foster DH, particularly due to the valuable interactions it facilitated between esteemed professionals and students. It also supports the cultivation of leadership skills in DH, a field that has not yet received the recognition it deserves.

Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study

BACKGROUND

Artificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users.

OBJECTIVE

The aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers.

METHODS

A cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies.

RESULTS

Of 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively).

CONCLUSIONS

The surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.

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.

Digital competencies for Singapore's national medical school curriculum: a qualitative study

Studies have shown that national-level initiatives to equip medical students with relevant digital competencies carry many benefits. Yet, few countries have outlined such competencies for clinical practice in the core medical school curriculum. This paper identifies current training gaps at the national level in digital competencies needed by students in the formal curricula of all three medical schools in Singapore from the perspectives of clinical educators and institutional leaders. It bears implications for countries that intend to implement standardized learning objectives for training in these digital competencies. Findings were drawn from in-depth interviews with 19 clinical educators and leaders of local medical schools. Participants were recruited using purposive sampling. Data were interpreted using qualitative thematic analysis. Thirteen of the participants were clinical educators while 6 were deans or vice deans of education from one of the three medical schools in Singapore. While the schools have introduced some relevant courses, they are not standardized nationally. Moreover, the school's niche areas have not been leveraged upon for training in digital competencies. Participants across all schools acknowledged that more formal training is needed in digital health, data management, and applying the principles of digital technologies. Participants also noted that the healthcare needs of the population, patient safety, and safe procedures in the utilisation of digital healthcare technologies should be prioritized when determining the competencies needed by students. Additionally, participants highlighted the need for stronger collaboration among medical schools, and for a stronger link between current curriculum and clinical practice. The findings highlighted the need for better collaboration among medical schools in the sharing of educational resources and expertise. Furthermore, stronger collaborations with professional bodies and the healthcare system should be established to ensure that the goals and outcomes of medical education and the healthcare system are aligned.

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.

Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care

BACKGROUND

Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.

METHODS

We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.

RESULTS

We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.

CONCLUSION

Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as "consumers", "translators", or "developers". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.

Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices

Introduction

The increasing use of Artificial Intelligence (AI) in medicine has raised ethical concerns, such as patient autonomy, bias, and transparency. Recent studies suggest a need for teaching AI ethics as part of medical curricula. This scoping review aimed to represent and synthesize the literature on teaching AI ethics as part of medical education.

Methods

The PRISMA-SCR guidelines and JBI methodology guided a literature search in four databases (PubMed, Embase, Scopus, and Web of Science) for the past 22 years (2000-2022). To account for the release of AI-based chat applications, such as ChatGPT, the literature search was updated to include publications until the end of June 2023.

Results

1384 publications were originally identified and, after screening titles and abstracts, the full text of 87 publications was assessed. Following the assessment of the full text, 10 publications were included for further analysis. The updated literature search identified two additional relevant publications from 2023 were identified and included in the analysis. All 12 publications recommended teaching AI ethics in medical curricula due to the potential implications of AI in medicine. Anticipated ethical challenges such as bias were identified as the recommended basis for teaching content in addition to basic principles of medical ethics. Case-based teaching using real-world examples in interactive seminars and small groups was recommended as a teaching modality.

Conclusion

This scoping review reveals a scarcity of literature on teaching AI ethics in medical education, with most of the available literature being recent and theoretical. These findings emphasize the importance of more empirical studies and foundational definitions of AI ethics to guide the development of teaching content and modalities. Recognizing AI's significant impact of AI on medicine, additional research on the teaching of AI ethics in medical education is needed to best prepare medical students for future ethical challenges.

Western Australian medical students' attitudes towards artificial intelligence in healthcare

INTRODUCTION

Surveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students' attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum.

METHODS

A digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7th of September 2021 to the 7th of November 2021. Students' categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques.

RESULTS

Overall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20-29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes.

CONCLUSION

Medical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.

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.

Integrating ChatGPT in Medical Education: Adapting Curricula to Cultivate Competent Physicians for the AI Era

The rapid advancements in artificial intelligence (AI) language models, particularly ChatGPT (OpenAI, San Francisco, California, United States), necessitate the adaptation of medical education curricula to cultivate competent physicians in the AI era. In this editorial, we discuss short-term solutions and long-term adaptations for integrating ChatGPT into medical education. We recommend promoting digital literacy, developing critical thinking skills, and emphasizing evidence-based relevance as quick fixes. Long-term adaptations include focusing on the human factor, interprofessional collaboration, continuous professional development, and research and evaluation. By implementing these changes, medical educators can optimize medical education for the AI era, ensuring students are well prepared for a technologically advanced future in healthcare.

