BMJ Health & Care Informatics

ObjectiveEducating clinicians about Artificial Intelligence (AI) is an urgent need(1) as the UK General Medical Council (GMC) places liability with practitioners(2) and the EU AI Act with employers to provide appropriate training(3), but also because AI, like any tool, requires training to use safely. NHSE Capability Framework provides guidance(4), but frontline clinicians perspectives are unknown so we sought to identify their priorities. Methods and AnalysisUsing iterative interviews with residents, educators and experts we synthesised 10 contextualised AI-related problem statements. We surveyed residents and consultant-educators in the East of England, who rated their confidence and importance. Participants also ranked their preferred learning modality. ResultsWe received 299 responses. Clinicians priorities, defined by high importance (I) and low confidence (C), were: understanding liability implications (I: 40%; C: 1.82/5), determining appropriate levels of confidence in AI algorithms (I: 36.5%; C: 1.98/5), and mitigating security and privacy risks (I: 34%; C: 1.68). Confidence was low (mean 20, range 10-50), with no significant difference between educators and residents. Residents preferred integration of training into regional teaching, while consultant-educators favoured webinars. ConclusionOur findings show that clinicians prioritise practical concerns, such as liability and determining confidence in algorithmic outputs. In contrast, critical appraisal and explaining AI to patients were deprioritised, despite their relevance to clinical safety. This study enhances the NHSE Capability Framework by contextualizing AI-related capabilities for clinicians as users and identifying priorities with which to develop scalable training. Key MessagesO_ST_ABSWhat is already know on this topicC_ST_ABSWhile clinicians face legal accountability for their use of AI in healthcare(2,3,5), there remains no standardised educational pathway to support them in acquiring the necessary skills. Although expert-informed capability frameworks exist(6), they are necessarily broad and lack operational clarity for day-to-day clinical roles. What this study addsThis study translates 31 AI-related capabilities from the NHSE DART-Ed Capability Framework(6) into 10 concise AI learning needs for clinicians of the user archetype through iterative interviews with residents, educators and AI experts. A regional survey with 299 responses from residents and educators highlights practical concerns such as liability and determining appropriate confidence in AI algorithms as learners priorities, whilst critical appraisal and explaining AI to patients were deprioritised despite their relevance to clinical safety. How this study might affect research, practice or policyThe educational priorities of clinicians as users of AI identified in this study provides engaging, curriculum-ready content mapped to the user archetype of the DART-Ed framework, which can be adapted to role and task-specific educational activities.

ObjectivesGiven widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research. DesignA qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI scenarios. Data were analysed using qualitative description thematic analysis. SettingsTwo cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga, (part of the Greater Toronto Area). ParticipantsForty-one purposively sampled members of the public (21M:20F, 25-65 years, median age 40). ResultsParticipants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed AI as a tool for the analysis of health data when there is strong potential for public benefit, providing that concerns about privacy, consent, and commercial motives were addressed. Inductive thematic analysis identified AI-specific hopes (e.g., potential for faster and more accurate analyses, ability to use more data), fears (e.g., loss of human touch, skill depreciation from over-reliance on machines) and conditions (e.g., human verification of computer-aided decisions, transparency). There were mixed views about whether consent is required for health data research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect. ConclusionsNotwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research. STRENGTHS AND LIMITATIONS OF THIS STUDYA strength of this study is the analysis of how diverse members of the general public perceive three realistic scenarios in which health data are used for AI research. The detailed health AI scenarios incorporate points that previous qualitative research has indicated are likely to elicit discussion (e.g., use of health data without express consent, involvement of commercial organisations in health research, inability to guarantee anonymity of genetic data) and may also be useful in future qualitative research studies and for educational purposes. The findings are likely to be relevant to organisations that are considering making health data available for AI research and development. Notwithstanding the diverse ethnic and educational backgrounds of participants, overall the sample represents the general (mainstream) population of Ontario and results cannot be interpreted as presenting the views of specific subpopulations and may not be generalisable across Ontario or to other settings. Given the low level of knowledge about AI in general it is possible that the views of participants would change substantially if they learned and understood more about AI. TRANSPARENCY STATEMENTP. Alison Paprica affirms that the manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that there were no discrepancies from the study as originally approved by the University of Toronto Research Ethics Board.

