Assessing Digital and AI-Readiness in Medical Education: A Delphi-Based Development of a Digital Health Competency Questionnaire

Assessing Digital and AI-Readiness in Medical Education: A Delphi-Based Development of a Digital Health Competency Questionnaire | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Digital and AI-Readiness in Medical Education: A Delphi-Based Development of a Digital Health Competency Questionnaire Marciana Nona Duma, Stefan Knippen, Britta Wulfhorst, Inka Susanne Eisfeld, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9150280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background With digital health and artificial intelligence (AI) increasingly shaping medical practice, assessing medical students’ digital competencies, attitudes, and educational needs is essential for designing future-oriented medical curricula. This study aimed to develop and refine a comprehensive, evidence-based questionnaire to assess digital literacy and AI-related competencies among medical students, ensuring alignment with educational needs and healthcare trends. Methods A multi-round Delphi process involving medical educators and senior medical students was conducted to evaluate and revise an initial 26-item questionnaire. Panelists assessed each item’s relevance, clarity, and completeness. Revisions were guided by panel feedback and supported by targeted literature searches in PubMed. Only evidence-based changes were accepted. The final instrument contains 25 questions organized into four thematic sections: (1) Demographics and Background, (2) Proficiency in Technical Tools and Software, (3) Attitudes Toward Technology in Medical Practice, and (4) Expectations for Medical Education and Support. Results Twelve experts participated in the Delphi panel, including ten medical educators and two medical students. After two consensus rounds, the final questionnaire retained 25 items with an average agreement rate of 96%. Items were refined between rounds to improve clarity and align with contemporary digital health practices. Notably, attitude statements were structured to reflect the CanMEDS physician roles. The questionnaire captures students’ familiarity with digital and AI tools, usage frequency, device ownership, and educational expectations. Conclusions The resulting questionnaire offers a literature based, structured instrument for assessing digital literacy and AI readiness among medical students. It responds to gaps in existing tools and provides a foundation for curricular development, institutional benchmarking, and further research. Digital health education Artificial intelligence in medicine Digital literacy Medical education curriculum Clinical decision support systems Technology acceptance Digital skills assessment Medical education research Figures Figure 1 Background In the digital age, the ability to navigate, evaluate, and utilize digital tools effectively is a fundamental skill for academic and professional success [ 1 ]. While Prensky's "digital natives" concept suggested individuals born after 1980 possess inherent digital literacy [ 2 ] modern research challenges this assumption. Studies show that exposure to digital tools does not guarantee competence - even in highly connected countries like Japan, students with near-universal device ownership fail to utilize technology meaningfully for academic purposes [ 3 , 4 ]. Digital literacy goes beyond basic computer skills; it involves understanding how to use digital tools responsibly, ethically, and safely. This includes the ability to discern credible sources from misinformation, protect personal privacy, and engage in digital communication and collaboration [ 5 ]. Digital literacy is particularly crucial in medical education, where students must engage with digital resources for learning, research, clinical decision-making and patient reported outcomes. Despite growing emphasis on digital literacy, its integration into medical education faces significant barriers including educator expertise gaps [ 6 , 7 ], time constraints in already demanding curricula [ 7 ], and the rapid evolution of digital and artificial intelligence (AI) tools create barriers to effective implementation [ 8 ]. These challenges can affect students’ academic self-efficacy—their belief in their ability to succeed—and may contribute to academic procrastination [ 9 ]. The empirical results of the study of Yuan et al. [ 9 ] confirmed that digital literacy positively influenced academic self-efficacy and negatively correlated with academic procrastination. While several survey instruments were employed in studies on digital literacy in medical students, many lack detailed documentation of their development processes (including item generation, expert involvement and consensus, literature-based development or validation procedures) [ 10 – 21 ], are poorly reported and definitions of consensus also vary widely [ 22 , 23 ]. Given healthcare’s growing reliance on digital literacy and AI, assessing both general digital competence and AI literacy is essential for preparing future medical professionals [ 24 ] and ensuring their continued relevance, particularly in the context of debates over the potential redundancy of lengthy medical and law programs [ 25 ]. This study aimed to develop a scientifically grounded, Delphi consensus-based questionnaire to systematically assess medical students' digital and AI literacy. The resulting tool is intended to inform evidence-based curriculum design and guide targeted educational interventions. Material and Methods Study Design This study followed a rigorous Delphi process adapted from Diamond et al. [ 22 ]. In the following sections, each of these methodological steps—study objective, participant selection, consensus definition, and Delphi process—is described in detail, including how they were operationalized in the present study. Study Objective The objective of the Delphi study is to generate a set of questions that reflect expert consensus and effectively assess medical students’ competencies in digital literacy and artificial intelligence. Participants The panel will comprise experienced medical educators with demonstrated expertise in medical education, digital literacy, and AI applications. Purposive sampling will recruit approximately 10 experts from diverse institutions and disciplines. Medical students will be included to enhance survey relevance to the target population. Consensus Definition Consensus was defined a priori as ≥ 70% agreement among panelists [ 5 ]. Items not reaching this threshold will be removed. Delphi Process The Delphi process is described in Fig. 1 . Data Analysis Descriptive statistics (mean scores, range and percentages) will be used to evaluate expert ratings in round 2. Open-ended feedback will be analyzed thematically to incorporate qualitative insights into the questionnaire design. Results Panel composition The final panel consisted of twelve participants, including ten medical educators with expertise in digital health and curriculum design, and two senior medical students to represent the learner perspective. Delphi Process Round 1 – Initial Questionnaire Development For the initial development of the questions an initial literature review using PubMed to identify relevant studies on digital health, artificial intelligence (AI), and medical education was performed by MND. Search queries included combinations of Medical Subject Headings (MeSH) and free-text terms. Examples of search strings used were: "digital health"[Title/Abstract] AND "medical education"[Title/Abstract]; "artificial intelligence"[MeSH Terms] AND "undergraduate medical education"[Title/Abstract]; "digital literacy"[Title/Abstract] AND "medical students"[Title/Abstract]; "AI tools"[Title/Abstract] AND "clinical training"[Title/Abstract]; "technology acceptance"[Title/Abstract] AND "medical students"[Title/Abstract]; "curriculum"[Title/Abstract] AND ("eHealth" OR "digital health"). Inclusion criteria focused on peer-reviewed articles published in the past 15 years, written in English or German, that addressed digital competencies in medical education, student attitudes toward AI, and pedagogical strategies for integrating these technologies. Neither available instruments systematically evaluated student readiness for AI integration in clinical practice, machine learning platforms, or AI-enhanced diagnostics, nor did they incorporate curriculum-relevant roles (like CanMEDS) that reflect the evolving demands of future physicians [ 10 – 12 , 26 , 27 ]. The first draft of questions defined by MND took all this into account and included a combination of closed-ended (Likert-scale) and open-ended questions addressing key areas of digital and AI literacy. Experts were invited to assess each item for relevance, clarity, and comprehensiveness, and to suggest revisions or propose additional questions. All revisions and new items proposed by the panel during the first round of the Delphi process were reviewed by the first author through targeted literature search, again using PubMed. No proposed revision/item was excluded due to lack of supporting scientific literature; all proposed changes were grounded in published evidence and aligned with current developments in digital health and medical education and were thus incorporated into the adapted round 2 questionnaire. Round 2 – Refinement and Rating The initial version of the questionnaire comprised 26 questions organized into five thematic sections: (1) Demographics and Background Information, (2) Knowledge of Software, Apps, and Related Tools, (3) Attitudes Toward IT in Medical Practice, (4) Medical Specialty and Career Preferences, and (5) Perceptions of IT and the Future of Medicine. Experts provided detailed written feedback suggesting consolidation of redundant items, alignment of response formats (e.g., Likert vs. multiple-choice), and greater clarity in wording. Several panelists also noted a lack of integration between subjective attitude items and objective self-assessments, particularly in the section on digital tool usage. Based on this feedback, substantial revisions were made. The research team restructured the questionnaire into four main sections: (1) Demographics and Background, (2) Proficiency in Technical Tools and Software, (3) Attitudes Toward Technology in Medicine, and (4) Expectations for Medical Education and Institutional Support. Table 1 presents the overall structure and structural revisions between rounds. Table 1 Overview of Questionnaire Section Revisions Between Round 1 and Round 2 of the Delphi Process Section Round 1 (n = 5 sections) Round 2 (n = 4 sections) Demographics and Background Retained Retained with minor edits Software & App Knowledge Merged with Digital and AI Literacy Streamlined; combined objective and subjective items Attitudes Toward Information Technology in Practice Integrated into Attitudes Toward Technology section Refocused around CanMEDS competencies Career Preferences Integrated into Attitudes and Expectations Merged into specialty-related items and AI perspectives as well as demographics and background Views on Automation and AI Condensed and reworded for neutrality and clarity Integrated with general attitude and trust items Table 2 depicts the agreement of the panel on the items in the final round. Table 2 Summary of Agreement Rates and Item Inclusion (final round) Item Category Items Presented Items Retained Mean Agreement Rate (%) (range) Demographics and Background 7 7 92.85% (75%-100%) Proficiency in Technical Tools and Software 10 9 * 95.9% (67%-100%) Attitudes toward Technology in Medical practice 3 3 100% Expectations for institutional support 6 6 98.67% (92%-100%) Total 26 25 # (after consolidation) ~ 96% Note: * Question 16 (Q16) was removed during the final revision. It originally asked respondents to specify which medical gadgets or digital diagnostic tools they owned or used, as a follow-up to Q15. Since the examples in Q15 were already comprehensive, and due to overlapping content and expert panel feedback, this follow-up was deemed redundant and removed for clarity and conciseness. # Some items were merged or condensed based on overlapping content or panel feedback. Final consensus The final questionnaire consists of 25 items distributed across four thematic sections. Section 1, Demographics and Background Information , includes 7 questions utilizing single-choice, multiple-choice, and open-text formats to gather contextual data on participants. Section 2, Proficiency in Technical Tools and Software , comprises 9 items using Likert scales, multiple-choice options, and frequency scales to assess digital literacy and tool usage. Section 3, Attitudes Toward Technology in Medical Practice , includes 3 items designed to capture respondents’ perspectives through single-choice and Likert-scale responses. Finally, Section 4, Expectations for Medical Education and Support , includes 6 questions incorporating multiple-choice, Likert-scale, and open-text formats to explore students’ preferences for institutional support and educational content. The final version of the questionnaire includes items assessing self-reported familiarity and frequency of use for more than ten key digital tools, such as electronic health records (EHRs), clinical decision support systems (CDSS), learning management systems (LMS), and advanced artificial intelligence platforms like ChatGPT and TensorFlow. It also captures data on the ownership and use of medical gadgets and diagnostic devices, including digital stethoscopes, smartwatches, fitness trackers, and other wearable technologies. In addition, the questionnaire evaluates students’ attitudes toward digital health and artificial intelligence through statements aligned with the CanMEDS roles [ 26 ]. Furthermore, it explores the perceived impact of digital health and AI education on students' career planning and university selection. Finally, it collects insights into students' preferred learning formats and the types of institutional support they consider most helpful in developing digital competencies. The full questionnaire including the introductory text based on [ 1 , 2 , 4 , 5 , 7 – 9 ] is provided in the supplementary material. Discussion There is limited data on how medical students actually use AI and digital health tools, which tools they are proficient in, and how these skills vary by specialty choice and stage of training. Most studies rely on self-assessed confidence rather than combining subjective and objective measures of engagement. No available questionnaires have been developed in a systematic manner. To address these gaps, this study developed and refined a questionnaire to map students’ digital and AI competencies, usage patterns, and learning needs, providing actionable insights for curriculum design and targeted educational interventions. The final 25-item instrument was created through a two-round Delphi process involving medical educators and student representatives, achieving strong expert consensus and alignment with established competencies, including the CanMEDS roles. [ 26 ]. The following discussion examines the rationale and relevance of the final items in light of current literature on digital skills, AI integration, and educational needs in medical training. Section 1: Demographics and Background Information The questions herein aim to capture medical students’ demographic background, prior exposure to digital/AI training, and their preferences and interests regarding future medical specialties and career orientation. Demographic factors can influence attitudes toward digital health and AI [ 13 , 15 , 16 ]. Collecting such data—linked to students’ prior experience (Q3–Q6)—is essential for tailoring educational interventions as sudents’ stage in medical education affects their exposure to digital tools, with perceptions varying by level of training [ 10 ]. Only 6% of medical students felt in 2023 competent to inform patients about AI [ 28 ], but prior exposure to digital health increases confidence and preparedness in clinical practice. Further, patients often distrust AI, perceiving AI-labeled medical advice as less reliable and empathetic, reflecting an anti-AI bias [ 29 ] and nearly 70% of students fear a loss of humanistic aspects in medicine [ 17 , 28 ] making it important to explore preferences between patient-centered care and technology-driven practice (Q6) [ 30 ]. Moreover, certain specialties, such as radiology [ 31 – 33 ] and pathology [ 34 , 35 ], are more reliant on digital and AI tools, and insight into students’ residency preferences — particularly amid debates on the potential obsolescence of lengthy medical and law degrees [ 25 ] — can inform curriculum development to ensure relevant competencies are addressed in medical training, baseline competencies are identified and targeted programs are designed, potentially incorporating peer teaching. Section 2: Proficiency in Technical Tools and Software The next set of questions aim to objectively assess medical students’ current level of digital and AI-related skills, their familiarity and frequency of use of general, medical, and AI-specific digital tools, as well as their experience with medical gadgets or diagnostic technologies. A study by Blakemore et al. showed that students often