The need for digital health education among next-generation health workers in China: a cross-sectional survey on digital health education

BACKGROUND

Digital health is important for sustainable health systems and universal health coverage. Since the outbreak of COVID-19, many countries, including China, have promoted the introduction of digital health in their medical services. Developing the next generation of physicians with digital health knowledge and skills is a prerequisite for maximizing the potential of digital health.

OBJECTIVE

We aimed to understand the perception of digital health among Chinese medical students, the current implementation of digital health education in China, and the urgent need of medical students.

METHODS

Our cross-sectional survey was conducted online and anonymously among current medical students in China. We used descriptive statistical analysis to examine participant demographic characteristics and the demand for digital health education. Additional analysis was conducted by grouping responses by current participation in a digital health course.

RESULTS

A total of 2122 valid responses were received from 467 medical schools. Most medical students had positive expectations that digital health will change the future of medicine. Compared with wearable devices (85.53%), telemedicine (84.16%), and medical big data (86.38%), fewer respondents believed in the benefits of clinical decision support systems (CDSS) (63.81%). Most respondents said they urgently needed digital health knowledge and skills, and the teaching method of practical training and internship (78.02%) was more popular than the traditional lecture (10.54%). However, only 41.45% wanted to learn about the ethical and legal issues surrounding digital health.

CONCLUSIONS

Our study shows that the current needs of Chinese medical students for digital health education remain unmet. A national initiative on digital health education, is necessary and attention should be paid to digital health equity and education globally, focusing on CDSS and artificial intelligence. Ethics knowledge must also be included in medical curriculum. Students as Partners (SAP) is a promising approach for designing digital health courses.

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

BACKGROUND

The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context.

OBJECTIVE

We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education.

METHODS

For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data.

RESULTS

The qualitative content analysis led to the formation of three main categories ("Knowledge," "Interpretation," and "Application") with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one's related competencies.

CONCLUSIONS

The analyzed expert interviews' results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.

Technical Difficulties: Teaching Critical Philosophical Orientations toward Technology

Issue: Technological innovation is accelerating, creating less time to reflect on the impact new technologies will have on the medical profession. Modern technologies are becoming increasingly embedded in routine medical practice with far-reaching impacts on the patient-physician relationship and the very essence of the health professions. These impacts are often difficult to predict and can create unintended consequences for medical education. This article is driven by a main question: How do we prepare trainees to critically assess technologies that we cannot foresee and effectively use technology to support equitable and compassionate care? Evidence: We translate insights from the philosophy of technology into a proposal for integrating critical technical consciousness in medical curricula. We identify three areas required to develop critical consciousness with regard to emerging technologies. The first area is technical literacy, which involves not just knowing how to use technology, but also understanding its limitations and appropriate contexts for use. The second area is the ability to assess the social impact of technology. This practice requires understanding that while technification creates new possibilities it can also have adverse, unintended consequences. The third area is critical reflection on the relationship between 'the human' and 'the technical' as it relates to the values of the medical profession and professional identity formation. Human and technology are two sides of the same coin; therefore, thinking critically about technology also forces us to think about what we consider 'the human side of medicine'. Implications: Critical technical consciousness can be fostered through an educational program underpinned by the recognition that, although technological innovation can create new possibilities for healing, technology is never neutral. Rather, it is imperative to emphasize that technology is interwoven with the social fabric that is essential to healing. Like medication, technology can be both potion and poison.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals

PURPOSE

The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals.

METHOD

In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team.

RESULTS

Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care.

CONCLUSIONS

The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Clinical informatics training in medical school education curricula: a scoping review

OBJECTIVES

This scoping review evaluates the existing literature on clinical informatics (CI) training in medical schools. It aims to determine the essential components of a CI curriculum in medical schools, identify methods to evaluate the effectiveness of a CI-focused education, and understand its delivery modes.

MATERIALS AND METHODS

This review was informed by the methodological guidance of the Joanna Briggs Institute. Three electronic databases including PubMed, Scopus, and Web of Science were searched for articles discussing CI between January 2010 and December 2021.