Primary care artificial intelligence adoption among United States (US) physicians accelerated from 38% to 66% within one year. Implementation strategies typically assume physician resistance as the primary barrier; however, emerging evidence suggests a different challenge where enthusiastic adoption precedes adequate knowledge development. Aims: To assess physician readiness for AI implementation in organized primary care, including knowledge levels, attitudes, implementation priorities, and actual usage patterns among Swiss primary care physicians. Methods: Multicentric cross-sectional survey involving four regional subnetworks as study centers (Zurich, Bern, Ticino, Romandie) within mediX Switzerland, conducted August-September 2024. The mediX network comprises 900+ primary care physicians across three Swiss linguistic regions operating within a hybrid managed care model. Online survey of 620 primary care physicians yielding 155 analyzable responses (25.8% response rate). Analysis employed Wilson Score confidence intervals for proportions, Cohens h effect sizes, and sensitivity analyses addressing both non-response bias and knowledge threshold definitions. Results: A pronounced knowledge-attitude gap emerged among respondents. While 69.0% (95% CI: 61.4%-75.8%) expressed positive attitudes toward AI and 81.9% (95% CI: 75.1%-87.2%) sought training opportunities, only 14.8% (95% CI: 10.1%- 21.3%) self-assessed their knowledge as high or excellent (levels 4-5), our primary threshold for adequate knowledge. Even when including moderate self-assessed knowledge (level 3+), only 47.1% met this threshold, indicating a persistent 21.9 percentage point knowledge-attitude gap. Critically, 27.7% (95% CI: 21.3%-35.3%) already use AI tools for clinical purposes notwithstanding acknowledged competency gaps. Non-response sensitivity analyses suggest population-level training interest ranges from 20.5% to 81.3% depending on assumptions about non-responders. Physicians demonstrated clear implementation preferences: immediate priority for administrative support (80.0%) and image analysis (73.5%), medium-term priority for medication management (64.5%) and diagnostic support (61.9%), and long-term perspective for complex applications. Conclusions: Among AI-engaged physicians, this exploratory study reveals a substantial knowledge-attitude gap and documents current AI usage patterns that may precede formal knowledge acquisition. While selection bias limits generalizability, these findings suggest that educational interventions and governance frameworks merit urgent consideration in coordinated care settings where AI adoption is accelerating. What is already known on this topicAI adoption in primary care accelerated from 38% to 66% within one year, creating urgent need for readiness assessment Current implementation strategies assume physician resistance, though evidence suggests knowledge deficits may be a greater barrier Knowledge-attitude gaps have been reported across healthcare systems, but their magnitude and implications for patient safety remain poorly understood What this study addsReveals a 54.2 percentage point knowledge-attitude gap persistent across sensitivity analyses, indicating barriers stem from education infrastructure deficits rather than fundamental resistance Identifies unsupervised AI usage by 27.7% of physicians despite acknowledged knowledge limitations--a patient safety concern absent from previous implementation literature Establishes physician-consensus implementation hierarchy enabling systematic, evidence-based AI deployment: begin with administrative applications ([&ge;]70% support), progress to clinical support (50-69%), reserve complex applications (<50%) for mature phases

Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary co-creation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive medical AI. We identified three key challenges for realising inclusive medical AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and co-design approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, system, process, and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal and societal aspects when aiming to foster responsible and inclusive AI in healthcare. Author SummaryArtificial intelligence (AI) is being used more and more in healthcare to improve diagnosis, treatment, and personalised care. However, if not designed carefully, these technologies can unintentionally increase existing inequalities and exclude certain groups from their benefits. In our study, we brought together experts from healthcare, ethics, law, social sciences, and patient advocacy to explore how AI in medicine can be made more inclusive. Over five days, we worked together to identify key issues and come up with practical solutions. We focused on three main areas: 1) Ensuring diverse voices are heard during the development of AI tools; 2) Making data collection fair and representative; and 3) Creating regulations that protect human rights. From the discussions of the workshop, we created the PREFER-IT framework, which outlines eight key principles for inclusive AI: O_LIParticipatory and co-design approaches C_LIO_LIRepresentative and diverse data C_LIO_LIEducation and digital literacy C_LIO_LIFairness C_LIO_LIEthical and legal accountability C_LIO_LIReal-world validation and feedback C_LIO_LIInclusive communication C_LIO_LITechnical interoperability C_LI This framework helps guide developers, policymakers, and healthcare professionals in creating AI systems that are not only effective but also fair and respectful of all users. Our work emphasises the importance of involving patients and communities in shaping the future of AI.