overestimate their digital skills, underscoring the need for objective assessments to complement self-ratings [ 36 ]. Further, large-scale studies revealed that over 50% of participants drop out of app-based health studies within the first week, with younger populations disengaging particularly quickly despite high smartphone ownership, especially when clear incentives or alignment with daily routines are lacking [ 37 ].Nonetheless, research by Jarahi et al. found steady improvements in digital literacy among medical students, especially in word processing (78%) and presentation software (68%), though gaps persist in spreadsheets (49%) and email (34%) [ 18 ]. Q8 captures self-assessed digital literacy and AI knowledge, while Q9–Q17 provide a more objective evaluation of tool usage, familiarity, and frequency. We consider the assessment of the frequency of use particularly important, as for e.g. Clinical Decision Support Systems (CDSS) are integral to modern healthcare but can be significantly affected by system downtimes—reported by 96% of healthcare organizations [ 38 ]. Assessing how often students use these tools provides valuable insight into their practical integration during clinical training. Regular exposure, combined with targeted training on managing situations when such systems are unavailable, is essential for building both competence and confidence, and for ensuring preparedness during system outages. Together, Q8-Q17 offer a comprehensive picture of students’ digital competencies to guide tailored educational interventions. Thus, this survey included commonly used tools such as word processing, presentations, spreadsheets, cloud storage, video conferencing, and online survey tools as studies confirm their ubiquity in teaching, learning, and collaboration [ 18 – 20 , 39 – 47 ]. Beyond general tools, Q11–Q12 focus on medical applications. Familiarity with Electronic Health Records (EHRs) and CDSS is critical for practice, yet many students report low confidence, highlighting curricular gaps [ 15 , 21 , 48 – 52 ]. Similarly, databases (e.g., PubMed, UpToDate), Learning Management Systems platforms, telemedicine, and collaboration tools are widely used but often without formal training [ 19 , 43 – 45 , 53 ]. Q13–Q14 examine AI-related tools. Exposure to chatbots (e.g., ChatGPT), machine learning platforms, diagnostic AI, and computer vision applications is increasingly common, with evidence showing their growing role in clinical decision-making and education [ 17 , 54 – 71 ]. ChatGPT recently scored 67.1% on the Polish medical specialization exam (PES), correctly answering 80 questions [ 72 ] and performed at or near the 60% passing threshold on the USMLE [ 73 ]. Machine learning platforms provide the infrastructure and tools necessary for developing, training, and deploying machine learning models. For information technology students, these platforms are crucial for hands-on learning and building practical skills in AI and machine learning. Platforms like AWS SageMaker, Azure Machine Learning, Google Vertex AI, and Databricks are popular enterprise-level solutions. Additionally, tools like TensorFlow, Scikit-learn, and Keras, along with online courses on platforms like Coursera and edX, offer beginner-friendly resources for learning [ 74 , 75 ]. Although medical students may not build AI models from scratch, familiarity with these platforms supports interprofessional collaboration and equips future clinicians with the literacy needed to interpret, evaluate, and responsibly oversee AI tools. Regular use supports competence, though engagement varies with motivation and perceived relevance [ 17 , 54 ]. Given the rapid advancements in this area, these questions (Q13-14) will require regular updates if the questionnaire is to be used over time. Finally, Q15–Q16 assess ownership and use of digital medical devices. As shown by the AMA survey, adoption of tools such as handheld ultrasound or wearable monitors has expanded rapidly, mirroring trends among practicing physicians, who increasingly cite efficiency, accuracy, and care coordination as motivators for adoption [ 76 ]. Section 3: Attitudes Toward Technology in Medical Practice This section aims to explore medical students’ interest in digital innovations and AI in healthcare, their attitudes toward the role of technology in patient care, ethics, and teamwork, and their perceptions of opportunities and challenges in developing digital competence during medical training. Assessing students’ overall interest in technology provides insight into their motivation to adopt digital tools [ 37 ]. To further explore attitudes, items were developed around the CanMEDS framework [ 26 ], linking digital literacy and AI to core physician roles such as Medical Expert, Communicator, and Scholar. Table 3 outlines the statements used in the questionnaire to reflect the seven CanMEDS roles. Table 3 CanMED roles and correlated survey questions Corresponding CanMED role Statement Medical Expert A strong foundation in digital literacy and responsible use of AI tools is essential to providing accurate, efficient, and evidence-based patient care. Communicator While digital tools and AI can support communication, they must not compromise the quality of human connection in the doctor–patient relationship. Collaborator Digital platforms and AI-supported systems can enhance interdisciplinary teamwork and care coordination when used thoughtfully and transparently. Leader Physicians should take an active role in guiding the safe and effective integration of digital technologies and AI into clinical environments. Health Advocate Digital health technologies and AI have the potential to improve access to care and reduce disparities, but only if physicians advocate for equitable and ethical implementation. Scholar Lifelong learning in digital skills and the ability to critically evaluate AI applications are essential for maintaining medical competence. Professional Ethical, legal, and professional responsibilities include ensuring secure and just use of digital data and AI systems in patient care. While students and physicians generally acknowledge the importance of digital transformation in healthcare, prior research shows they may also feel uncertain, anxious, or unprepared to critically assess new tools [ 10 ][ 14 , 17 , 77 ]. To capture this complexity, Q19 combines general statements (e.g., the societal importance of AI) with personal reflections (e.g., confidence in using or evaluating digital tools). This mixed design helps distinguish between broad acceptance of technology and individual readiness, supporting future subscale development on perceived impact, digital self-efficacy, and emotional responses [ 77 – 89 ]. This mixed approach allows for a nuanced understanding of how medical students differentiate between perceived societal or professional trends and their own readiness, motivation, and self-efficacy. From a psychometric perspective, this structure also supports the later formation of subscales, such as the perceived impact of technology on medicine, the personal confidence and digital self-efficacy as well as emotional and motivational responses to AI. Section 4: Expectations for Medical Education and Support The questions herein focus on medical students’ expectations for digital health and AI education, their preferred learning formats and hands-on training opportunities, and the institutional support they consider most valuable for preparing to work in a technology-driven healthcare environment. Students increasingly value digital health education, seeking courses on data management, ethics, legal frameworks, research, and hands-on training [ 15 ]. Previous research shows medical students often lack confidence in eHealth but favor practical, supervised experiences[ 90 ]. Identifying preferred learning formats and support mechanisms helps shape effective, student-centered digital health education strategies [ 15 ]. Evaluating whether education in digital health and AI influences students’ choice of university is essential. Addressing these needs will not only help universities better prepare their students but also guide targeted policy and educational initiatives to remain competitive in the evolving healthcare education landscape. Limitations and Future Directions While the Delphi process enabled the structured development and expert validation of the questionnaire, certain limitations remain. Psychometric properties such as validity and reliability have not yet been systematically assessed. Future studies should therefore focus on the empirical evaluation of these quality criteria in diverse student