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

Essential competencies of nurses working with AI-driven lifestyle monitoring in long-term care: A modified Delphi study

BACKGROUND

As more and more older adults prefer to stay in their homes as they age, there's a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in long-term care.

METHODS

A three round modified Delphi study was conducted, consisting of two online questionnaires and one focus group. A group of 48 experts participated in the study: nurses, innovators, developers, researchers, managers and educators. In the first two rounds experts assessed clarity and relevance on a proposed list of competencies, with the opportunity to provide suggestions for adjustments or inclusion of new competencies. In the third round the items without consensus were bespoken in a focus group.

FINDINGS

After the first round consensus was reached on relevance and clarity on n = 46 (72 %) of the competencies, after the second round on n = 54 (83 %) of the competencies. After the third round a final list of 10 competency domains and 61 sub-competencies was finalized. The 10 competency domains are: Fundamentals of AI, Participation in AI design, Patient-centered needs assessment, Personalisation of AI to patients' situation, Data reporting, Interpretation of AI output, Integration of AI output into clinical practice, Communication about AI use, Implementation of AI and Evaluation of AI use. These competencies span from basic understanding of AI-driven lifestyle monitoring, to being able to integrate it in daily work, being able to evaluate it and communicate its use to other stakeholders, including patients and informal caregivers.

CONCLUSION

Our study introduces a novel framework highlighting the (sub)competencies, required for nurses to work with AI-driven lifestyle monitoring in long-term care. These findings provide a foundation for developing initial educational programs and lifelong learning activities for nurses in this evolving field. Moreover, the importance that experts attach to AI competencies calls for a broader discussion about a potential shift in nursing responsibilities and tasks as healthcare becomes increasingly technologically advanced and data-driven, possibly leading to new roles within nursing.

Health Care Professionals' Concerns About Medical AI and Psychological Barriers and Strategies for Successful Implementation: Scoping Review

BACKGROUND

The rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction.

OBJECTIVE

This scoping review aims to offer a thorough analysis of the current status of integrating AI into medical practice as well as the apprehensions expressed by HC professionals (HCPs) over its application.

METHODS

This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to examine articles that investigated the apprehensions of HCPs about medical AI. Following the application of inclusion and exclusion criteria, 32 of an initial 217 studies (14.7%) were selected for the final analysis. We aimed to develop an attitude range that accurately captured the unfavorable emotions of HCPs toward medical AI. We achieved this by selecting attitudes and ranking them on a scale that represented the degree of aversion, ranging from mild skepticism to intense fear. The ultimate depiction of the scale was as follows: skepticism, reluctance, anxiety, resistance, and fear.

RESULTS

In total, 3 themes were identified through the process of thematic analysis. National surveys performed among HCPs aimed to comprehensively analyze their current emotions, worries, and attitudes regarding the integration of AI in the medical industry. Research on technostress primarily focused on the psychological dimensions of adopting AI, examining the emotional reactions, fears, and difficulties experienced by HCPs when they encountered AI-powered technology. The high-level perspective category included studies that took a broad and comprehensive approach to evaluating overarching themes, trends, and implications related to the integration of AI technology in HC. We discovered 15 sources of attitudes, which we classified into 2 distinct groups: intrinsic and extrinsic. The intrinsic group focused on HCPs' inherent professional identity, encompassing their tasks and capacities. Conversely, the extrinsic group pertained to their patients and the influence of AI on patient care. Next, we examined the shared themes and made suggestions to potentially tackle the problems discovered. Ultimately, we analyzed the results in relation to the attitude scale, assessing the degree to which each attitude was portrayed.

CONCLUSIONS

The solution to addressing resistance toward medical AI appears to be centered on comprehensive education, the implementation of suitable legislation, and the delineation of roles. Addressing these issues may foster acceptance and optimize AI integration, enhancing HC delivery while maintaining ethical standards. Due to the current prominence and extensive research on regulation, we suggest that further research could be dedicated to education.

Explanation and elaboration of MedinAI: guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services

The increasing adoption of artificial intelligence (AI) in medicines, pharmacotherapy, and pharmaceutical services necessitates clear guidance on reporting standards. While the MedinAI Statement (Bottacin in Int J Clin Pharm, https://doi.org/10.1007/s11096-025-01905-3, 2025) provides core guidelines for reporting AI studies in these fields, detailed explanations and practical examples are crucial for optimal implementation. This companion document was developed to offer comprehensive guidance and real-world examples for each guideline item. The document elaborates on all 14 items and 78 sub-items across four domains: core, ethical considerations in medication and pharmacotherapy, medicines as products, and services related to medicines and pharmacotherapy. Through clear, actionable guidance and diverse examples, this document enhances MedinAI's utility, enabling researchers and stakeholders to improve the quality and transparency of AI research reporting across various contexts, study designs, and development stages.

The content analysis used in nursing research and the possibility of including artificial intelligence support: A methodological review

BACKGROUND

This article explores how AI supports nurses by employing content analysis for scientific nursing research.

METHODS

A narrative literature review was conducted.

RESULTS

The article summarizes the knowledge known about content analysis and outlines qualitative and quantitative content analysis concepts and simplifies the issues related to the coding process. It explains how to identify and assess quality during content analysis and gives examples of topics that can be investigated using it, especially in the field of nursing.

CONCLUSIONS

Knowledge of AI capabilities is needed to make positive use of it. These capabilities change very quickly and require constant knowledge updates. Legal and ethical regulations concerning the use of technology are still lacking, so AI outputs still require human verification of them.