BackgroundArtificial intelligence is increasingly embedded in healthcare delivery. Its legitimacy depends on institutional governance, not technical performance alone. Prior research has centered on clinicians and patients. Less attention has been given to cybersecurity professionals who sustain the digital infrastructures that support health AI. This study examines how cybersecurity professionals conceptualize AI as clinical infrastructure and how these interpretations shape understandings of trust, risk, and oversight. MethodsGuided by sociotechnical systems theory and institutional trust scholarship, we conducted semi-structured in-depth interviews with twenty cybersecurity professionals working in healthcare-relevant domains. Participants were recruited through professional networks and LinkedIn outreach. Interviews were conducted between May and August 2025. They were audio-recorded and transcribed verbatim. Data were analyzed using qualitative content analysis with constant comparison. Two researchers independently coded transcripts and refined themes through iterative discussion. The study received Institutional Review Board approval. ResultsParticipants described health AI as an augmented clinical infrastructure. They emphasized that AI extends workflow capacity but requires sustained human oversight. Healthcare data systems were characterized as fragmented and vulnerable. Breaches were treated as anticipated events. Trust in AI was described as contingent and built over time through visible accountability. Cybersecurity stewardship was framed as foundational to institutional trustworthiness. ConclusionsHealth AI credibility emerges through governance practices that demonstrate accountability. Cybersecurity professionals and institutional stakeholders jointly shape trust in digitally mediated healthcare systems through governance decisions that signal accountability.

BackgroundCanada has invested significantly in artificial intelligence (AI) research and development over the last several years. Canadians knowledge of and attitudes towards AI in healthcare are understudied. ObjectivesTo explore the relationships between age, gender, education level, and income on Canadians knowledge of AI, their comfort with its use in healthcare, and their comfort with using personal health data in AI research. MethodsOrdinal logistics regression and multivariate polynomial regression were applied to data from the 2021 Canadian Digital Health Survey using RStudio and SigmaZones Design of Experiments Pro. ResultsFemale and older Canadians self-report less knowledge about AI than males and other genders and younger Canadians. Female Canadians and healthcare professionals are less comfortable with use of AI in healthcare compared to males and people with other levels of education. Discomfort appears to stem from concerns about data security and the current maturity level of the technology. ConclusionKnowledge of AI and the use of AI in healthcare are inversely correlated with age and directly correlated with education and income levels. Overall, female respondents self-reported less knowledge and comfort with AI in healthcare and research than other genders. Privacy concerns should continue to be addressed as a major consideration when implementing AI tools. Canadians, especially older females, not only need more education about AI in healthcare, but also need more reassurance about the safe and responsible use of their data and how bias and other issues with AI are being addressed. Author SummaryArtificial intelligence (AI) and its application has garnered significant public interest and excitement within healthcare in recent years. However, its successful integration and use in healthcare will depend on patient and user adoption. As a result, AI tools may be limited in healthcare when user concerns are not carefully addressed and if patients are not educated about how these technologies work. While there have been studies on the attitudes of clinicians and healthcare professionals toward AI, little is known about the general publics perception of AI within the healthcare setting. Our study addresses this gap in the literature by analyzing data from the 2021 Canadian Digital Health Survey to understand the relationships between Canadians attitudes towards AI and various socioeconomic and demographic factors. Our results found that older Canadians, Canadians with less formal education and women need to be better informed about the safe and responsible use of AI and be reassured about good data security practices before it can be broadly accepted by them. In addition, the element of trust may be a factor that is contributing to the higher levels of discomfort with AI observed in middle-aged Canadians. The findings from this study will help stakeholders better implement and broaden the accessibility of AI technologies.

IntroductionRegulation is important for medical software, but advances in software, notably developments in artificial intelligence (AI), are developing quickly. There are concerns that regulatory processes are not keeping up and that there is a need for more pro-innovation approaches. MethodsWe conducted a survey (n = 34) and four focus groups to discuss experiences of regulation among UK-based developers. ResultsIn the survey, 35% agreed/strongly agreed that they were confident in their knowledge of relevant regulation, while 50% agreed/strongly agreed that poor regulation was allowing bad products to come to market. The focus groups identified 10 themes around challenges with current processes: the process of obtaining regulatory approval is uncertain; lack of knowledge about regulatory approval; difficulties in obtaining reliable advice; complexity and slow pace of approvals; difficult to get NHS clinician involvement; process is costly and difficult to fund; implications for competition; international differences; incentives to develop lower classification products; and lack of harmonisation between NHS and MHRA. Respondents suggestions for solutions to improve processes fell under four themes: financial and structural support; regulatory collaboration and commissioner involvement; process efficiency and adaptability; and education and guidance. DiscussionDevelopers are unhappy with the process of regulation for medical software in the UK, finding it confusing and expensive. They feel systems compare poorly to international comparators. Integration between the MHRA system and NHS commissioning is considered poor.