populations. Moreover, while this expert consensus ensures fact and content validity, the dynamic nature of digital tools and AI applications in medicine necessitates regular updates and re-evaluations of questionnaire items to maintain relevance and accuracy. Conclusion This Delphi study successfully developed a consensus-based questionnaire to assess medical students’ digital literacy, familiarity with AI tools, and expectations for digital health education. Across two rounds, a diverse panel of educators and students refined the instrument, resulting in 25 validated items across four thematic areas. The final questionnaire captures essential competencies for future physicians—from tool usage to professional attitudes aligned with CanMEDS roles—and provides a robust framework to inform curricular innovations in technology-driven medical education. Declarations Acknowledgement · Human ethics approval and consent to participate o This study was conducted in accordance with the Declaration of Helsinki . Ethical approval was granted by the Ethics Committee of the Medical School Hamburg (Reference number: MSH-2025/33). o Informed consent was obtained from all individual participants included in the study. · Availability of data and materials o The final survey questions generated during this study are included in this published article and its supplementary information files. · Competing interests o Marciana Nona Duma: Received honoraria for lectures from AstraZeneca; unrelated to this study. o Stefan Knippen: Received honoraria for lectures from AstraZeneca; unrelated to this study. o Holger Joswig: Received research and travel grants from Miethke, Nevro, and Medtronic; unrelated to this study. o All other authors report no conflicts of interest. · Funding o No funding · Authors' contributions o MND: Conceptualized the study and led the design of the questionnaire. Coordinated the Delphi rounds, managed participant communication, analyzed feedback, and aligned questionnaire items with competency frameworks. o SK, FB: Provided key peer feedback during the Delphi process. o ISE, BW, CGE, WG, CE, ALF, MK, HJ, LK, JP, RP, SRS, MIS, KKW: Contributed to item revision, review of clarity, scope, feasibility, and alignment with medical curricula and AI competencies. o All authors contributed to the refinement of questionnaire items, critically reviewed the manuscript, and approved the final version. · AI Prompts o To improve clarity and structure, AI-based language models were used exclusively to optimize the phrasing of selected sentences. o The following prompts guided this process: · "Rephrase this." · "Fix phrasing, don’t change content." · "Polish the grammar, don’t change content." · “Shorten this.” References Estrela M, et al. Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis. 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Cardiol J. 2024;31(3):442–50. Kung TH, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198. Raschka S, Patterson J, Nolet C. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information. 2020;11. 10.3390/info11040193 . CloudOptimo. SageMaker vs Azure ML vs Google AI Platform: A comprehensive comparison. 2024. American Medical A. Physicians’ Motivations and Key Requirements for Adopting Digital Health Adoption and Attitudinal Shifts from 2016 to 2022. Alliance for Connected Care; 2022. Shanafelt TD, et al. Burnout and career satisfaction among American surgeons. Ann Surg. 2009;250(3):463–71. Jiang F, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. Sutton RT, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17. Laka M, et al. Challenges and opportunities in implementing clinical decision support systems (CDSS) at scale: Interviews with Australian policymakers. Health Policy Technol. 2022;11(3):100652. Sokol K, Fackler J, Vogt JE. Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap. npj Digit Med. 2025;8(1):345. Sharab L, Butul B, Guha U. Integrating Critical thinking and embracing Artificial Intelligence: Dual Pillars for advancing dental education. Saudi Dent J. 2024;36(12):1660–7. Morrow E, et al. Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Front Psychol. 2022;13:971044. Jayakumar P, et al. 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Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 25 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 17 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9150280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614709002,"identity":"b93803d9-5856-4e3f-9b16-5296dd66af3c","order_by":0,"name":"Marciana Nona Duma","email":"data:image/png;base64,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","orcid":"","institution":"MSH Medical School Hamburg - 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University of Applied Sciences and Medical University","correspondingAuthor":false,"prefix":"","firstName":"Franziska","middleName":"","lastName":"Baessler","suffix":""}],"badges":[],"createdAt":"2026-03-17 14:56:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9150280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9150280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106054878,"identity":"0843f1a2-2eb4-4e33-ab54-f5507d3f50b1","added_by":"auto","created_at":"2026-04-03 01:02:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129788,"visible":true,"origin":"","legend":"\u003cp\u003eDelphi process overview.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9150280/v1/5c22016931fb3b9efc2c87d2.png"},{"id":106094867,"identity":"7739ccb1-a864-479e-a588-94c6fe4c36ff","added_by":"auto","created_at":"2026-04-03 11:43:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":793539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9150280/v1/1d9ca5c5-4896-4776-87ca-783e0007fd34.pdf"},{"id":106054877,"identity":"710a5a94-6e69-467d-a611-0a49d39115a3","added_by":"auto","created_at":"2026-04-03 01:02:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":42260,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9150280/v1/f7ce22e9b3bb6482f678cb21.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Digital and AI-Readiness in Medical Education: A Delphi-Based Development of a Digital Health Competency Questionnaire","fulltext":[{"header":"Background","content":"\u003cp\u003eIn the digital age, the ability to navigate, evaluate, and utilize digital tools effectively is a fundamental skill for academic and professional success [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While Prensky's \"digital natives\" concept suggested individuals born after 1980 possess inherent digital literacy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] modern research challenges this assumption. Studies show that exposure to digital tools does not guarantee competence - even in highly connected countries like Japan, students with near-universal device ownership fail to utilize technology meaningfully for academic purposes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigital literacy goes beyond basic computer skills; it involves understanding how to use digital tools responsibly, ethically, and safely. This includes the ability to discern credible sources from misinformation, protect personal privacy, and engage in digital communication and collaboration [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigital literacy is particularly crucial in medical education, where students must engage with digital resources for learning, research, clinical decision-making and patient reported outcomes. Despite growing emphasis on digital literacy, its integration into medical education faces significant barriers including educator expertise gaps [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], time constraints in already demanding curricula [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the rapid evolution of digital and artificial intelligence (AI) tools create barriers to effective implementation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These challenges can affect students\u0026rsquo; academic self-efficacy\u0026mdash;their belief in their ability to succeed\u0026mdash;and may contribute to academic procrastination [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The empirical results of the study of Yuan et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] confirmed that digital literacy positively influenced academic self-efficacy and negatively correlated with academic procrastination.