Bridging the Gap: From AI Success in Clinical Trials to Real-World Healthcare Implementation-A Narrative Review

BACKGROUND

Artificial intelligence (AI) has demonstrated remarkable diagnostic accuracy in controlled clinical trials, sometimes rivaling or even surpassing experienced clinicians. However, AI's real-world effectiveness is frequently diminished when applied to diverse clinical settings, owing to methodological shortcomings, limited multicenter studies, and insufficient real-world validations.

OBJECTIVE

This narrative review critically examines the discrepancy between AI's robust performance in clinical trials and its inconsistent real-world implementation. Our goal is to synthesize methodological, ethical, and operational challenges impeding AI integration and propose a comprehensive framework to bridge this gap.

METHODS

We conducted a thematic synthesis of peer-reviewed studies from the PubMed, IEEE Xplore, and Scopus databases, targeting studies from 2014 to 2024. Included studies addressed diagnostic, therapeutic, or operational AI applications and related implementation challenges in healthcare. Non-peer-reviewed articles and studies without rigorous analysis were excluded.

RESULTS

Our synthesis identified key barriers to AI's real-world deployment, including algorithmic bias from homogeneous datasets, workflow misalignment, increased clinician workload, and ethical concerns surrounding transparency, accountability, and data privacy. Additionally, scalability remains a challenge due to interoperability issues, insufficient methodological rigor, and inconsistent reporting standards. To address these challenges, we introduce the AI Healthcare Integration Framework (AI-HIF), a structured model incorporating theoretical and operational strategies for responsible AI implementation in healthcare.

CONCLUSIONS

Translating AI from controlled environments to real-world clinical practice necessitates a multifaceted, interdisciplinary approach. Future research should prioritize large-scale pragmatic trials and observational studies to empirically validate the proposed AI Healthcare Integration Framework (AI-HIF) in diverse, real-world healthcare contexts.

A Narrative Review of Key Studies in Medical Education in 2023: Applying the Current Literature to Educational Practice and Scholarship

Pediatric clinician educators face the challenge of juggling clinical practice with teaching responsibilities. The balancing act is even more challenging when one considers the need to stay current with evidence from clinical and medical education literature. In this narrative review of 2023 medical education literature, the Academic Pediatric Association Education Committee's Top Articles team summarizes high-yield articles that possess the potential to significantly influence pediatric clinician educator teaching and practice. A standardized blinded rubric was applied to identify the most impactful articles from 19 medical education and specialty journals. Final selections were categorized into six domains: artificial intelligence and technology, belonging in the learning environment, bias in the workplace, clinical learning, curriculum and assessment, and family and community partnerships. The reviewers summarize key findings from the top articles and describe implications for pediatric clinician educator practice.

Entrustment and EPAs for Artificial Intelligence (AI): A Framework to Safeguard the Use of AI in Health Professions Education

ABSTRACT

In this article, the authors propose a repurposing of the concept of entrustment to help guide the use of artificial intelligence (AI) in health professions education (HPE). Entrustment can help identify and mitigate the risks of incorporating generative AI tools with limited transparency about their accuracy, source material, and disclosure of bias into HPE practice. With AI's growing role in education-related activities, like automated medical school application screening and feedback quality and content appraisal, there is a critical need for a trust-based approach to ensure these technologies are beneficial and safe. Drawing parallels with HPE's entrustment concept, which assesses a trainee's readiness to perform clinical tasks-or entrustable professional activities-the authors propose assessing the trustworthiness of AI tools to perform an HPE-related task across 3 characteristics: ability (competence to perform tasks accurately), integrity (transparency and honesty), and benevolence (alignment with ethical principles). The authors draw on existing theories of entrustment decision-making to envision a structured way to decide on AI's role and level of engagement in HPE-related tasks, including proposing an AI-specific entrustment scale. Identifying tasks that AI could be entrusted with provides a focus around which considerations of trustworthiness and entrustment decision-making may be synthesized, making explicit the risks associated with AI use and identifying strategies to mitigate these risks. Responsible, trustworthy, and ethical use of AI requires health professions educators to develop safeguards for using it in teaching, learning, and practice-guardrails that can be operationalized via applying the entrustment concept to AI. Without such safeguards, HPE practice stands to be shaped by the oncoming wave of AI innovations tied to commercial motivations, rather than modeled after HPE principles-principles rooted in the trust and transparency that are built together with trainees and patients.

Generative Artificial Intelligence in Medical Education-Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey

Background

Interest has recently increased in generative artificial intelligence (GenAI), a subset of artificial intelligence that can create new content. Although the publicly available GenAI tools are not specifically trained in the medical domain, they have demonstrated proficiency in a wide range of medical assessments. The future integration of GenAI in medicine remains unknown. However, the rapid availability of GenAI with a chat interface and the potential risks and benefits are the focus of great interest. As with any significant medical advancement or change, medical schools must adapt their curricula to equip students with the skills necessary to become successful physicians. Furthermore, medical schools must ensure that faculty members have the skills to harness these new opportunities to increase their effectiveness as educators. How medical schools currently fulfill their responsibilities is unclear. Colleges of Osteopathic Medicine (COMs) in the United States currently train a significant proportion of the total number of medical students. These COMs are in academic settings ranging from large public research universities to small private institutions. Therefore, studying COMs will offer a representative sample of the current GenAI integration in medical education.

Objective

This study aims to describe the policies and training regarding the specific aspect of GenAI in US COMs, targeting students, faculty, and administrators.

Methods

Web-based surveys were sent to deans and Student Government Association (SGA) presidents of the main campuses of fully accredited US COMs. The dean survey included questions regarding current and planned policies and training related to GenAI for students, faculty, and administrators. The SGA president survey included only those questions related to current student policies and training.