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AIs effectiveness depends on the data its fed. In healthcare, where accuracy is critical, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Data was collected from February to March 2023. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. The coding process was validated through inter-coder reliability assessments, achieving a Cohens Kappa coefficient of 0.75. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. While all users valued the content quality, privacy, and trustworthiness of ChatGPT across different contexts, those using it for healthcare information place a greater emphasis on safety, trust, and the depth of information. Conversely, users engaging with ChatGPT for non-healthcare purposes prioritize usability, human-like interaction, and unbiased content. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use.

ImportanceClinician adoption and adaptation of new tools evolve over time. Prior studies of ambient Artificial intelligence (AI) scribes have primarily relied on single time-point measurements (e.g., pre-post), potentially obfuscating their true impact on outcomes. ObjectiveTo investigate longitudinal effects of an AI scribe tool on patient encounter-level outcomes. DesignCase series across 48 weeks (24 pre, 24 post) per clinician. SettingPrimary care clinical encounters occurring between 01/05/24 and 10/31/25. ParticipantsPrimary care clinicians (attending physicians and advanced practice providers). ExposureAmbient AI scribe introduction to clinical workflow, indexed to study day zero, per clinician. Main outcomes and measuresEncounter-level measurements of documentation time (note writing time, time outside of scheduled hours (TOSH), pajama time), note writing patterns (note length, note closure <24h) and clinicians billed work Relative Value Units (wRVU) with a focus on changes from pre-period outcomes at Day 0 and 150. Results220 primary care clinicians (Mean age=43.7, 70.9% females; 56.4% physicians) from 36 clinics, conducting 314,845 patient encounters were included. All outcomes evolved from day zero to day 150 and are compared back to pre-period levels. There was evidence of an immediate 7% decrease on average in note writing time at day zero (Incidence Rate Ratio, IRR 0.93, 95%CI [0.89, 0.96]), intensifying to a 15% decrease by day 150 (IRR 0.85, 95%CI [0.83, 0.87]). There was no evidence of a change in pajama time or TOSH at day zero; however, at day 150, there was evidence of a 18% decrease in pajama time (0.82, 95%CI [0.73, 0.91]) and a 13% decrease in TOSH (0.87, 95%CI [0.77, 0.99]). At day zero, there was evidence of a 5% increase (1.05, 95%CI [1.00, 1.10]) in note length and 31% increase in note closures (1.31, 95%CI [1.13, 1.53]), with both slowly attenuating to pre-period levels by day 150. Although there was no evidence of changes in wRVU at day zero, there was a 2% increase total wRVU at day 150 (1.02, 95%CI [1.01, 1.03]). Conclusions and relevanceLongitudinal changes were gradual, but persistent, underscoring the gradual adaptation of AI scribes, as clinicians situated these tools within their workflows. Key PointsO_ST_ABSQuestionC_ST_ABSHow do the patterns of use of an ambient Artificial Intelligence (AI) scribe evolve over time? FindingsIn this longitudinal, quasi-experimental study on clinician use of an ambient AI scribe, documentation time, note writing patterns and financial productivity evolved over a 150-day period. Compared to the pre-period, note writing time savings increased from 7% (day zero) to 15% (day 150); changes in all other considered outcomes including time outside of scheduled hours, pajama time, note length, note closure <24h, billed work Relative Value Units evolved over the 150-day period. MeaningClinician use of ambient AI scribes showed persistent changes in patterns of use over a 150-day period, highlighting a gradual adaptation process and the need for longitudinal assessment.