\u003c/p\u003e \u003cp\u003eWhile several survey instruments were employed in studies on digital literacy in medical students, many lack detailed documentation of their development processes (including item generation, expert involvement and consensus, literature-based development or validation procedures) [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], are poorly reported and definitions of consensus also vary widely [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Given healthcare\u0026rsquo;s growing reliance on digital literacy and AI, assessing both general digital competence and AI literacy is essential for preparing future medical professionals [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and ensuring their continued relevance, particularly in the context of debates over the potential redundancy of lengthy medical and law programs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to develop a scientifically grounded, Delphi consensus-based questionnaire to systematically assess medical students' digital and AI literacy. The resulting tool is intended to inform evidence-based curriculum design and guide targeted educational interventions.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e \u003cp\u003eThis study followed a rigorous Delphi process adapted from Diamond et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the following sections, each of these methodological steps\u0026mdash;study objective, participant selection, consensus definition, and Delphi process\u0026mdash;is described in detail, including how they were operationalized in the present study.\u003c/p\u003e \u003cp\u003eStudy Objective\u003c/p\u003e \u003cp\u003eThe objective of the Delphi study is to generate a set of questions that reflect expert consensus and effectively assess medical students\u0026rsquo; competencies in digital literacy and artificial intelligence.\u003c/p\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eThe panel will comprise experienced medical educators with demonstrated expertise in medical education, digital literacy, and AI applications. Purposive sampling will recruit approximately 10 experts from diverse institutions and disciplines. Medical students will be included to enhance survey relevance to the target population.\u003c/p\u003e \u003cp\u003eConsensus Definition\u003c/p\u003e \u003cp\u003eConsensus was defined a priori as \u0026ge;\u0026thinsp;70% agreement among panelists [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Items not reaching this threshold will be removed.\u003c/p\u003e \u003cp\u003eDelphi Process\u003c/p\u003e \u003cp\u003eThe Delphi process is described in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics (mean scores, range and percentages) will be used to evaluate expert ratings in round 2. Open-ended feedback will be analyzed thematically to incorporate qualitative insights into the questionnaire design.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePanel composition\u003c/p\u003e \u003cp\u003eThe final panel consisted of twelve participants, including ten medical educators with expertise in digital health and curriculum design, and two senior medical students to represent the learner perspective.\u003c/p\u003e \u003cp\u003eDelphi Process\u003c/p\u003e \u003cp\u003eRound 1 \u0026ndash; Initial Questionnaire Development\u003c/p\u003e \u003cp\u003eFor the initial development of the questions an initial literature review using PubMed to identify relevant studies on digital health, artificial intelligence (AI), and medical education was performed by MND. Search queries included combinations of Medical Subject Headings (MeSH) and free-text terms. Examples of search strings used were: \"digital health\"[Title/Abstract] AND \"medical education\"[Title/Abstract]; \"artificial intelligence\"[MeSH Terms] AND \"undergraduate medical education\"[Title/Abstract]; \"digital literacy\"[Title/Abstract] AND \"medical students\"[Title/Abstract]; \"AI tools\"[Title/Abstract] AND \"clinical training\"[Title/Abstract]; \"technology acceptance\"[Title/Abstract] AND \"medical students\"[Title/Abstract]; \"curriculum\"[Title/Abstract] AND (\"eHealth\" OR \"digital health\"). Inclusion criteria focused on peer-reviewed articles published in the past 15 years, written in English or German, that addressed digital competencies in medical education, student attitudes toward AI, and pedagogical strategies for integrating these technologies. Neither available instruments systematically evaluated student readiness for AI integration in clinical practice, machine learning platforms, or AI-enhanced diagnostics, nor did they incorporate curriculum-relevant roles (like CanMEDS) that reflect the evolving demands of future physicians [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe first draft of questions defined by MND took all this into account and included a combination of closed-ended (Likert-scale) and open-ended questions addressing key areas of digital and AI literacy. Experts were invited to assess each item for relevance, clarity, and comprehensiveness, and to suggest revisions or propose additional questions.\u003c/p\u003e \u003cp\u003eAll revisions and new items proposed by the panel during the first round of the Delphi process were reviewed by the first author through targeted literature search, again using PubMed. No proposed revision/item was excluded due to lack of supporting scientific literature; all proposed changes were grounded in published evidence and aligned with current developments in digital health and medical education and were thus incorporated into the adapted round 2 questionnaire.\u003c/p\u003e \u003cp\u003eRound 2 \u0026ndash; Refinement and Rating\u003c/p\u003e \u003cp\u003eThe initial version of the questionnaire comprised 26 questions organized into five thematic sections: (1) Demographics and Background Information, (2) Knowledge of Software, Apps, and Related Tools, (3) Attitudes Toward IT in Medical Practice, (4) Medical Specialty and Career Preferences, and (5) Perceptions of IT and the Future of Medicine. Experts provided detailed written feedback suggesting consolidation of redundant items, alignment of response formats (e.g., Likert vs. multiple-choice), and greater clarity in wording. Several panelists also noted a lack of integration between subjective attitude items and objective self-assessments, particularly in the section on digital tool usage. Based on this feedback, substantial revisions were made. The research team restructured the questionnaire into four main sections: (1) Demographics and Background, (2) Proficiency in Technical Tools and Software, (3) Attitudes Toward Technology in Medicine, and (4) Expectations for Medical Education and Institutional Support.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the overall structure and structural revisions between rounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of Questionnaire Section Revisions Between Round 1 and Round 2 of the Delphi Process\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRound 1 (n\u0026thinsp;=\u0026thinsp;5 sections)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound 2 (n\u0026thinsp;=\u0026thinsp;4 sections)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics and Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetained with minor edits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoftware \u0026amp; App Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerged with Digital and AI Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStreamlined; combined objective and subjective items\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitudes Toward Information Technology in Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntegrated into Attitudes Toward Technology section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRefocused around CanMEDS competencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCareer Preferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntegrated into Attitudes and Expectations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMerged into specialty-related items and AI perspectives as well as demographics and background\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViews on Automation and AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCondensed and reworded for neutrality and clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntegrated with general attitude and trust items\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the agreement of the panel on the items in the final round.