Results

Responses were received from 81% (26/32) of COMs surveyed. This included 47% (15/32) of the deans and 50% (16/32) of the SGA presidents (with 5 COMs represented by both the deans and the SGA presidents). Most COMs did not have a policy on the student use of GenAI, as reported by the dean (14/15, 93%) and the SGA president (14/16, 88%). Of the COMs with no policy, 79% (11/14) had no formal plans for policy development. Only 1 COM had training for students, which focused entirely on the ethics of using GenAI. Most COMs had no formal plans to provide mandatory (11/14, 79%) or elective (11/15, 73%) training. No COM had GenAI policies for faculty or administrators. Eighty percent had no formal plans for policy development. Furthermore, 33.3% (5/15) of COMs had faculty or administrator GenAI training. Except for examination question development, there was no training to increase faculty or administrator capabilities and efficiency or to decrease their workload.

Conclusions

The survey revealed that most COMs lack GenAI policies and training for students, faculty, and administrators. The few institutions with policies or training were extremely limited in scope. Most institutions without current training or policies had no formal plans for development. The lack of current policies and training initiatives suggests inadequate preparedness for integrating GenAI into the medical school environment, therefore, relegating the responsibility for ethical guidance and training to the individual COM member.

The Hallmarks of Predictive Oncology

SIGNIFICANCE

As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks, we hope to establish standards and guidelines that enable the symbiotic development of artificial intelligence and precision oncology.

Artificial Intelligence In Health And Health Care: Priorities For Action

The field of artificial intelligence (AI) has entered a new cycle of intense opportunity, fueled by advances in deep learning, including generative AI. Applications of recent advances affect many aspects of everyday life, yet nowhere is it more important to use this technology safely, effectively, and equitably than in health and health care. Here, as part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2025 initiative, which is designed to provide guidance on pressing health care issues for the incoming presidential administration, we describe the steps needed to achieve these goals. We focus on four strategic areas: ensuring safe, effective, and trustworthy use of AI; promotion and development of an AI-competent health care workforce; investing in AI research to support the science, practice, and delivery of health and health care; and promotion of policies and procedures to clarify AI liability and responsibilities.

Artificial Intelligence-Generated Writing in the ERAS Personal Statement: An Emerging Quandary for Post-graduate Medical Education

OBJECTIVE

This study was designed to investigate if artificial intelligence (AI) detection software can determine the use of AI in personal statements for residency applications.

METHOD

Previously written personal statements were collected from physicians who had already matched to residency through the Electronic Residency Application System. Physicians were recruited for the study through collegial relationships and were given study information via email. The study team constructed five parallel statements from the shared personal statements to prompt AI to create a personal statement of similar content. An online AI detection tool, GPTZero, was used to assess all the personal statements. Statistical analyses were conducted using R. Descriptive statistics, t-tests, and Pearson correlations were used to assess the data.

RESULTS

Eight physicians' statements were compared to eight AI-generated statements. GPTZero was able to correctly identify AI-generated writing, assigning them significantly higher AI probability scores compared to human-authored essays. Human-generated statements were considered more readable, used shorter words with fewer syllables, and had more sentences compared to AI-generated essays. Longer average sentence length, low readability scores, and high SAT word percentages were strongly associated with AI-generated essays.

CONCLUSIONS

This study shows the capacity of GPTZero to distinguish human-created versus AI-generated writing. Use of AI can pose significant ethical challenges and carries a risk of inadvertent harm to certain applicants and erosion of trust in the application process. Authors suggest standardization of protocol regarding the use of AI prior to its integration in post-graduate medical education.

Assessing Physician Motivation to Engage in Continuing Professional Development on Artificial Intelligence

ABSTRACT

To realize the transformative potential of artificial intelligence (AI) in health care, physicians must learn how to use AI-based tools effectively, safely, and equitably. Continuing professional development (CPD) activities are one way to learn how to do this. The purpose of this article is to describe a theory-based approach for assessing health professionals' motivation to participate in CPD on AI-based tools. An online survey, based on an AI competency framework developed from existing literature and expert consultations, was administered to practicing physicians in Ontario, Canada. Across eight subcompetencies for using AI-based tools (eg, appraise AI-based tools for their regulatory and legal status), the survey measured physicians' perception they could successfully enact the competency, the importance of the competency in meeting their practice needs, and the desirability of participating in CPD activities on the competency. Motivation scores were calculated by multiplying the three scores together. Ninety-five physicians completed the survey. The highest motivation scores were for the subcompetency of identifying AI-based tools based on clinical needs, while the lowest motivation scores were for appraising tools' regulatory and legal status. All AI subcompetencies were generally rated as important, and CPD activities were generally perceived as desirable. This survey demonstrates the utility of a theory-based approach for assessing physicians' motivation to learn. Although the survey results are context specific, the approach may be useful for other CPD providers to support decision making about future AI-related CPD activities.

Physicians' required competencies in AI-assisted clinical settings: a systematic review

BACKGROUND

Utilizing Artificial Intelligence (AI) in clinical settings may offer significant benefits. A roadblock to the responsible implementation of medical AI is the remaining uncertainty regarding requirements for AI users at the bedside. An overview of the academic literature on human requirements for the adequate use of AI in clinical settings is therefore of significant value.

SOURCES OF DATA

A systematic review of the potential implications of medical AI for the required competencies of physicians as mentioned in the academic literature.

AREAS OF AGREEMENT

Our findings emphasize the importance of physicians' critical human skills, alongside the growing demand for technical and digital competencies.