ObjectiveIn Sri Lanka, resource limitations have led to the continued use of paper-based records for patient management. We implemented a cloud-based Electronic Health Record (EHR) system in a tertiary surgical oncology unit, running it alongside the existing paper system. The EHR provided authorised, real-time remote access to patient data, digital theatre scheduling, and facilitated multidisciplinary team collaboration. MethodsTwenty-six healthcare workers (consultants, medical officers, nursing officers, trainees, and clerical staff) completed an online questionnaire assessing the EHRs usability, user satisfaction, and impact on workflow. We prospectively tracked and compared key time metrics between the paper and EHR systems, including theatre list preparation times and cancer biopsy turnaround (biopsy-to-diagnosis interval), to evaluate efficiency gains. ResultsMost participants (84.6%) used the EHR routinely. Users rated the system as highly intuitive, user-friendly, easily accessible, and simple for data entry (mean ratings [~] 4.0 out of 5). Overall satisfaction was high (mean 4.31/5), though system speed was rated slightly lower (mean 3.92), and technical glitches were noted (mean 3.65). Adequate training was associated with significantly higher satisfaction (p<0.05), and satisfaction correlated with perceived intuitiveness (r=0.43) and ease of use (r=0.60). The EHR reduced average theatre list preparation time from 4 minutes 6 seconds (paper) to 2 minutes 24 seconds, saving approximately 1 minute 42 seconds per list(p<0.001). Similarly, the median biopsy-to-diagnosis interval decreased from 14.95 days with the paper process to 8.40 days with the EHRs notification system- an average reduction of 6.55 days(p<0.001). ConclusionImplementing a customised EHR system in a resource-limited surgical oncology setting significantly improved workflow efficiency, reduced diagnostic delays, and enhanced data accessibility and team coordination. Users reported high satisfaction, but challenges such as technical limitations, infrastructure issues, and resistance to change persist. Targeted training, supportive infrastructure, and stakeholder engagement are recommended to sustain the EHR integration and promote greater adoption. HighlightsO_LIElectronic health records enhance workflow, data access, and team collaboration. C_LIO_LIElectronic health records significantly reduced biopsy-to-diagnosis delays. C_LIO_LIHigh user satisfaction is associated with intuitive design and adequate training. C_LIO_LITechnical issues and system speed were primary challenges for users. C_LIO_LITargeted training and robust infrastructure are vital for successful implementation. C_LI

ObjectiveA paper from Goh et al found that a large language model (LLM) working alone outperformed American clinicians assisted by the same LLM in diagnostic reasoning tests [1]. We aimed to replicate this result in a UK setting and explore how interactions with the LLM might explain the observed gaps in performance. Methods and AnalysisThis was a within-subjects study of UK physicians. 22 participants answered structured questions on 4 clinical vignettes. For 2 cases physicians had access to an LLM via a custom-built web-application. Results were analysed using a mixed-effects model accounting for case difficulty and the variability of clinicians at baseline. Qualitative analysis involved coding of participant-LLM interaction logs and evaluating the rates of LLM use per question. ResultsPhysicians with LLM assistance scored significantly lower than the LLM alone (mean difference 21.3 percentage points, p < 0.001). Access to the LLM was associated with improved physician performance compared to using conventional resources (73.7% vs 66.3%, p = 0.001). There was significant heterogeneity in the degree of LLM-assisted improvement (SD 10.4%). Qualitative analysis revealed that only 30% of case questions were directly posed to the LLM, which suggests that under-utilisation of the LLM contributed to the observed performance gap. ConclusionWhile access to an LLM can improve diagnostic accuracy, realising the full potential of human-AI collaboration may require a focus on training clinicians to integrate these tools into their cognitive workflows and on designing systems that make these integrations the default rather than an optional extra.

Artificial Intelligence (AI)-augmented interventions are currently being rolled out across primary care, but how it affects health equity remains insufficiently understood. This qualitative study addresses this gap through an ethnographical inquiry based on 32 interviews and focus groups with stakeholders including commissioners, decision makers, AI developers, researchers, GPs and patient groups involved in the implementation of AI in English primary care. We took a sociotechnical perspective in order to assess how the stakeholders can improve health equity through the implementation process of AI within the wider system. We found that regulation and policy alone cannot guarantee equitable implementation of AI but can provide a framework to enable other stakeholders to take measures to promote equity: fostering a shared understanding of the causal mechanisms of AI and health equity, how to measure health equity, and how to share data necessary for equity promotion. Further, all stakeholders need to be on board for equitable implementation, and currently innovation leaves clinicians and patients behind. Capacity building is needed to achieve this, in particular at local commissioning and clinician level. Careful implementation and pragmatically focused research are needed to make AI in primary care capable of advancing health equity.

