Radiological imaging is a central facet of the multidisciplinary evaluation of suspected child physical abuse. Current guidelines for the imaging of suspected child physical abuse are often unclear, incomplete and highly variable regarding recommendations on critical questions, thereby risking clinical heterogeneity, unstructured decision-making and missed diagnoses. We, therefore, aim to develop and report an evidence-based and consensus-derived international guideline for the radiological investigation of index and contact children in the context of suspected physical abuse and to ascertain areas of scientific uncertainty to inform future research priorities.

The international guidelines for the imaging investigation of suspected child physical abuse (IGISPA) consensus group includes formal representation from 127 recognised experts across 14 subspecialties, six continents and 32 national and/or international organisations. Participants will be divided into five longitudinal subgroups (indications for imaging, skeletal imaging, visceral imaging, neuroimaging and postmortem imaging) with three cross-cutting themes (radiography, genetics and adaptations for low- and lower-middle-income countries). Each subgroup will develop preliminary consensus statements via integration of current evidence-based guidelines, systematic literature review and the clinical expertise of a multinational group of experts. Statements will then undergo anonymised voting in a modified e-Delphi process and iterative revision until consensus (≥80% agreement) is achieved. Final statements will undergo both internal and external peer review prior to endorsement.

As an anonymous survey of consenting healthcare professionals, this study did not require ethical approval. Experts provided written informed consent to participate prior to commencement of the modified Delphi process. The IGISPA consensus statement and any subsequent guidance will be published open access in peer-reviewed medical journals.

Musculoskeletal shoulder pain is a common reason for people to be treated in physiotherapy services, but diagnosis can be difficult and often does not guide treatment or predict outcome. People with shoulder pain cite a need for clear information, and timely, tailored consultations for their pain. This trial will evaluate the introduction of a personalised guided consultation to help physiotherapists manage care for individuals with shoulder pain.

This is a cluster randomised controlled trial to evaluate the clinical and cost-effectiveness of introducing a personalised guided consultation compared with usual UK NHS physiotherapy care. Physiotherapy services (n=16) will be randomised in a 1:1 ratio to either intervention (physiotherapy training package and personalised guided consultation incorporating a new prognostic tool) or control (usual care); 832 participants (416 in each arm) identified from participating physiotherapy service waiting lists aged 18 years or over with shoulder pain will be enrolled. Follow-up will occur at 3 time points: 6 weeks, 6 months and 12 months. The primary outcome will be the Shoulder Pain and Disability Index (SPADI) score over 12 months. Secondary outcomes include global perceived change of the shoulder condition, sleep, work absence and the impact of shoulder pain on work performance, healthcare utilisation and health-related quality of life (using EuroQol 5 Dimension 5 Level (EQ-5D-5L)). A multimethod process evaluation will investigate views and experiences of participants and physiotherapists, assess uptake, facilitators and barriers to delivery, and changes in factors assumed to explain intervention outcomes. Primary analysis of effectiveness will be by intention-to-treat, and a health economic evaluation will assess cost-utility of introducing the personalised consultation.

The trial received ethics approval from the Yorkshire & The Humber (South Yorkshire) Research Ethics Committee (REC reference: 23/YH/0070). Findings will be shared through journal publications, media outlets and conference presentations. Supported by patient contributors and clinical advisors, we will communicate findings through a designated website, networks, newsletters, leaflets and in the participating physiotherapy services.

ISRCTN45377604.

This study aimed to systematically map the evidence and identify patterns of barriers and facilitators to clinician artificial intelligence (AI) acceptance and use across the types of AI healthcare application and levels of income of geographic distribution of clinician practice.

This scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guideline.

PubMed and Embase were searched from 2010 to 21 August 2023.

This scoping review included both empirical and conceptual studies published in peer-reviewed journals that focused on barriers to and facilitators of clinician acceptance and use of AI in healthcare facilities. Studies that involved either hypothetical or real-life applications of AI in healthcare settings were included. Studies not written in English and focused on digital devices or robots not supported by an AI system were excluded.

Three independent investigators conducted data extraction using a pre-tested tool meticulously designed based on eligibility criteria and constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to systematically summarise data. Subsequently, two independent investigators applied the framework analysis method to identify additional barriers to and facilitators of clinician acceptance and use in healthcare settings, extending beyond those captured by UTAUT.

The search identified 328 unique articles, of which 46 met the eligibility criteria, including 44 empirical studies and 2 conceptual studies. Among these, 32 studies (69.6%) were conducted in high-income countries and 9 studies (19.6%) in low-income and middle-income countries (LMICs). In terms of the types of healthcare settings, 21 studies examined primary care, 26 focused on secondary care and 21 reported on tertiary care. Overall, drivers of clinician AI acceptance and use were ambivalent, functioning as either barriers or facilitators depending on context. Performance expectancy and facilitating conditions emerged as the most frequent and consistent drivers across healthcare contexts. Notably, there were significant gaps in evidence examining the moderator effect of clinician demographics on the relationship between drivers and AI acceptance and use. Key themes not encompassed by the UTAUT framework included physician involvement as a facilitator and clinician hesitancy and legal and ethical considerations as barriers. Other factors, such as conclusiveness, relational dynamics, and technical features, were identified as ambivalent drivers. While clinicians’ perceptions and experiences of these drivers varied across primary, secondary and tertiary care, there was a notable lack of evidence exclusively examining drivers of clinician AI acceptance in LMIC clinical practice.

This scoping review highlights key gaps in understanding clinician acceptance and use of AI in healthcare, including the limited examination of individual moderators and context-specific factors in LMICs. While universal determinants such as performance expectancy and facilitating conditions were consistently identified across settings, factors not covered by the UTAUT framework such as clinician hesitancy, relational dynamics, legal and ethical considerations, technical features and clinician involvement emerged with varying impact depending on the level of healthcare context. These findings underscore the need to refine frameworks like UTAUT to incorporate context-specific drivers of AI acceptance and use. Future research should address these gaps by investigating both universal and context-specific barriers and expanding existing frameworks to better reflect the complexities of AI adoption in diverse healthcare settings.