AREAS OF CONTROVERSY

Concrete guidance on physicians' required competencies in AI-assisted clinical settings remains ambiguous and requires further clarification and specification. Dissensus remains over whether physicians are adequately equipped to use and monitor AI in clinical settings in terms of competencies, skills and expertise, issues of ownership regarding normative guidance, and training of physicians' skills.

GROWING POINTS

Our review offers a basis for subsequent further research and normative analysis on the responsible use of AI in clinical settings.

AREAS TIMELY FOR DEVELOPING RESEARCH

Future research should clearly outline (i) how physicians must be(come) competent in working with AI in clinical settings, (ii) who or what should take ownership of embedding these competencies in a normative and regulatory framework, (iii) investigate conditions for achieving a reasonable amount of trust in AI, and (iv) assess the connection between trust and efficiency in patient care.

Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?

Depression poses significant challenges to global healthcare systems and impacts the quality of life of individuals and their family members. Recent advancements in artificial intelligence (AI) have had a transformative impact on the diagnosis and treatment of depression. These innovations have the potential to significantly enhance clinical decision-making processes and improve patient outcomes in healthcare settings. AI-powered tools can analyze extensive patient data-including medical records, genetic information, and behavioral patterns-to identify early warning signs of depression, thereby enhancing diagnostic accuracy. By recognizing subtle indicators that traditional assessments may overlook, these tools enable healthcare providers to make timely and precise diagnostic decisions that are crucial in preventing the onset or escalation of depressive episodes. In terms of treatment, AI algorithms can assist in personalizing therapeutic interventions by predicting the effectiveness of various approaches for individual patients based on their unique characteristics and medical history. This includes recommending tailored treatment plans that consider the patient's specific symptoms. Such personalized strategies aim to optimize therapeutic outcomes and improve the overall efficiency of healthcare. This theoretical review uniquely synthesizes current evidence on AI applications in primary care depression management, offering a comprehensive analysis of both diagnostic and treatment personalization capabilities. Alongside these advancements, we also address the conflicting findings in the field and the presence of biases that necessitate important limitations.

Twelve tips on applying AI tools in HPE scholarship using Boyer's model

AI has changed the landscape of health professions education. With the hype now behind us, we find ourselves in the phase of reckoning, considering what's next; where do we start and how can educators use these powerful tools for daily teaching and learning. We recognize the great need for training to use AI meaningfully for education. Boyer's model of scholarship provides a pedagogical approach for teaching with AI and how to maximize these efforts towards scholarship. By offering practical solutions and demonstrating their usefulness, this Twelve tips article demonstrates how to apply AI towards scholarship by leveraging the capabilities of the tools. Despite their potential, our recommendation is to exercise caution against AI dependency and to role model responsible use of AI by evaluating AI outputs critically with a commitment to accuracy and scrutinize for hallucinations and false citations.

Generative artificial intelligence in graduate medical education

Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including healthcare and education. This paper explores the potential opportunities and risks of GenAI in graduate medical education (GME). We review the existing literature and provide commentary on how GenAI could impact GME, including five key areas of opportunity: electronic health record (EHR) workload reduction, clinical simulation, individualized education, research and analytics support, and clinical decision support. We then discuss significant risks, including inaccuracy and overreliance on AI-generated content, challenges to authenticity and academic integrity, potential biases in AI outputs, and privacy concerns. As GenAI technology matures, it will likely come to have an important role in the future of GME, but its integration should be guided by a thorough understanding of both its benefits and limitations.

Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI-Based Mixed Methods Study

Background

Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.

Objective

This study aims to enhance the teaching of clinical pharmacology in medical school by using a multimodal generative artificial intelligence (genAI) approach to create compelling, cinematic clinical narratives (CCNs).

Methods

We transformed a standard clinical case into an engaging, interactive multimedia experience called "Shattered Slippers." This CCN used various genAI tools for content creation: GPT-4 for developing the storyline, Leonardo.ai and Stable Diffusion for generating images, Eleven Labs for creating audio narrations, and Suno for composing a theme song. The CCN integrated narrative styles and pop culture references to enhance student engagement. It was applied in teaching first-year medical students about immune system pharmacology. Student responses were assessed through the Situational Interest Survey for Multimedia and examination performance. The target audience comprised first-year medical students (n=40), with 18 responding to the Situational Interest Survey for Multimedia survey (n=18).

Results

The study revealed a marked preference for the genAI-enhanced CCNs over traditional teaching methods. Key findings include the majority of surveyed students preferring the CCN over traditional clinical cases (14/18), as well as high average scores for triggered situational interest (mean 4.58, SD 0.53), maintained interest (mean 4.40, SD 0.53), maintained-feeling interest (mean 4.38, SD 0.51), and maintained-value interest (mean 4.42, SD 0.54). Students achieved an average score of 88% on examination questions related to the CCN material, indicating successful learning and retention. Qualitative feedback highlighted increased engagement, improved recall, and appreciation for the narrative style and pop culture references.

Conclusions

This study demonstrates the potential of using a multimodal genAI-driven approach to create CCNs in medical education. The "Shattered Slippers" case effectively enhanced student engagement and promoted knowledge retention in complex pharmacological topics. This innovative method suggests a novel direction for curriculum development that could improve learning outcomes and student satisfaction in medical education. Future research should explore the long-term retention of knowledge and the applicability of learned material in clinical settings, as well as the potential for broader implementation of this approach across various medical education contexts.