RESULTS

Fifty-nine out of 3055 articles were included in our final analysis. Components of CI education include its utilization in clinical practice, ethical implications, key CI-related concepts, and digital health. Evaluation of educational effectiveness entails external evaluation by organizations external to the teaching institute, and internal evaluation from within the teaching institute. Finally, modes of delivery include various pedagogical strategies and teaching CI using a multidisciplinary approach.

DISCUSSION

Given the broad discussion on the required competencies, we propose 4 recommendations in CI delivery. These include situating CI curriculum within specific contexts, developing evidence-based guidelines for a robust CI education, developing validated assessment techniques to evaluate curriculum effectiveness, and equipping educators with relevant CI training.

CONCLUSION

The literature reveals that CI training in the core curricula will complement if not enhance clinical skills, reiterating the need to equip students with relevant CI competencies. Furthermore, future research needs to comprehensively address current gaps in CI training in different contexts, evaluation methodologies, and delivery modes to facilitate structured training.

Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector

Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.

Graphical Abstract

ChatGPT Output Regarding Compulsory Vaccination and COVID-19 Vaccine Conspiracy: A Descriptive Study at the Outset of a Paradigm Shift in Online Search for Information

BACKGROUND

Being on the verge of a revolutionary approach to gathering information, ChatGPT (an artificial intelligence (AI)-based language model developed by OpenAI, and capable of producing human-like text) could be the prime motive of a paradigm shift on how humans will acquire information. Despite the concerns related to the use of such a promising tool in relation to the future of the quality of education, this technology will soon be incorporated into web search engines mandating the need to evaluate the output of such a tool. Previous studies showed that dependence on some sources of online information (e.g., social media platforms) was associated with higher rates of vaccination hesitancy. Therefore, the aim of the current study was to describe the output of ChatGPT regarding coronavirus disease 2019 (COVID-19) vaccine conspiracy beliefs. and compulsory vaccination.

METHODS

The current descriptive study was conducted on January 14, 2023 using the ChatGPT from OpenAI (OpenAI, L.L.C., San Francisco, CA, USA). The output was evaluated by two authors and the degree of agreement regarding the correctness, clarity, conciseness, and bias was evaluated using Cohen's kappa.

RESULTS

The ChatGPT responses were dismissive of conspiratorial ideas about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) origins labeling it as non-credible and lacking scientific evidence. Additionally, ChatGPT responses were totally against COVID-19 vaccine conspiracy statements. Regarding compulsory vaccination, ChatGPT responses were neutral citing the following as advantages of this strategy: protecting public health, maintaining herd immunity, reducing the spread of disease, cost-effectiveness, and legal obligation, and on the other hand, it cited the following as disadvantages of compulsory vaccination: ethical and legal concerns, mistrust and resistance, logistical challenges, and limited resources and knowledge.

CONCLUSIONS

The current study showed that ChatGPT could be a source of information to challenge COVID-19 vaccine conspiracies. For compulsory vaccination, ChatGPT resonated with the divided opinion in the scientific community toward such a strategy; nevertheless, it detailed the pros and cons of this approach. As it currently stands, the judicious use of ChatGPT could be utilized as a user-friendly source of COVID-19 vaccine information that could challenge conspiracy ideas with clear, concise, and non-biased content. However, ChatGPT content cannot be used as an alternative to the original reliable sources of vaccine information (e.g., the World Health Organization [WHO] and the Centers for Disease Control and Prevention [CDC]).

A smarter perspective: Learning with and from AI-cases

Artificial intelligence (AI) has only partially (or not at all) been integrated into medical education, leading to growing concerns regarding how to train healthcare practitioners to handle the changes brought about by the introduction of AI. Programming lessons and other technical information into healthcare curricula has been proposed as a solution to support healthcare personnel in using AI or other future technology. However, integrating these core elements of computer science knowledge might not meet the observed need that students will benefit from gaining practical experience with AI in the direct application area. Therefore, this paper proposes a dynamic approach to case-based learning that utilizes the scenarios where AI is currently used in clinical practice as examples. This approach will support students' understanding of technical aspects. Case-based learning with AI as an example provides additional benefits: (1) it allows doctors to compare their thought processes to the AI suggestions and critically reflect on the assumptions and biases of AI and clinical practice; (2) it incentivizes doctors to discuss and address ethical issues inherent to technology and those already existing in current clinical practice; (3) it serves as a foundation for fostering interdisciplinary collaboration via discussion of different views between technologists, multidisciplinary experts, and healthcare professionals. The proposed knowledge shift from AI as a technical focus to AI as an example for case-based learning aims to encourage a different perspective on educational needs. Technical education does not need to compete with other essential clinical skills as it could serve as a basis for supporting them, which leads to better medical education and practice, ultimately benefiting patients.