BackgroundArtificial intelligence (AI) has impacted healthcare at urban and academic medical centers globally. The current focus on AI deployments in urban areas and the history of US urban-rural digital divides raises concerns that the promise of AI may not be realized in rural communities. This may exacerbate well-documented health disparities. Without the benefits of AI-driven improvements in patient outcomes and increased efficiency, rural healthcare facilities may fall farther behind their urban counterparts and rural hospital closure rates may continue to rise. MethodsWe conducted a scoping review following the PRISMA guidelines. We included peer-reviewed, original research studies indexed in PubMed, Embase, and WebOfScience after January 1, 2010 and through April 29, 2025. Studies were required to discuss the development, implementation, or evaluation of AI tools in rural US healthcare, including frameworks that help facilitate AI development (e.g., data warehouses). FindingsOur search strategy found 26 studies meeting inclusion criteria after full text screening with 14 papers discussing predictive AI models and 12 papers discussing data or research infrastructure. AI models most commonly targeted resource allocation and distribution. Few studies explored model deployment and impact. Half noted the lack of data and analytic resources as a limitation to both development and validation. None of the studies discussed examples of generative AI being trained, evaluated, or deployed in a rural setting. InterpretationPractical limitations may be influencing and limiting the types of AI models evaluated in the rural US. We noted validation of tools in the rural US was underwhelming, and ultimately, neglected. With few studies moving beyond AI model design and development stages, there is a clear gap in our understanding of how to reliably validate, deploy, and sustain AI models in rural settings to advance health in all communities. FundingNational Library of Medicine Research in contextEvidence before this study: Clinical artificial intelligence (AI)--both for prediction modeling and generative tools-- tools promise to reduce care delays, improve diagnosis and treatment decision-making, reduce care costs, and improve efficiency to reduce provider workload and enhance practice management. Unfortunately, efforts to deploy artificial intelligence (AI)--both for prediction modeling and generative tools--in healthcare are advancing, primarily at large academic medical centers and in urban areas. An emerging new digital divide in the use of clinical AI could exacerbate the well-documented health disparities between urban and rural communities in the United States. A better understanding of if and how AI is being developed, deployed, and evaluated across rural US communities is necessary to identify resources gaps and challenges to broad AI use in all communities. Added value of this study: This study analyzes the current state of artificial intelligence research in the rural United States. For predictive AI models, applications most commonly targeted resource allocation and distribution. We noted several attempts to predict resource utilization of patients who were either tested or tested positive to COVID-19. However, we noted few AI solutions for acute medical events faced by rural patients, such as trauma and stroke, despite worse outcomes for rural patients suffering from these acute events. The limited availability of time-critical specialties such as trauma/emergency medicine, neurology, and cardiology in rural areas often necessitates patients with such conditions be transferred to larger, more resourced hospitals. Practical limitations may be influencing and limiting the types of AI models evaluated in rural US medical facilities. The most frequent model employed were tree-based ensembles, such as random forests and gradient-boosting trees. Our review also highlighted few studies of AI models moving beyond the design and develop stages, leaving a clear gap in our understanding of how to deploy and sustain predictive AI models in rural settings. Several challenges noted in the reviewed studies may provide insight into this lack of translation from research to implementation. We note that validation of A tools in the rural US was underwhelming, and ultimately, neglected. The most common form of model validation employed was a single random holdout test set. Half of the included papers mentioned a lack of reliable data sources or limited data volume as a potential challenge in developing and adopting AI/ML tools. The use of patient-level EHR data was often limited to what was available to the authors or at a specific medical center. Implications of all the available evidence: Our review indicates a gap and highlights opportunity for innovation in leveraging AI tools to predict and support patients in rural communities. Further research is needed to enhance the translation of state-of-the-art modeling techniques into effective AI tools for use in the rural US, including exploring partnerships between academic medical centers and rural communities and solutions to logistic challenges of such partnerships, including data and resource sharing.

BackgroundArtificial Intelligence (AI) is at the forefront of todays technological revolution, enhancing efficiency in many organisations and sectors. However, in some research environments, its adoption is tempered by the risks AI poses to data protection, ethics, and research integrity. For research funding organisations (RFOs), although there is interest in the application of AI to boost productivity, there is also uncertainty around AIs utility and its safe integration into organisational systems and processes. The scoping review explored: What does the evidence say about the current and emerging use of AI?; What are the potential benefits of AI for RFOs? and What are the considerations and risks of AI for RFOs? MethodsA scoping review was undertaken with no study, language, or field limits. Due to the rapidly evolving AI field, searches were limited to the last three years (2022-2024). Four databases were searched for academic and grey literature in February 2024 (including 13 funding and professional research organisation websites). A classification framework captured the utility and potential, and considerations and risks of AI for RFOs. Results122 eligible articles revealed that current and emerging AI solutions could potentially benefit RFOs by enhancing data processes, administration, research insights, operational management, and strategic decision-making. These solutions ranged from AI algorithms to data management platforms, frameworks, guidelines, and business models. However, several considerations and risks need to be addressed before RFOs can successfully integrate AI (e.g., improving data quality, regulating ethical use, data science training). ConclusionWhile RFOs could potentially benefit from a breadth of AI-driven solutions to improve operations, decision-making and data management, there is a need to assess organisational AI readiness. Although technological advances could be the solution there is a need to address AI accountability, governance and ethics, address societal impact, and the risks to the research funding landscape.