Demystifying Artificial Intelligence for Health Care Professionals: Continuing Professional Development as an Agent of Transformation Leading to Artificial Intelligence-Augmented Practice

ABSTRACT

The rapid rise of artificial intelligence (AI) is transforming society; yet, the education of health care providers in this field is lagging. In health care, where AI promises to facilitate diagnostic accuracy, and allow for personalized treatment, bridging the knowledge and skill gaps for providers becomes vital. This article explores the challenges of AI education, such as the emergence of self-proclaimed experts during the pandemic, and the need for comprehensive training in AI language, mechanics, and ethics. It advocates for a new breed of health care professionals who are both practitioners and informaticians, who are capable through initial training or through continuing professional development of harnessing AI's potential. Interdisciplinary collaboration, ongoing education, and incentives are proposed to ensure health care benefits from AI's trajectory. This perspective article explores the hurdles and the imperative of creating educational programming designed specifically to help health care professionals augment their practice with AI.

Artificial Intelligence Compared to Manual Selection of Prospective Surgical Residents

BACKGROUND

Artificial Intelligence (AI) in the selection of residency program applicants is a new tool that is gaining traction, with the aim of screening high numbers of applicants while introducing objectivity and mitigating bias in a traditionally subjective process. This study aims to compare applicants screened by an AI software to a single Program Director (PD) for interview selection.

METHODS

A single PD at an ACGME-accredited, academic general surgery program screened applicants. A parallel screen by AI software, programmed by the same PD, was conducted on the same pool of applicants. Weighted preferences were assigned in the following order: personal statement, research, medical school rankings, letters of recommendation, personal qualities, board scores, graduate degree, geographic preference, past experiences, program signal, honor society membership, and multilingualism. Statistical analyses were conducted by chi-square, ANOVA, and independent two-sided t-tests.

RESULTS

Out of 1235 applications, 144 applications were PD-selected and 150 AI-selected (294 top applications). Twenty applications (7.3%) were both PD and AI selected for a total analysis cohort of 274 prospective residents. We performed two analyses: 1) PD-selected vs. AI-selected vs. Both and 2) PD-selected vs. AI-selected with the overlapping applicants censored. For the first analysis, AI selected significantly: more White/Hispanic applicants (p < 0.001), less signals (p < 0.001), more AOA honors society (p = 0.016), and more publications (p < 0.001). When censoring overlapping PD and AI selection, AI selected significantly: more White/Hispanic applicants (p < 0.001), less signals (p < 0.001), more US medical graduates (p = 0.027), less applicants needing visa sponsorship (p = 0.01), younger applicants (p = 0.024), higher USMLE Step 2 CK scores (p < 0.001), and more publications (p < 0.001).

CONCLUSIONS

There was only a 7% overlap between PD-selected and AI-selected applicants for interview screening in the same applicant pool. Despite the same PD educating the AI software, the 2 application pools differed significantly. In its present state, AI may be utilized as a tool in resident application selection but should not completely replace human review. We recommend careful analysis of the performance of each AI model in the respective environment of each institution applying it, as it may alter the group of interviewees.

How do we teach generative artificial intelligence to medical educators? Pilot of a faculty development workshop using ChatGPT

PURPOSE

Artificial intelligence (AI) is already impacting the practice of medicine and it is therefore important for future healthcare professionals and medical educators to gain experience with the benefits, limitations, and applications of this technology. The purpose of this project was to develop, implement, and evaluate a faculty development workshop on generative AI using ChatGPT, to familiarise participants with AI.

MATERIALS AND METHODS

A brief workshop introducing faculty to generative AI and its applications in medical education was developed for preclinical clinical skills preceptors at our institution. During the workshop, faculty were given prompts to enter into ChatGPT that were relevant to their teaching activities, including generating differential diagnoses and providing feedback on student notes. Participant feedback was collected using an anonymous survey.

RESULTS

27/36 participants completed the survey. Prior to the workshop, 15% of participants indicated having used ChatGPT, and approximately half were familiar with AI applications in medical education. Interest in using the tool increased from 43% to 65% following the workshop, yet participants expressed concerns regarding accuracy and privacy with use of ChatGPT.

CONCLUSION

This brief workshop serves as a model for faculty development in AI applications in medical education. The workshop increased interest in using ChatGPT for educational purposes, and was well received.

Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education

Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.

The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy

BACKGROUND

The recent surge in the capabilities of artificial intelligence systems, particularly large language models, is also impacting the medical and pharmaceutical field in a major way. Beyond specialized uses in diagnostics and data discovery, these tools have now become accessible to the general public.

AIM

The study aimed to critically analyse the current performance of large language models in answering patient's self-care questions regarding medications and supplements.

METHOD

Answers from six major language models were analysed for correctness, language-independence, context-sensitivity, and reproducibility using a newly developed reference set of questions and a scoring matrix.

RESULTS

The investigated large language models are capable of answering a clear majority of self-care questions accurately, providing relevant health information. However, substantial variability in the responses, including potentially unsafe advice, was observed, influenced by language, question structure, user context and time. GPT 4.0 scored highest on average, while GPT 3.5, Gemini, and Gemini Advanced had varied scores. Responses were context and language sensitive. In terms of consistency over time, Perplexity had the worst performance.

CONCLUSION

Given the high-quality output of large language models, their potential in self-care applications is undeniable. The newly created benchmark can facilitate further validation and guide the establishment of strict safeguards to combat the sizable risk of misinformation in order to reach a more favourable risk/benefit ratio when this cutting-edge technology is used by patients.

Diagnostic scope: the AI can't see what the mind doesn't know

BACKGROUND

Diagnostic scope is the range of diagnoses found in a clinical setting. Although the diagnostic scope is an essential feature of training and evaluating artificial intelligence (AI) systems to promote diagnostic excellence, its impact on AI systems and the diagnostic process remains under-explored.