The medical profession transformed by artificial intelligence: Qualitative study

BACKGROUND

Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear.

OBJECTIVE

We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns.

METHODS

We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed.

RESULTS

Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization.

CONCLUSIONS

The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.

The development path of the medical profession in China's engineering universities from the perspective of the 'four new' disciplines

In recent years, China has actively promoted the construction of first-class universities and disciplines of the world ('Double First-Class'), and built a new model of university development to solve Chinese problems and support high-quality economic and social development. In the context of China's efforts to promote the construction of new engineering, new medicine, new agriculture, and new liberal arts (referred to as the 'four new' disciplines), these disciplines are developing rapidly. As a specialty dealing with major life issues, medical education has become increasingly prominent. To enhance the comprehensive strength of universities, corresponding to the 'four new' disciplines strategy, engineering universities are building and developing medical specialties one after another. At present, the greatest problem in the medical specialty of engineering universities is the tendency to blindly follow trends and integrate new concepts with traditional methods. However, to date, the integration of medical and nonmedical specialties has been superficial and thus has not been successful. To address this problem, this paper, guided by the policies aimed at developing the 'four new' disciplines, analyses the current situation of traditional medicine education and professional development in engineering universities and proposes measures to enhance the competitiveness of new medicine in engineering universities, thereby promoting the development of universities.KEY MESSAGESThe implementation of the 'four new' disciplines is a strategic choice for higher education.Engineering technology is an efficient path and hands-on approach to solving medical problems.Interdisciplinary and comprehensive educational approaches play an important role in the development of medical science.

Artificial intelligence in medical education: a cross-sectional needs assessment

BACKGROUND

As the information age wanes, enabling the prevalence of the artificial intelligence age; expectations, responsibilities, and job definitions need to be redefined for those who provide services in healthcare. This study examined the perceptions of future physicians on the possible influences of artificial intelligence on medicine, and to determine the needs that might be helpful for curriculum restructuring.

METHODS

A cross-sectional multi-centre study was conducted among medical students country-wide, where 3018 medical students participated. The instrument of the study was an online survey that was designed and distributed via a web-based service.

RESULTS

Most of the medical students perceived artificial intelligence as an assistive technology that could facilitate physicians' access to information (85.8%) and patients to healthcare (76.7%), and reduce errors (70.5%). However, half of the participants were worried about the possible reduction in the services of physicians, which could lead to unemployment (44.9%). Furthermore, it was agreed that using artificial intelligence in medicine could devalue the medical profession (58.6%), damage trust (45.5%), and negatively affect patient-physician relationships (42.7%). Moreover, nearly half of the participants affirmed that they could protect their professional confidentiality when using artificial intelligence applications (44.7%); whereas, 16.1% argued that artificial intelligence in medicine might cause violations of professional confidentiality. Of all the participants, only 6.0% stated that they were competent enough to inform patients about the features and risks of artificial intelligence. They further expressed that their educational gaps regarding their need for "knowledge and skills related to artificial intelligence applications" (96.2%), "applications for reducing medical errors" (95.8%), and "training to prevent and solve ethical problems that might arise as a result of using artificial intelligence applications" (93.8%).

CONCLUSIONS

The participants expressed a need for an update on the medical curriculum, according to necessities in transforming healthcare driven by artificial intelligence. The update should revolve around equipping future physicians with the knowledge and skills to effectively use artificial intelligence applications and ensure that professional values and rights are protected.

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

German medical students´ views regarding artificial intelligence in medicine: A cross-sectional survey

BACKGROUND

Medical students will likely be most impacted by the envisaged move to artificial intelligence (AI) driven digital medicine, and there is a need to better understand their views regarding the use of AI technology in medicine. This study aimed to explore German medical students´ views about AI in medicine.