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Agreement Rates and Item Inclusion (final round)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems Presented\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems Retained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Agreement Rate (%) (range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics and Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.85% (75%-100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProficiency in Technical Tools and Software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.9% (67%-100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitudes toward Technology in Medical practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectations for institutional support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.67% (92%-100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(after consolidation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e~\u0026thinsp;96%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eNote:\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003e* Question 16 (Q16) was removed during the final revision. It originally asked respondents to specify which medical gadgets or digital diagnostic tools they owned or used, as a follow-up to Q15. Since the examples in Q15 were already comprehensive, and due to overlapping content and expert panel feedback, this follow-up was deemed redundant and removed for clarity and conciseness.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e#\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSome items were merged or condensed based on overlapping content or panel feedback.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFinal consensus\u003c/p\u003e \u003cp\u003eThe final questionnaire consists of 25 items distributed across four thematic sections. Section 1, \u003cem\u003eDemographics and Background Information\u003c/em\u003e, includes 7 questions utilizing single-choice, multiple-choice, and open-text formats to gather contextual data on participants. Section 2, \u003cem\u003eProficiency in Technical Tools and Software\u003c/em\u003e, comprises 9 items using Likert scales, multiple-choice options, and frequency scales to assess digital literacy and tool usage. Section 3, \u003cem\u003eAttitudes Toward Technology in Medical Practice\u003c/em\u003e, includes 3 items designed to capture respondents\u0026rsquo; perspectives through single-choice and Likert-scale responses. Finally, Section 4, \u003cem\u003eExpectations for Medical Education and Support\u003c/em\u003e, includes 6 questions incorporating multiple-choice, Likert-scale, and open-text formats to explore students\u0026rsquo; preferences for institutional support and educational content. The final version of the questionnaire includes items assessing self-reported familiarity and frequency of use for more than ten key digital tools, such as electronic health records (EHRs), clinical decision support systems (CDSS), learning management systems (LMS), and advanced artificial intelligence platforms like ChatGPT and TensorFlow. It also captures data on the ownership and use of medical gadgets and diagnostic devices, including digital stethoscopes, smartwatches, fitness trackers, and other wearable technologies. In addition, the questionnaire evaluates students\u0026rsquo; attitudes toward digital health and artificial intelligence through statements aligned with the CanMEDS roles [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, it explores the perceived impact of digital health and AI education on students' career planning and university selection. Finally, it collects insights into students' preferred learning formats and the types of institutional support they consider most helpful in developing digital competencies. The full questionnaire including the introductory text based on [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] is provided in the supplementary material.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is limited data on how medical students actually use AI and digital health tools, which tools they are proficient in, and how these skills vary by specialty choice and stage of training. Most studies rely on self-assessed confidence rather than combining subjective and objective measures of engagement. No available questionnaires have been developed in a systematic manner. To address these gaps, this study developed and refined a questionnaire to map students\u0026rsquo; digital and AI competencies, usage patterns, and learning needs, providing actionable insights for curriculum design and targeted educational interventions. The final 25-item instrument was created through a two-round Delphi process involving medical educators and student representatives, achieving strong expert consensus and alignment with established competencies, including the CanMEDS roles. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe following discussion examines the rationale and relevance of the final items in light of current literature on digital skills, AI integration, and educational needs in medical training.\u003c/p\u003e \u003cp\u003eSection 1: Demographics and Background Information\u003c/p\u003e \u003cp\u003eThe questions herein aim to capture medical students\u0026rsquo; demographic background, prior exposure to digital/AI training, and their preferences and interests regarding future medical specialties and career orientation. Demographic factors can influence attitudes toward digital health and AI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Collecting such data\u0026mdash;linked to students\u0026rsquo; prior experience (Q3\u0026ndash;Q6)\u0026mdash;is essential for tailoring educational interventions as sudents\u0026rsquo; stage in medical education affects their exposure to digital tools, with perceptions varying by level of training [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Only 6% of medical students felt in 2023 competent to inform patients about AI [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but prior exposure to digital health increases confidence and preparedness in clinical practice. Further, patients often distrust AI, perceiving AI-labeled medical advice as less reliable and empathetic, reflecting an anti-AI bias [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and nearly 70% of students fear a loss of humanistic aspects in medicine [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] making it important to explore preferences between patient-centered care and technology-driven practice (Q6) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, certain specialties, such as radiology [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and pathology [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], are more reliant on digital and AI tools, and insight into students\u0026rsquo; residency preferences \u0026mdash; particularly amid debates on the potential obsolescence of lengthy medical and law degrees [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] \u0026mdash; can inform curriculum development to ensure relevant competencies are addressed in medical training, baseline competencies are identified and targeted programs are designed, potentially incorporating peer teaching.\u003c/p\u003e \u003cp\u003eSection 2: Proficiency in Technical Tools and Software\u003c/p\u003e \u003cp\u003eThe next set of questions aim to objectively assess medical students\u0026rsquo; current level of digital and AI-related skills, their familiarity and frequency of use of general, medical, and AI-specific digital tools, as well as their experience with medical gadgets or diagnostic technologies. A study by Blakemore et al. showed that students often overestimate their digital skills, underscoring the need for objective assessments to complement self-ratings [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Further, large-scale studies revealed that over 50% of participants drop out of app-based health studies within the first week, with younger populations disengaging particularly quickly despite high smartphone ownership, especially when clear incentives or alignment with daily routines are lacking [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].Nonetheless, research by Jarahi et al. found steady improvements in digital literacy among medical students, especially in word processing (78%) and presentation software (68%), though gaps persist in spreadsheets (49%) and email (34%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Q8 captures self-assessed digital literacy and AI knowledge, while Q9\u0026ndash;Q17 provide a more objective evaluation of tool usage, familiarity, and frequency. We consider the assessment of the frequency of use particularly important, as for e.g. Clinical Decision Support Systems (CDSS) are integral to modern healthcare but can be significantly affected by system downtimes\u0026mdash;reported by 96% of healthcare