CONTENT

We define the concept of diagnostic scope, discuss its nuanced role in building safe and effective AI-based diagnostic decision support systems, review current challenges to measurement and use, and highlight knowledge gaps for future research.

SUMMARY

The diagnostic scope parallels the differential diagnosis although the latter is at the level of an encounter and the former is at the level of a clinical setting. Therefore, diagnostic scope will vary by local characteristics including geography, population, and resources. The true, observed, and considered scope in each setting may also diverge, both posing challenges for clinicians, patients, and AI developers, while also highlighting opportunities to improve safety. Further work is needed to systematically define and measure diagnostic scope in terms that are accurate, equitable, and meaningful at the bedside. AI tools tailored to a particular setting, such as a primary care clinic or intensive care unit, will each require specifying and measuring the appropriate diagnostic scope.

OUTLOOK

AI tools will promote diagnostic excellence if they are aligned with patient and clinician needs and trained on an accurately measured diagnostic scope. A careful understanding and rigorous evaluation of the diagnostic scope in each clinical setting will promote optimal care through human-AI collaborations in the diagnostic process.

Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process

Objectives

Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

Materials and Methods

We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle.

Results Discussion and Conclusion

Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.

Preparing Physicians of the Future: Incorporating Data Science into Medical Education

The recent excitement surrounding artificial intelligence (AI) in health care underscores the importance of physician engagement with new technologies. Future clinicians must develop a strong understanding of data science (DS) to further enhance patient care. However, DS remains largely absent from medical school curricula, even though it is recognized as vital by medical students and residents alike. Here, we evaluate the current DS landscape in medical education and illustrate its impact in medicine through examples in pathology classification and sepsis detection. We also explore reasons for the exclusion of DS and propose solutions to integrate it into existing medical education frameworks.

Differing perspectives on artificial intelligence in mental healthcare among patients: a cross-sectional survey study

Introduction

Artificial intelligence (AI) is being developed for mental healthcare, but patients' perspectives on its use are unknown. This study examined differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health.

Methods

We conducted a cross-sectional survey of an online sample of 500 adults asking about general perspectives, comfort with AI, specific concerns, explainability and transparency, responsibility and trust, and the importance of relevant bioethical constructs.

Results

Multiple vulnerable subgroups perceive potential harms related to AI being used in mental healthcare, place importance on upholding bioethical constructs, and would blame or reduce trust in multiple parties, including mental healthcare professionals, if harm or conflicting assessments resulted from AI.

Discussion

Future research examining strategies for ethical AI implementation and supporting clinician AI literacy is critical for optimal patient and clinician interactions with AI in mental healthcare.

Pharmacy student use of ChatGPT: A survey of students at a U.S. School of Pharmacy

OBJECTIVE

To learn how students in an accredited PharmD program in the United States are using ChatGPT for personal, academic, and clinical reasons, and whether students think ChatGPT training should be incorporated into their program's curriculum.

METHODS

In August 2023, an 18-item survey was developed, pilot tested, and sent to all students who were enrolled during the Spring 2023 semester in the entry-level PharmD program at the University of Colorado. E-mail addresses were separated from survey responses to maintain anonymity. Responses were described using descriptive statistics.

RESULTS

206 pharmacy students responded to the survey for a 49% response rate. Nearly one-half (48.5%) indicated they had used ChatGPT for personal reasons; 30.2% had used it for academic reasons; and 7.5% had used it for clinical reasons. The most common personal use for ChatGPT was answering questions and looking-up information (67.0%). The top academic reason for using ChatGPT was summarizing information or a body of text (42.6%), while the top clinical reason was simplifying a complex topic (53.3%). Most respondents (61.8%) indicated they would be interested in learning about how ChatGPT could help them in pharmacy school, and 28.1% thought ChatGPT training should be incorporated into their pharmacy curriculum.

CONCLUSION

At the time of the survey, ChatGPT was being used by approximately one-half of our pharmacy student respondents for personal, academic, or clinical reasons. Overall, many students indicated they want to learn how to use ChatGPT to help them with their education and think ChatGPT training should be integrated into their curriculum.

Toward a responsible future: recommendations for AI-enabled clinical decision support

BACKGROUND

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES

This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS

In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process.

RESULTS

Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided.

DISCUSSION

AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow.

CONCLUSIONS

Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

Use of AI in Mental Health Care: Community and Mental Health Professionals Survey

Background

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support.

Objective

This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs).

Methods

This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted.

Results

The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback.

Conclusions

Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review

Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems.

Increasing Generative Artificial Intelligence Competency among Students Enrolled in Doctoral Nursing Research Coursework

BACKGROUND

 Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.

OBJECTIVE

 This study aimed to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in health care.

METHODS

 We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat Generative Pretrained Transformer (ChatGPT) 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in health care, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in health care practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students (n = 10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis.

RESULTS

 Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. All of them reported increasing their self-rated competency in generative AI by one to two points on a five-point rating scale.

CONCLUSION

 This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.

From Theory to Practice: Artificial Intelligence (AI) Literacy Course for First-Year Medical Students

Artificial intelligence (AI) is rapidly transforming healthcare by enhancing diagnostics, personalized medicine, and clinical decision-making. In medical education, AI chatbots have the potential to be used as virtual tutors or learning assistants. Despite AI's growing impact, its integration into medical education remains limited. AI is not a standard component of medical curricula, which could leave many future healthcare professionals unprepared for an AI-driven workplace. To address this significant gap, this editorial describes the development of a mini-course to integrate AI training for first-year medical students. The course was focused on the fundamentals of AI, prompt engineering, practical applications of chatbots as learning assistants, and ethical use of generative AI.