METHODS

A cross-sectional survey was conducted in October 2019 with all new medical students at the Ludwig Maximilian University of Munich and the Technical University Munich. This represented approximately 10% of all new medical students in Germany.

RESULTS

A total of 844 medical students participated (91.9% response rate). Two thirds (64.4%) did not feel well informed about AI in medicine. Just over a half (57.4%) of students thought that AI has useful applications in medicine, particularly in drug research and development (82.5%), less so for clinical uses. Male students were more likely to agree with advantages of AI, and female participants were more likely to be concerned about disadvantages. The vast majority of students thought that when AI is used in medicine that it is important that there are legal rules regarding liability (97%) and oversight mechanisms (93.7%), that physicians should be consulted prior to implementation (96.8%), that developers should be able to explain to them the details of the algorithm (95.6%), that algorithms should use representative data (93.9%), and that patients should always be informed when AI is used (93.5%).

CONCLUSIONS

Medical schools and continuing medical education organisers need to promptly develop programs to ensure that clinicians are able to fully realize the potential of AI technology. It is also important that legal rules and oversight are implemented to ensure that future clinicians are not faced with a workplace where important issues around responsibility are not clearly regulated.

The Cases for and against Artificial Intelligence in the Medical School Curriculum

Although artificial intelligence (AI) has immense potential to shape the future of medicine, its place in undergraduate medical education currently is unclear. Numerous arguments exist both for and against including AI in the medical school curriculum. AI likely will affect all medical specialties, perhaps radiology more so than any other. The purpose of this article is to present a balanced perspective on whether AI should be included officially in the medical school curriculum. After presenting the balanced point-counterpoint arguments, the authors provide a compromise. Keywords: Artificial Intelligence, Medical Education, Medical School Curriculum, Medical Students, Radiology, Use of AI in Education © RSNA, 2022.

Undergraduate Medical Students' and Interns' Knowledge and Perception of Artificial Intelligence in Medicine

PURPOSE

Artificial intelligence (AI) is playing an increasingly important role in healthcare and health professions education. This study explored medical students' and interns' knowledge of artificial intelligence (AI), perceptions of the role of AI in medicine, and preferences around the teaching of AI competencies.

METHODS

In this cross-sectional study, the authors used a previously validated Canadian questionnaire and gathered responses from students and interns at KIST Medical College, Nepal. Face validity and reliability of the tool were assessed by administering the questionnaire to 20 alumni as a pilot sample (Cronbach alpha = 0.6). Survey results were analyzed quantitatively (p-value = 0.05).

RESULTS

In total 216 students (37% response rate) participated. The median AI knowledge score was 11 (interquartile range 4), and the maximum possible score was 25. The score was higher among final year students (p = 0.006) and among those with additional training in AI (p = 0.040). Over 49% strongly agreed or agreed that AI will reduce the number of jobs for doctors. Many expect AI to impact their specialty choice, felt the Nepalese health-care system is ill-equipped to deal with the challenges of AI, and opined every student of medicine should receive training on AI competencies.

CONCLUSION

The lack of coverage of AI and machine learning in Nepalese medical schools has resulted in students being unaware of AI's impact on individual patients and the healthcare system. A high perceived willingness among respondents to learn about AI is a positive sign and a strong indicator of futuristic successful curricula changes. Systematic implementation of AI in the Nepalese healthcare system can be a potential tool in addressing health-care challenges related to resource and manpower constraints. Incorporating topics related to AI and machine learning in medical curricula can be a useful first step.

Artificial intelligence in healthcare: a primer for medical education in radiomics

The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.

Digitalization in Medicine: Are German Medical Students Well Prepared for the Future?

The German healthcare system is facing a major transformation towards digitalized medicine. The aim was to find out the attitude and the degree of preparation of upcoming medical professionals for digital medicine. By means of an online survey, medical students from 38 German faculties were asked about different topics concerning digitalization. Most students (70.0%) indicated that they had not had any university courses on digital topics. Thus, only 22.2% feel prepared for the technical reality of digitalized medicine. Most fear losing patient contact because of digitalized medicine and assume that the medical profession will not be endangered by digitalization. Security systems, data protection, infrastructure and inadequate training are cited as the top problems of digitalization in medicine. Medical students have major concerns about incorrect decisions and the consecutive medicolegal aspects of using digital support as part their treatment plans. Digitalization in medicine is progressing faster than it can currently be implemented in the practical work. The generations involved have different understandings of technology, and there is a lack of curricular training in medical schools. There must be a significant improvement in training in digital medical skills so that the current and future healthcare professionals are better prepared for digitalized medicine.