organizations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Assessing how often students use these tools provides valuable insight into their practical integration during clinical training. Regular exposure, combined with targeted training on managing situations when such systems are unavailable, is essential for building both competence and confidence, and for ensuring preparedness during system outages. Together, Q8-Q17 offer a comprehensive picture of students\u0026rsquo; digital competencies to guide tailored educational interventions. Thus, this survey included commonly used tools such as word processing, presentations, spreadsheets, cloud storage, video conferencing, and online survey tools as studies confirm their ubiquity in teaching, learning, and collaboration [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44 CR45 CR46\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Beyond general tools, Q11\u0026ndash;Q12 focus on medical applications. Familiarity with Electronic Health Records (EHRs) and CDSS is critical for practice, yet many students report low confidence, highlighting curricular gaps [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similarly, databases (e.g., PubMed, UpToDate), Learning Management Systems platforms, telemedicine, and collaboration tools are widely used but often without formal training [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQ13\u0026ndash;Q14 examine AI-related tools. Exposure to chatbots (e.g., ChatGPT), machine learning platforms, diagnostic AI, and computer vision applications is increasingly common, with evidence showing their growing role in clinical decision-making and education [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66 CR67 CR68 CR69 CR70\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. ChatGPT recently scored 67.1% on the Polish medical specialization exam (PES), correctly answering 80 questions [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and performed at or near the 60% passing threshold on the USMLE [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Machine learning platforms provide the infrastructure and tools necessary for developing, training, and deploying machine learning models. For information technology students, these platforms are crucial for hands-on learning and building practical skills in AI and machine learning. Platforms like AWS SageMaker, Azure Machine Learning, Google Vertex AI, and Databricks are popular enterprise-level solutions. Additionally, tools like TensorFlow, Scikit-learn, and Keras, along with online courses on platforms like Coursera and edX, offer beginner-friendly resources for learning [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Although medical students may not build AI models from scratch, familiarity with these platforms supports interprofessional collaboration and equips future clinicians with the literacy needed to interpret, evaluate, and responsibly oversee AI tools. Regular use supports competence, though engagement varies with motivation and perceived relevance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Given the rapid advancements in this area, these questions (Q13-14) will require regular updates if the questionnaire is to be used over time. Finally, Q15\u0026ndash;Q16 assess ownership and use of digital medical devices. As shown by the AMA survey, adoption of tools such as handheld ultrasound or wearable monitors has expanded rapidly, mirroring trends among practicing physicians, who increasingly cite efficiency, accuracy, and care coordination as motivators for adoption [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSection 3: Attitudes Toward Technology in Medical Practice\u003c/p\u003e \u003cp\u003eThis section aims to explore medical students\u0026rsquo; interest in digital innovations and AI in healthcare, their attitudes toward the role of technology in patient care, ethics, and teamwork, and their perceptions of opportunities and challenges in developing digital competence during medical training. Assessing students\u0026rsquo; overall interest in technology provides insight into their motivation to adopt digital tools [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To further explore attitudes, items were developed around the CanMEDS framework [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], linking digital literacy and AI to core physician roles such as Medical Expert, Communicator, and Scholar. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the statements used in the questionnaire to reflect the seven CanMEDS roles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCanMED roles and correlated survey questions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorresponding CanMED role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Expert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA strong foundation in digital literacy and responsible use of AI tools is essential to providing accurate, efficient, and evidence-based patient care.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile digital tools and AI can support communication, they must not compromise the quality of human connection in the doctor\u0026ndash;patient relationship.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollaborator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital platforms and AI-supported systems can enhance interdisciplinary teamwork and care coordination when used thoughtfully and transparently.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysicians should take an active role in guiding the safe and effective integration of digital technologies and AI into clinical environments.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Advocate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital health technologies and AI have the potential to improve access to care and reduce disparities, but only if physicians advocate for equitable and ethical implementation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScholar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLifelong learning in digital skills and the ability to critically evaluate AI applications are essential for maintaining medical competence.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthical, legal, and professional responsibilities include ensuring secure and just use of digital data and AI systems in patient care.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhile students and physicians generally acknowledge the importance of digital transformation in healthcare, prior research shows they may also feel uncertain, anxious, or unprepared to critically assess new tools [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. To capture this complexity, Q19 combines general statements (e.g., the societal importance of AI) with personal reflections (e.g., confidence in using or evaluating digital tools). This mixed design helps distinguish between broad acceptance of technology and individual readiness, supporting future subscale development on perceived impact, digital self-efficacy, and emotional responses [\u003cspan additionalcitationids=\"CR78 CR79 CR80 CR81 CR82 CR83 CR84 CR85 CR86 CR87 CR88\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. This mixed approach allows for a nuanced understanding of how medical students differentiate between perceived societal or professional trends and their own readiness, motivation, and self-efficacy. From a psychometric perspective, this structure also supports the later formation of subscales, such as the perceived impact of technology on medicine, the personal confidence and digital self-efficacy as well as emotional and motivational responses to AI.\u003c/p\u003e \u003cp\u003eSection 4: Expectations for Medical Education and Support\u003c/p\u003e \u003cp\u003eThe questions herein focus on medical students\u0026rsquo; expectations for digital health and AI education, their preferred learning formats and hands-on training opportunities, and the institutional support they consider most valuable for preparing to work in a technology-driven healthcare environment. Students increasingly value digital health education, seeking courses on data management, ethics, legal frameworks, research, and hands-on training [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous research shows medical students often lack confidence in eHealth but favor practical, supervised experiences[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Identifying preferred learning formats and support mechanisms helps shape effective, student-centered digital health education strategies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Evaluating whether education in digital health and AI influences students\u0026rsquo; choice of university is essential. Addressing these needs will not only help universities better prepare their students but also guide targeted policy and educational initiatives to remain competitive in the evolving healthcare education landscape.