Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre.
Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleBracey S, Ainsworth B, Alderman J, et al., 2026,
The 'Hippocratic Oath' for AI-based clinical decision support systems
, BMC Medical Informatics and Decision Making, Vol: 26, ISSN: 1472-6947BackgroundThe implementation of Artificial Intelligence assisted Clinical Decision Support Systems (AI-CDSS) shows significant potential to improve healthcare. However, implementing AI-CDSS has many associated challenges. This article introduces the ‘Hippocratic Oath’ for AI which promotes safe and effective AI-CDSS development and implementation.MethodsThis paper summarises discussions which took place during the Turing-Roche Clinical AI Interest Group Joint Workshop. The workshop began with scoping lectures from AI experts, leading into focus group discussions of key themes surrounding AI-CDSS implementation. These include the ethics, trust, evaluation, regulation, human factors and challenges involved with implementing AI-CDSS into healthcare settings. Focus group outcomes, alongside insight from lectures, were used to formulate the arguments in this paper.ResultsThis article presents a consensus definition of AI-CDSS and outlines a comprehensive table of implementation challenges alongside mitigation measures. It introduces the ‘Hippocratic Oath for AI’ and discusses its potential to promote safe and effective AI-CDSS implementation through addressing human factors and explainability.ConclusionsThe ‘Hippocratic Oath for AI’, can be used by AI-CDSS implementers and developers as a framework to mitigate challenges involved with implementing AI-CDSS into healthcare settings. This framework is likely to promote safe and effective implementation and maximise HCP uptake of the AI-CDSS. Through facilitating AI-CDSS use, this oath can transform health care practice via reducing medical errors, healthcare costs and improving patient outcomes.
-
Journal articlevan Kessel R, Carter AW, Nasir K, et al., 2026,
Artificial intelligence needs better health systems to reduce inequalities
, Npj Digital Medicine, Vol: 9Artificial Intelligence (AI) technologies are increasingly prevalent in healthcare, yet without adaptive governance, even well-designed systems risk exacerbating health inequalities. This perspective examines five interconnected governance domains: legal frameworks, evidence generation, regulation and market access, workforce readiness, and public trust. We argue these form a cyclical governance chain in which weaknesses cascade across domains, and identify critical gaps and system-level reforms to ensure AI reduces rather than amplifies disparities.
-
Journal articleAnderson M, O'Neill E, Ljungqvist G, et al., 2026,
Designing eligibility and evaluation criteria for antibacterial pull incentives: a comparative review of implemented and emerging initiatives
, Lancet Regional Health Europe, Vol: 70Background: Pull incentives aim to improve the commercial viability of antibacterials, but the criteria determining which products and companies qualify have not been compared across countries. We compared product and company eligibility and evaluation criteria across these incentives. Methods: We conducted an umbrella review of MEDLINE and Embase for English-language reviews of pull incentives targeting antibacterial innovation and access (2014 to 14 October 2024), supplemented by grey literature searches and the Global AMR R&D Hub dashboard (December 2025). Two reviewers independently extracted criteria from official documentation, using a framework of six product domains (high-priority medical need, relative effectiveness, unmet clinical need, innovative characteristics, health-system impact, and other) and five company domains (antibacterial sustainability, patient access, environmental health, economic criteria, and other). Findings: We identified 28 pull incentives: 20 implemented (UK, Sweden, Germany, France, Italy, US, Japan, and EU) and 8 proposed or in development (US, Japan, Canada, Australia, Switzerland, and EU). As mechanisms overlapped within countries, the 20 implemented incentives were analysed as nine groups. All nine targeted high-priority medical need and seven (78%) included unmet clinical need. Relative effectiveness featured in six (67%), economic criteria in five (56%), and innovative characteristics, health-system impact, patient access, and antibacterial sustainability in four each (44%); environmental health in two (22%). The UK Subscription Model applied all 11 domains and Sweden's Annual Revenue Guarantee eight (73%). Priority pathogen lists were widely referenced but varied in breadth. Interpretation: Eligibility and evaluation criteria vary substantially across countries in scope and specificity. Company-level obligations on stewardship, access, and environmental safeguards are applied inconsistently, and most comprehensive in the
-
Journal articleLi Z, Zhou Z, Lou H, et al., 2026,
A novel pouch-based tension sensor array for soft Cable-Driven Parallel Surgical Robot
, Measurement Journal of the International Measurement Confederation, Vol: 288, ISSN: 0263-2241Endoscopic Submucosal Dissection (ESD) is an advanced, minimally invasive procedure for the removal of tumors or lesions from the gastrointestinal tract. While robot-assisted surgery has enhanced the safety and efficacy of ESD, a significant limitation of current robot systems is the lack of force feedback capabilities. This deficiency increases the risk of excessive force being exerted on tissue. To address this challenge, this study proposes a soft Cable-Driven Parallel Robot (CDPR) integrated with a novel pouch-based sensor array that is disposable and has simple structure. This system utilizes hydraulic pressure sensors and pouch structures to estimate both cable tension and the three-dimensional forces at the end-effector, thereby providing the surgeon with crucial force feedback. During an ex vivo experiment, the mean absolute errors between the estimated forces and the ground truth values were 0.0267 N, 0.0659 N, and 0.0509 N for the x, y, and z axes, respectively. These results demonstrate the potential of the proposed CDPR for future clinical applications.
-
Journal articleHine J, Bush A, De Simoni A, et al., 2026,
Experiences of financial incentives to improve medication adherence for children and young people with asthma: a qualitative study.
, Arch Dis Child -
Journal articleGandhewar R, Guni A, Tandon D, et al., 2026,
Surgical Artificial Intelligence Innovation Trends and Regulatory Insights: A Systematic Analysis of Food and Drug Administration-Approved Devices.