Teaching Data Science through an Interactive, Hands-On Workshop with Clinically Relevant Case Studies

BACKGROUND

 In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

OBJECTIVES

 Addressing the limited exposure of health care providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.

METHODS

 The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts.

RESULTS

 Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians.

CONCLUSION

 Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven health care landscape, offering a template for integrating data science education into health care informatics programs or continuing professional development.

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study

BACKGROUND

The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored.

OBJECTIVE

This study aims to understand public perceptions regarding potential benefits of AI, concerns about AI, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health care.

METHODS

We conducted a 1-time cross-sectional survey with a nationally representative sample of 500 US-based adults. Participants provided structured responses on their perceived benefits, concerns, comfort, and values regarding AI for mental health care. They could also add free-text responses to elaborate on their concerns and values.

RESULTS

A plurality of participants (245/497, 49.3%) believed AI may be beneficial for mental health care, but this perspective differed based on sociodemographic variables (all P<.05). Specifically, Black participants (odds ratio [OR] 1.76, 95% CI 1.03-3.05) and those with lower health literacy (OR 2.16, 95% CI 1.29-3.78) perceived AI to be more beneficial, and women (OR 0.68, 95% CI 0.46-0.99) perceived AI to be less beneficial. Participants endorsed concerns about accuracy, possible unintended consequences such as misdiagnosis, the confidentiality of their information, and the loss of connection with their health professional when AI is used for mental health care. A majority of participants (80.4%, 402/500) valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked who was responsible for the misdiagnosis of mental health conditions using AI, 81.6% (408/500) of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of patients' information.

CONCLUSIONS

Future work involving the use of AI for mental health care should investigate strategies for conveying the level of AI's accuracy, factors that drive patients' mental health risks, and how data are used confidentially so that patients can determine with their health professionals when AI may be beneficial. It will also be important in a mental health care context to ensure the patient-health professional relationship is preserved when AI is used.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Background

With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives

This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Methods

We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail.

Results

Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare.

Conclusions

The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Sociotechnical imaginaries in academic medicine strategic planning: a document analysis

Purpose Along with other industries, healthcare is becoming increasingly digitized. Our study explores how the field of academic medicine is preparing for this digital future. Method Active strategic plans available in English were collected from faculties of medicine in Canada (n = 14), departments in medical schools (n = 17), academic health science centres (n = 23) and associated research institutes (n = 5). In total, 59 strategic plans were subjected to a practice-oriented form of document analysis, informed by the concept of sociotechnical imaginaries. Results On the one hand, digital health is discursively treated as a continuation of the academic medicine vision, with expansions of physician competencies and of research institutes contributions. These imaginaries do not necessarily disrupt the field of academic medicine as currently configured. On the other hand, there is a vision of digital health pursuing a robust sociotechnical future with transformative implications for how care is conducted, what forms of knowledge are prioritized, how patients and patienthood will be understood, and how data work will be distributed. This imaginary may destabilize existing distributions of knowledge and power. Conclusions Looking through the lens of sociotechnical imaginaries, this study illuminates strategic plans as framing desirable futures, directing attention towards specific ways of understanding problems of healthcare, and mobilizing the resources to knit together social and technical systems in ways that bring these visions to fruition. There are bound to be tensions as these sociotechnical imaginaries are translated into material realities. Many of those tensions and their attempted resolutions will have direct implications for the expectations of health professional graduates, the nature of clinical learning environments, and future relationships with patients. Sociology of digital health and science and technology studies can provide useful insights to guide leaders in academic medicine shaping these digital futures.

Large Language Models in Pediatric Education: Current Uses and Future Potential

Generative artificial intelligence, especially large language models (LLMs), has the potential to affect every level of pediatric education and training. Demonstrating speed and adaptability, LLMs can aid educators, trainees, and practicing pediatricians with tasks such as enhancing curriculum design through the creation of cases, videos, and assessments; creating individualized study plans and providing real-time feedback for trainees; and supporting pediatricians by enhancing information searches, clinic efficiency, and bedside teaching. LLMs can refine patient education materials to address patients' specific needs. The current versions of LLMs sometimes provide "hallucinations" or incorrect information but are likely to improve. There are ethical concerns related to bias in the output of LLMs, the potential for plagiarism, and the possibility of the overuse of an online tool at the expense of in-person learning. The potential benefits of LLMs in pediatric education can outweigh the potential risks if employed judiciously by content experts who conscientiously review the output. All stakeholders must firmly establish rules and policies to provide rigorous guidance and assure the safe and proper use of this transformative tool in the care of the child. In this article, we outline the history, current uses, and challenges with generative artificial intelligence in pediatrics education. We provide examples of LLM output, including performance on a pediatrics examination guide and the creation of patient care instructions. Future directions to establish a safe and appropriate path for the use of LLMs will be discussed.

Evaluation of artificial intelligence-generated drug therapy communication skill competencies in medical education

AIMS

This study compared three artificial intelligence (AI) platforms' potential to identify drug therapy communication competencies expected of a graduating medical doctor.

METHODS

We presented three AI platforms, namely, Poe Assistant©, ChatGPT© and Google Bard©, with structured queries to generate communication skill competencies and case scenarios appropriate for graduating medical doctors. These case scenarios comprised 15 prototypical medical conditions that required drug prescriptions. Two authors independently evaluated the AI-enhanced clinical encounters, which integrated a diverse range of information to create patient-centred care plans. Through a consensus-based approach using a checklist, the communication components generated for each scenario were assessed. The instructions and warnings provided for each case scenario were evaluated by referencing the British National Formulary.