\u003c/p\u003e \u003cp\u003eLimitations and Future Directions\u003c/p\u003e \u003cp\u003eWhile the Delphi process enabled the structured development and expert validation of the questionnaire, certain limitations remain. Psychometric properties such as validity and reliability have not yet been systematically assessed. Future studies should therefore focus on the empirical evaluation of these quality criteria in diverse student populations. Moreover, while this expert consensus ensures fact and content validity, the dynamic nature of digital tools and AI applications in medicine necessitates regular updates and re-evaluations of questionnaire items to maintain relevance and accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis Delphi study successfully developed a consensus-based questionnaire to assess medical students\u0026rsquo; digital literacy, familiarity with AI tools, and expectations for digital health education. Across two rounds, a diverse panel of educators and students refined the instrument, resulting in 25 validated items across four thematic areas. The final questionnaire captures essential competencies for future physicians\u0026mdash;from tool usage to professional attitudes aligned with CanMEDS roles\u0026mdash;and provides a robust framework to inform curricular innovations in technology-driven medical education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003e\u0026middot; Human ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eo This study was conducted in accordance with the \u003cstrong\u003eDeclaration of Helsinki\u003c/strong\u003e. Ethical approval was granted by the \u003cstrong\u003eEthics Committee of the Medical School Hamburg\u003c/strong\u003e (Reference number: MSH-2025/33).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eo Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Availability of data and materials\u003c/p\u003e\n\u003cp\u003eo The final survey questions generated during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Competing interests\u003c/p\u003e\n\u003cp\u003eo Marciana Nona Duma: Received honoraria for lectures from AstraZeneca; unrelated to this study.\u003c/p\u003e\n\u003cp\u003eo Stefan Knippen: Received honoraria for lectures from AstraZeneca; unrelated to this study.\u003c/p\u003e\n\u003cp\u003eo Holger Joswig: Received research and travel grants from Miethke, Nevro, and Medtronic; unrelated to this study.\u003c/p\u003e\n\u003cp\u003eo All other authors report no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Funding\u003c/p\u003e\n\u003cp\u003eo No funding\u003c/p\u003e\n\u003cp\u003e\u0026middot; Authors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eo MND: Conceptualized the study and led the design of the questionnaire. Coordinated the Delphi rounds, managed participant communication, analyzed feedback, and aligned questionnaire items with competency frameworks.\u003c/p\u003e\n\u003cp\u003eo SK, FB: Provided key peer feedback during the Delphi process.\u003c/p\u003e\n\u003cp\u003eo ISE, BW, CGE, WG, CE, ALF, MK, HJ, LK, JP, RP, SRS, MIS, KKW: Contributed to item revision, review of clarity, scope, feasibility, and alignment with medical curricula and AI competencies.\u003c/p\u003e\n\u003cp\u003eo All authors contributed to the refinement of questionnaire items, critically reviewed the manuscript, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u0026middot; AI Prompts\u003c/p\u003e\n\u003cp\u003eo To improve clarity and structure, AI-based language models were used exclusively to optimize the phrasing of selected sentences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eo The following prompts guided this process:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026quot;Rephrase this.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026quot;Fix phrasing, don\u0026rsquo;t change content.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026quot;Polish the grammar, don\u0026rsquo;t change content.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026ldquo;Shorten this.\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEstrela M, et al. 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JMIR Med Educ. 2020;6(2):e17030.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital health education, Artificial intelligence in medicine, Digital literacy, Medical education curriculum, Clinical decision support systems, Technology acceptance, Digital skills assessment, Medical education research","lastPublishedDoi":"10.21203/rs.3.rs-9150280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9150280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith digital health and artificial intelligence (AI) increasingly shaping medical practice, assessing medical students\u0026rsquo; digital competencies, attitudes, and educational needs is essential for designing future-oriented medical curricula. This study aimed to develop and refine a comprehensive, evidence-based questionnaire to assess digital literacy and AI-related competencies among medical students, ensuring alignment with educational needs and healthcare trends.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multi-round Delphi process involving medical educators and senior medical students was conducted to evaluate and revise an initial 26-item questionnaire. Panelists assessed each item\u0026rsquo;s relevance, clarity, and completeness. Revisions were guided by panel feedback and supported by targeted literature searches in PubMed. Only evidence-based changes were accepted. The final instrument contains 25 questions organized into four thematic sections: (1) Demographics and Background, (2) Proficiency in Technical Tools and Software, (3) Attitudes Toward Technology in Medical Practice, and (4) Expectations for Medical Education and Support.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwelve experts participated in the Delphi panel, including ten medical educators and two medical students. After two consensus rounds, the final questionnaire retained 25 items with an average agreement rate of 96%. Items were refined between rounds to improve clarity and align with contemporary digital health practices. Notably, attitude statements were structured to reflect the CanMEDS physician roles. The questionnaire captures students\u0026rsquo; familiarity with digital and AI tools, usage frequency, device ownership, and educational expectations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe resulting questionnaire offers a literature based, structured instrument for assessing digital literacy and AI readiness among medical students. It responds to gaps in existing tools and provides a foundation for curricular development, institutional benchmarking, and further research.\u003c/p\u003e","manuscriptTitle":"Assessing Digital and AI-Readiness in Medical Education: A Delphi-Based Development of a Digital Health Competency Questionnaire","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 01:02:03","doi":"10.21203/rs.3.rs-9150280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T11:44:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24622285135175712289627832287072333283","date":"2026-05-09T11:11:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T11:55:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141107119521887586248049772451129612548","date":"2026-04-21T18:17:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T15:55:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T12:28:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T12:19:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T12:18:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-03-17T14:49:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9cbdcae8-3c0b-4aef-98c0-645717d2da23","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T11:44:14+00:00","index":48,"fulltext":""},{"type":"reviewerAgreed","content":"24622285135175712289627832287072333283","date":"2026-05-09T11:11:41+00:00","index":46,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T11:55:53+00:00","index":40,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T01:02:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 01:02:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9150280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9150280","identity":"rs-9150280","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}