, Ann SurgOBJECTIVE: To characterise the landscape of surgical artificial intelligence (AI) devices approved by the US Food and Drug Administration (FDA). BACKGROUND: AI has the potential to improve surgical pathways, offering capability across screening, diagnosis, decision support, and intraoperative care. Despite this, real-world adoption remains limited. Regulatory approval serves as a key step in clinical translation but is influenced by challenges in evidence generation, safety, bias, transparency, and implementation. Consequently, the characteristics, evidence base, and functional roles of FDA-cleared surgical AI devices remain incompletely defined. METHODS: FDA-cleared AI/machine learning medical devices were systematically screened for surgical relevance up to July 2025. Eligible devices were extracted for clinical application, underlying AI technology, evidence quality, and regulatory pathway. Devices were categorised using an adapted descriptive, diagnostic, predictive, prescriptive framework, and predicate networks were evaluated for risk of predicate creep. Analyses were performed in R. RESULTS: A total of 314 devices (25.1%) had surgical applications. Most were imaging-based devices and mapped to general surgery (24.8%), orthopaedic surgery (18.2%), neurosurgery (17.2%), and cardiac surgery (10.8%). The majority of devices were descriptive (50.6%) or diagnostic (40.4%), with relatively few predictive (1.3%) or prescriptive (7.6%) applications. Clinical validation was predominantly based on retrospective or bench studies, with only 5.1% supported by higher-quality prospective evidence. The premarket notification 510(k) pathway accounted for 95.2% of approvals. Among these, 37.5% demonstrated a high risk of predicate creep, reflecting substantial differences in intended use or technological characteristics compared with predicate devices. CONCLUSION: FDA-approved surgical AI devices are currently dominated by imaging-based diagnostic tools.The widespread reliance
-
Journal articleHine J, Bush A, De Simoni A, et al., 2026,
Financial INcentives to improve Asthma (FINA): a pilot RCT to improve medication adherence for children and young people with asthma.
, Arch Dis ChildOBJECTIVE: To assess effectiveness and feasibility of financial incentives to improve medication adherence for children and young people (CYP) with asthma. DESIGN: Pilot randomised controlled trial measuring adherence in CYP using an electronic monitoring device (EMD) with twice-daily reminders; design supported by patient and public involvement. SETTING: CYP were recruited between 2 and 12 weeks after attendance at an emergency department (ED) following a severe asthma exacerbation. PATIENTS: CYP aged 11-17 years old with asthma, who were prescribed inhaled corticosteroids for at least 6 months prior to their exacerbation, owned their own smartphone and understood verbal and written English. INTERVENTIONS: Intervention CYP received £1 per complete morning/£1 per complete evening inhaler dose(s) for 12 weeks (max £168). Controls received usual care (with EMD and reminder use) with no financial incentives. MAIN OUTCOME MEASURES: adherence, asthma control and psychological outcomes. Feasibility assessed by recruitment, randomisation and intervention delivery indicators. RESULTS: 32 CYP (mean age: 13.4 years (±1.64, SD); 65.6% male) were enrolled; 16 randomised to intervention. After 12 weeks, those who received financial incentives had higher median adherence (73.0%, range: 23.0-89.0%), compared with control (56.0%, range: 7.0-87.0%); this did not reach significance (p=0.077). During follow-up, intervention and control adherence significantly decreased (to 39.0%, range: 0.0-72.0%, p=0.012 and 31.5%, range: 3.0-62.0%, p=0.002, respectively). Study implementation was challenging due to slow recruitment and technological issues. CONCLUSIONS: In this small pilot study, financial incentives were not effective in sustaining adherence. Improved technology is necessary for a definitive study. Future work could explore individual characteristics that contribute to successful financial incentive use in CYP. TRIAL REGISTRATION
-
Journal articleNunez Duffourc M, Yiu A, Lam K, 2026,
A legal and ethical architecture for a surgical video data registry in England
, npj Digital Medicine -
Journal articleFerreira J, Bicknell CD, 2026,
Sex Specific Associations between Visceral Adiposity and Aneurysm Formation: A New Piece of the Abdominal Aortic Aneurysm Puzzle.
, Eur J Vasc Endovasc Surg -
Journal articleIbrahim R, Durand C, Macleod M, et al., 2026,
Implementation, adoption and impact of clinical decision support system for antibiotic prescribing in primary care: a systematic review
, Journal of Antimicrobial Chemotherapy, Vol: 81, ISSN: 0305-7453BackgroundClinical decision support systems (CDSS), computerized tools that assist clinicians in making guideline-based decisions, may support antimicrobial prescribing in primary care.MethodsStudies on CDSS implementation, use and outcomes in primary care on PubMed/MEDLINE and Embase were included up to June 2025.ResultsOf the 64 full-text articles assessed, 40 were included. Most were multicentric (n = 33, 82.5%) and conducted in high-income countries (n = 33, 82.5%), and 19 were randomized controlled trials (47.5%). CDSS mainly targeted respiratory infections (n = 24, 60%) and were integrated into electronic health records in 24 studies (60%). Implementation strategies were assessed in 19 studies with almost all using a multimodal approach, most commonly involving modification of health record systems (n = 18, 45%) and audit and feedback (n = 17, 42.5%). Adoption was highly variable: among the 11 studies reporting use, CDSS were used in more than half of consultations in only five studies. Among the 32 studies assessing clinical outcomes, 17 reported reduced overall antibiotic prescribing. Barriers to CDSS use included perceptions of redundancy, time consumption, language limitations, alert fatigue, technical issues and concerns about negative impacts on patient–doctors trust.ConclusionCDSS may improve guideline-concordant antibiotic prescribing in primary care, but wide variability in implementation and real-world adoption limits generalizability and scalability
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.