RESULTS

AI platforms demonstrated overlap in competency domains generated, albeit with variations in wording. The domains of knowledge (basic and clinical pharmacology, prescribing, communication and drug safety) were unanimously recognized by all platforms. A broad consensus among Poe Assistant© and ChatGPT© on drug therapy-related communication issues specific to each case scenario was evident. The consensus primarily encompassed salutation, generic drug prescribed, treatment goals and follow-up schedules. Differences were observed in patient instruction clarity, listed side effects, warnings and patient empowerment. Google Bard did not provide guidance on patient communication issues.

CONCLUSIONS

AI platforms recognized competencies with variations in how these were stated. Poe Assistant© and ChatGPT© exhibited alignment of communication issues. However, significant discrepancies were observed in specific skill components, indicating the necessity of human intervention to critically evaluate AI-generated outputs.

Perspectives on Artificial Intelligence in Nursing in Asia

Artificial intelligence (AI) is reshaping health care, including nursing, across Asia, presenting opportunities to improve patient care and outcomes. This viewpoint presents our perspective and interpretation of the current AI landscape, acknowledging its evolution driven by enhanced processing capabilities, extensive data sets, and refined algorithms. Notable applications in countries such as Singapore, South Korea, Japan, and China showcase the integration of AI-powered technologies such as chatbots, virtual assistants, data mining, and automated risk assessment systems. This paper further explores the transformative impact of AI on nursing education, emphasizing personalized learning, adaptive approaches, and AI-enriched simulation tools, and discusses the opportunities and challenges of these developments. We argue for the harmonious coexistence of traditional nursing values with AI innovations, marking a significant stride toward a promising health care future in Asia.

Residency Application Selection Committee Discriminatory Ability in Identifying Artificial Intelligence-Generated Personal Statements

OBJECTIVE

Advances in artificial intelligence (AI) have given rise to sophisticated algorithms capable of generating human-like text. The goal of this study was to evaluate the ability of human reviewers to reliably differentiate personal statements (PS) written by human authors from those generated by AI software.

SETTING

Four personal statements from the archives of two surgical program directors were de-identified and used as the human samples. Two AI platforms were used to generate nine additional PS.

PARTICIPANTS

Four surgeons from the residency selection committees of two surgical residency programs of a large multihospital system served as blinded reviewers. AI was also asked to evaluate each PS sample for authorship.

DESIGN

Sensitivity, specificity and accuracy of the reviewers in identifying the PS author were calculated. Kappa statistic for correlation between the hypothesized author and the true author were calculated. Inter-rater reliability was calculated using the kappa statistic with Light's modification given more than two reviewers in a fully-crossed design. Logistic regression was performed with to model the impact of perceived creativity, writing quality, and authorship or the likelihood of offering an interview.

RESULTS

Human reviewer sensitivity for identifying an AI-generated PS was 0.87 with specificity of 0.37 and overall accuracy of 0.55. The level of agreement by kappa statistic of the reviewer estimate of authorship and the true authorship was 0.19 (slight agreement). The reviewers themselves had an inter-rater reliability of 0.067 (poor), with only complete agreement (four out of four reviewers) on two PS, both authored by humans. The odds ratio of offering an interview (compared to a composite of "backup" status or no interview) to a perceived human author was 7 times that of a perceived AI author (95% confidence interval 1.5276 to 32.0758, p=0.0144). AI hypothesized human authorship for twelve of the PS, with the last one "unsure."

CONCLUSIONS

The increasing pervasiveness of AI will have far-reaching effects including on the resident application and recruitment process. Identifying AI-generated personal statements is exceedingly difficult. With the decreasing availability of objective data to assess applicants, a review and potential restructuring of the approach to resident recruitment may be warranted.

Achieving large-scale clinician adoption of AI-enabled decision support

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research

The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivotal role in utilizing AI to foster a more efficient and equitable health-care system. However, to fulfil this role, nurses will require education that prepares them with the necessary skills and knowledge for the effective and ethical application of AI. This article proposes a framework for nurses which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity. By adopting this educational framework, nurses will be prepared to make substantial contributions to reducing health disparities and fostering a health-care system that is more efficient and equitable.

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

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

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.

Perspectives of Pharmacy Students on Ethical Issues Related to Artificial Intelligence: A Comprehensive Survey Study

Background

The integration of artificial intelligence (AI) into pharmacy education and practice holds the potential to advance learning experiences and prepare future pharmacists for evolving healthcare practice. However, it also raises ethical considerations that need to be addressed carefully. This study aimed to explore pharmacy students' attitudes regarding AI integration into pharmacy education and practice.

Methods

A cross-sectional design was employed, utilizing a validated online questionnaire administered to 702 pharmacy students from diverse demographic backgrounds. The questionnaire gathered data on participants' attitudes and concerns regarding AI integration, as well as demographic information and factors influencing their attitudes.

Results

Most participants were female students (72.8%), from public universities (55.6%) and not working (64.2%). Participants expressed a generally negative attitude toward AI integration, citing concerns and barriers such as patient data privacy (62.0%), susceptibility to hacking (56.2%), potential job displacement (69.3%), cost limitations (66.8%), access (69.1%) and the absence of regulations (48.1% agree), training (70.4%), physicians' reluctance (65.1%) and patient apprehension (70.8%). Factors including country of residence, academic year, cumulative GPA, work status, technology literacy, and AI understanding significantly influenced participants' attitudes (p < 0.05).

Conclusion

The study highlights the need for comprehensive AI education in pharmacy curricula including related ethical concerns. Addressing students' concerns is crucial to ensuring ethical, equitable, and beneficial AI integration in pharmacy education and practice.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.