Machine learning and pre-medical education

Machine learning and artificial intelligence (AI)-driven technologies are contributing significantly to various facets of medicine and care management. It is likely that the next generation of healthcare professionals will be confronted with a series of innovations that are powered by AI, and they may not have sufficient time during their professional tenure to learn about the underlying machine learning frameworks that are driving these systems. Educating the aspiring clinicians and care providers with the right foundational courses in machine learning as part of postsecondary education will likely transform them as high-tech physicians and care providers of the future.

Insights from teaching artificial intelligence to medical students in Canada

Clinical artificial intelligence (AI) applications are rapidly developing but existing medical school curricula provide limited teaching covering this area. Here we describe an AI training curriculum we developed and delivered to Canadian medical undergraduates and provide recommendations for future training.

Impact of digital transformation on the future of medical education and practice

In this article, the author provides synopses of the factors that have finally propelled health-care education and practice to join, at times reluctantly, the overarching digital transformative process that has been swept other industries over the last few decades. The key contributors and driving forces that have energized the entry of health-care education and practices are mentioned. The roles of major universities, large technology companies, and the expanding roles of Artificial Intelligence and Machine Learning are described. The projected future developments are predicted to continue to be substantial, sweeping, and forcing changes that are unprecedented. Thus, academicians and practitioners should be alerted to what the rapidly changing landscape is likely to become and accordingly take steps to manage and preserve their roles or risk be left behind or worse be forced out.

Why Machine Learning Should Be Taught in Medical Schools

The rapid development of machine learning (ML) applications in healthcare promises to transform the landscape of healthcare. In order for ML advancements to be effectively utilized in clinical care, it is necessary for the medical workforce to be prepared to handle these changes. As physicians in training are exposed to a wide breadth of clinical tools during medical school, this offers an ideal opportunity to introduce ML concepts. A foundational understanding of ML will not only be practically useful for clinicians, but will also address ethical concerns for clinical decision making. While select medical schools have made effort to integrate ML didactics and practice into their curriculum, we argue that foundational ML principles should be taught broadly to medical students across the country.

Technology Literacy in Undergraduate Medical Education: Review and Survey of the US Medical School Innovation and Technology Programs

BACKGROUND

Modern innovations, like machine learning, genomics, and digital health, are being integrated into medical practice at a rapid pace. Physicians in training receive little exposure to the implications, drawbacks, and methodologies of upcoming technologies prior to their deployment. As a result, there is an increasing need for the incorporation of innovation and technology (I&T) training, starting in medical school.

OBJECTIVE

We aimed to identify and describe curricular and extracurricular opportunities for innovation in medical technology in US undergraduate medical education to highlight challenges and develop insights for future directions of program development.

METHODS

A review of publicly available I&T program information on the official websites of US allopathic medical schools was conducted in June 2020. Programs were categorized by structure and implementation. The geographic distribution of these categories across US regions was analyzed. A survey was administered to school-affiliated student organizations with a focus on I&T and publicly available contact information. The data collected included the founding year, thematic focus, target audience, activities offered, and participant turnout rate.

RESULTS

A total of 103 I&T opportunities at 69 distinct Liaison Committee on Medical Education-accredited medical schools were identified and characterized into the following six categories: (1) integrative 4-year curricula, (2) facilitated doctor of medicine/master of science dual degree programs in a related field, (3) interdisciplinary collaborations, (4) areas of concentration, (5) preclinical electives, and (6) student-run clubs. The presence of interdisciplinary collaboration is significantly associated with the presence of student-led initiatives (P=.001). "Starting and running a business in healthcare" and "medical devices" were the most popular thematic focuses of student-led I&T groups, representing 87% (13/15) and 80% (12/15) of respondents, respectively. "Career pathways exploration for students" was the only type of activity that was significantly associated with a high event turnout rate of >26 students per event (P=.03).

CONCLUSIONS

Existing school-led and student-driven opportunities in medical I&T indicate growing national interest and reflect challenges in implementation. The greater visibility of opportunities, collaboration among schools, and development of a centralized network can be considered to better prepare students for the changing landscape of medical practice.