IntroductionThe rapid evolution of digital health interventions has created challenges in navigating the ethics approval process for commercial enterprises. Recognising the need for processes that balance ethical considerations with the specifics of digital health research, this study aimed to describe what happens when enterprises seek ethical review in the UK and propose strategies for a smoother process. MethodsInductive thematic analysis was conducted on thirty-two ethics review documents (29 to an NHS Research Ethics Committee, 3 to an ethics committee at a higher education institution) submitted by digital health developers with commercial sponsors and ten semi-structured interviews with digital health enterprise representatives. ResultsEthics committees raised an average of 4.3 action points per submission. We identified five broad themes around committees concerns: ethical commitments in care; study design; digital health research peculiarities; data governance; document quality and completeness. Interviewees reported a range of experiences. Here, we identified six broad themes: submission and protocol revisions; the dynamic between parties; application time and procedures; acumen and practicality in digital health; support and guidance from RECs; enterprise expertise and resources. ConclusionWe suggest strategies for applicants to achieve a favourable decision, such as evidence-based study designs and participant support for better inclusion and equity, and identified specific pitfalls to avoid, such as lack of justification for data governance procedures. We recommend that UK research ethics committees provide adapted guidance and foster collaboration through open communication and mutual understanding, to facilitate a smoother approval process in digital health research.

PURPOSETo synthesize existing literature on patient attitudes toward AI in cancer care and identify knowledge gaps that can inform future research and clinical implementation. DESIGNA scoping review was conducted following PRISMA-ScR guidelines. MEDLINE, EMBASE, PsycINFO, and CINAHL were searched for peer-reviewed primary research studies published until February 1, 2025. The Population-Concept-Context framework guided study selection, focusing on adult patients with cancer and their attitudes toward AI. Studies with quantitative or qualitative data were included. Two independent reviewers screened studies, with a third resolving disagreements. Data were synthesized into tabular and narrative summaries. RESULTSOur search yielded 1,240 citations, of which 19 studies met the inclusion criteria, representing 2,114 patients with cancer across 15 countries. Most studies used quantitative methods (n=9) such as questionnaires or surveys. The most studied cancers were prostate, melanoma, breast, and colorectal cancer. While patients with cancer generally supported AI when used as a physician-guided tool, concerns about depersonalization, treatment bias, and data security highlighted challenges in implementation. Trust in AI was shaped by physician endorsement and patient familiarity, with greater trust when AI was physician-guided. Geographic differences were observed, with greater AI acceptance in Asia, while skepticism was more prevalent in North America and Europe. Additionally, patients with metastatic cancer were underrepresented, limiting insights into AI perceptions in this population. CONCLUSIONThis scoping review provides the first synthesis of patient attitudes toward AI across all cancer types and highlights concerns unique to patients with cancer. Clinicians can use these findings to enhance patient acceptance of AI by positioning it as a physician-guided tool and ensuring its integration aligns with patient values and expectations.

BackgroundTo provide more evidence in urolithiasis research, we have established the German Nationwide Register for RECurrent URolithiasis (RECUR) using local clinical data warehouses (CDWH). For RECUR and other registers relying on digitalized clinical data, it is crucial to ensure the datas reliability for answering scientific questions. In this work, we aim to compare the results of different CDWH-based queries on urolithiasis cases next to manual case extraction from the primary source. MethodsSources for data extraction included the Medical Center University of Freiburg (MCUF) hospital information system (HIS), MCUF performance data (a clinical data set with merged data from patients including data from various time points throughout their treatment), and MCUF reimbursement data. We extracted data on caseloads in urolithiasis algorithmically (performance and reimbursement data) and compared those to a reference group compiled of manually extracted data from the local HIS and algorithmically extracted data. ResultsAlgorithmic extraction based on performance data resulted in correct and complete case identification as compared to the reference group. The case numbers from manual extraction from HIS data and algorithmic extraction from reimbursement data differed by 14% and 12%, respectively. The reasons for deviations in HIS data included human errors and a lack of data availability from different wards. Deviations in reimbursement data arose primarily due to the merging of cases in the context of reimbursement mechanisms. As the CDWH at MCUF is part of the German Medical Informatics Initiative (MII), the results can be transferred to other medical centers with similar CDWH structure. ConclusionsThe current study provides firm evidence of the importance of clearly defining a studys target variable, e.g., urolithiasis cases, and a thorough understanding of the data sources and modes used to extract the target data. Our work clearly shows that, depending on various data sources, a case is not a case is not a case.

The integration of Artificial Intelligence (AI) in healthcare settings demands a nuanced approach that considers both technical performance and sociotechnical factors. Recognizing this, our study introduces the Clinical AI Sociotechnical Framework (CASoF), developed through literature synthesis, and refined via a Modified Delphi study involving global healthcare professionals. Our research identifies a critical gap in existing frameworks, which largely focus on either technical specifications or trial outcomes, neglecting the comprehensive sociotechnical dynamics essential for successful AI deployment in clinical environments. CASoF addresses this gap by providing a structured checklist that guides the planning, design, development, and implementation stages of AI systems in healthcare. The checklist emphasizes the importance of considering the value proposition, data integrity, human-AI interaction, technical architecture, organizational culture, and ongoing support and monitoring, ensuring that AI tools are not only technologically sound but also practically viable and socially adaptable within clinical settings. Our findings suggest that the successful integration of AI in healthcare depends on a balanced focus on both technological advancements and the socio-technical environment of clinical settings. CASoF represents a step forward in bridging this divide, offering a holistic approach to AI deployment that is mindful of the complexities of healthcare systems. The checklist aims to facilitate the development of AI tools that are effective, userfriendly, and seamlessly integrated into clinical workflows, ultimately enhancing patient care and healthcare outcomes.

BackgroundThe contemporary world is challenged by natural disasters accelerated by climate change, affecting a growing world population. Simultaneously, cancer remains a persistent threat as a leading cause of death, killing 10 million people annually. The efficacy of radiotherapy, a cornerstone in cancer treatment worldwide, depends on an uninterrupted course of therapy. However, natural disasters cause significant disruptions to the continuity of radiotherapy services, posing a critical challenge to cancer treatment. This paper explores how natural disasters impact radiotherapy practice, compares them to man-made disasters, and outlines strategies to mitigate adverse effects of natural disasters. Through this analysis, the study seeks to contribute to developing resilient healthcare frameworks capable of sustaining essential cancer treatment amidst the challenges posed by natural disasters. MethodWe conducted a Structured Literature Review to investigate this matter comprehensively, gathering and evaluating relevant academic publications. We explored how natural disasters affected radiotherapy practice and examined the experience of radiotherapy centres worldwide in resuming operations after such events. Subsequently, we validated and extended our research findings through a global online survey involving radiotherapy professionals. ResultsThe Structured Literature Review identified twelve academic publications describing hurricanes, floods, and earthquakes as the primary disruptors of radiotherapy practice. The analysis confirms and complements risk mitigation themes identified in our previous research, which focused on the continuity of radiotherapy practice during the COVID-19 pandemic. Our work describes nine overarching themes, forming the basis for a taxonomy of 36 distinct groups. The subsequent confirmative online survey supported and solidified our findings and served as a basis for developing a conceptual framework for natural disaster-resilient radiotherapy. DiscussionThe growing threat posed by natural disasters underscores the need to develop business continuity programs and define risk mitigation measures to ensure the uninterrupted provision of radiotherapy services. By drawing lessons from past disasters, we can better prepare for future hazards, supporting disaster management and planning efforts, particularly enhancing the resilience of radiotherapy practice. Additionally, our study can serve as a resource for shaping policy initiatives aimed at mitigating the impact of natural hazards.

IntroductionDiagnostic evaluation and treatment planning for newly diagnosed cancer requires a coordinated effort across multiple specialties. Delays in treatment initiation are common, leading to unnecessary anxiety and decreased survival. Given that timely treatment initiation is pivotal to providing high quality cancer care, we sought to characterize patient intake, workflows, and the role of health information technology (HIT) in a varied group of oncology practices nationwide. MethodsInterviews with oncologists were performed between March and September 2016, with follow-ups conducted between October and December 2021. Thematic analysis was used to assign codes to key elements of the transcripts, group these codes into conceptually distinct and clinically meaningful categories, and identify major cross-cutting themes. ResultsNine oncologists participated in an initial interview (one surgical, two radiation, six medical oncology). Four oncologists participated in a follow-up interview (one radiation, three medical oncology). In both time periods there was tremendous variation in staff roles and communication processes; some oncology practices obtained diagnostic studies before the first oncology consult visit, whereas others waited until after the initial consult visit to begin the diagnostic evaluation. Variability and tension were noted to arise from deficiencies in HIT, such as lack of interoperability, impaired speed and quality of data collection, cumbersome user interfaces, and variety of data types in oncology care. Oncologists reported only modest improvements in HIT between 2016 and 2021. ConclusionAssembling data to make a new cancer diagnosis and treatment plan is complex and time-intensive. HIT interoperability remains a quasi-manual process, contributing to preventable treatment delays. Federal policy supporting interoperability provides an opportunity to develop HIT that supports care coordination and patient-centered care, but effective implementation of such tools will be challenging within current workflows.