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.
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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.
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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
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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.
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Journal articleSchmidt J, Carter AW, McGuire A, et al., 2026,
A systematic review of economic evidence of artificial intelligence in healthcare.
, Health Policy, Vol: 172BACKGROUND: Ambitious claims suggest AI could save $200-360 billion annually in US healthcare and €212 billion in Europe, though the empirical evidence base supporting these projections remains unclear. OBJECTIVE: This systematic review seeks to synthesise the available economic evidence of AI technologies in healthcare and contextualise the available economic evidence against the broader policy expectations surrounding the economic impact of AI in healthcare. METHODS: We searched MEDLINE, Embase, Global Health, PsycINFO, and Cochrane Central for scientific sources. The JBI dominance ranking matrix was used to compare and interpret the results of the included economic evaluations. Methodological quality was assessed using the JBI Critical Appraisal tool of Economic Evaluations and the CHEERS-AI reporting checklist. RESULTS: We identified 16,430 academic records and 1,593 grey literature records, of which 91 records met the inclusion criteria, representing 98 unique evaluations. Of these, 36% demonstrate a clear health economic preference for the AI technology, which increased to 44% when only considering the 51 high-quality studies. AI interventions were mainly designed for healthcare providers, with ophthalmology and oncology being the most common. CONCLUSIONS: The high-quality studies show definite potential for positive cost-effectiveness and economic impact of AI technologies, though we cannot yet support the ambitious claims that AI technologies can translate to hundreds of billions in cost-savings. When combining our findings with those of randomised controlled trials evaluating AI technologies in clinical practice, it becomes evident that AI technologies frequently yield improvements in terms of clinical and economic outcomes or match the current standard of care.
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Journal articleGupta A, Prociuk D, Russo A, et al., 2026,
Automatic Conversion of NICE Guidelines to an Executable Computational Model Using Large Language Models.
, Learn Health Syst, Vol: 10INTRODUCTION: The UK National Institute for Health and Care Excellence (NICE) produce guidelines that provide evidence-based recommendations to support clinical care across England and Wales, but remain available in unstructured natural language form. Converting these guidelines into computable, logically coherent representations is an active area of research yet existing approaches typically focus on individual diseases, require substantial manual encoding, and do not scale. Recent advances in large language models offer an opportunity to automate much of this translation process. METHODS: We present an end-to-end approach that automatically converts textual clinical guidelines into an executable model capable of generating explainable patient-specific recommendations. Our approach uses a stepwise LLM-based transformation with in-context examples that can be customized to the guideline of your choice. Each step generates human-inspectable intermediate artifacts, ensuring full transparency and modifiability. We apply the approach to both pancreatic and lung cancer NICE guidelines and use expert human review to assess the alignment of the produced rules as well as evaluating the executable model over 20 pancreatic cancer patient vignettes. RESULTS: Human experts review demonstrated strong alignment between the natural language guidelines and the generated executable models, with the majority of guideline recommendations translated correctly. Most discrepancies involved partial omissions of specific details rather than incorrect logic, and instances of hallucinated or fundamentally incorrect rules were rare. When executed on the vignettes, the resulting executable models produced patient-specific recommendations with an F1 score of 82.5%. CONCLUSION: This work demonstrates that LLMs can be used to automatically transform natural language NICE guidelines into interpretable and executable models. The models preserve guideline structure, allow transparent inspection and
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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.
, J Antimicrob Chemother, Vol: 81BACKGROUND: Clinical decision support systems (CDSS), computerized tools that assist clinicians in making guideline-based decisions, may support antimicrobial prescribing in primary care. METHODS: Studies on CDSS implementation, use and outcomes in primary care on PubMed/MEDLINE and Embase were included up to June 2025. RESULTS: Of 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. CONCLUSION: CDSS may improve guideline-concordant antibiotic prescribing in primary care, but wide variability in implementation and real-world adoption limits generalizability and scalability.
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Journal articleLau SSS, Fong JWL, Lawrance EL, et al., 2026,
Extreme weather salience as a climate crisis signal: Examining the role of extreme weather fear in adaptive and maladaptive responses to eco-anxiety
, Global Environmental Change, Vol: 99, ISSN: 0959-3780Personal salience regarding extreme weather events may be crucial for recognising the profound effects of anthropogenic climate change. However, efforts to understand how individuals attribute extreme weather events to anthropogenic climate change, and their implications for eco-anxiety, have not effectively integrated psychological perspectives. Following Hong Kong’s wettest storm on record in 2023, we recruited a cross-sectional sample of adults (n = 376, ages 18–83) immediately after typhoon season onset (from 3 June to 11 September) in 2024. We aimed to examine (1) how extreme weather experiences and socio-demographic factors influence individuals’ subjective attribution of extreme weather to anthropogenic climate change, and (2) how extreme weather fear (i.e. storm fear), weather salience, uncertainty intolerance, anxiety control, nature connectedness, and future risk awareness of local climate disasters are associated with two distinct subdimensions of eco-anxiety, conceptualised as ‘habitual ecological worry’ and ‘negative consequences of eco-anxiety’, which may be associated respectively with adaptive and maladaptive responses to eco-anxiety. Our results suggest that only the personal salience of day-to-day local weather patterns significantly affected respondents’ subjective attribution of local weather patterns to anthropogenic climate impacts, rather than their extreme weather event experiences. Future risk awareness and nature connectedness were mainly associated with adaptive responses to eco-anxiety, while only storm fear was strongly associated with both adaptive and maladaptive responses. This finding underscores the dual effects of extreme weather fear, namely motivating action-oriented aspects of eco-anxiety and exacerbating functionally impairment of eco-anxiety. Promoting future risk awareness and increasing nature connectedness – independent of fear – could improve urbanised populations
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Journal articleAggarwal A, Erridge S, Varadpande M, et al., 2026,
UK Medical Cannabis Registry: A Clinical Outcomes Analysis for Autism Spectrum Disorder.
, Neuropsychopharmacol Rep, Vol: 46INTRODUCTION: Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with distressed behaviors and psychological challenges. This study aims to evaluate the change in health-related quality of life (HRQoL), anxiety, and sleep quality in autistic individuals prescribed cannabis-based medicinal products (CBMPs). METHOD: This observational case series analyzed data from the UK Medical Cannabis Registry on autistic adults treated with CBMPs. Demographic and clinical data were collected at baseline, with patient-reported outcome measures assessed up to 18 months. Primary outcomes included changes in anxiety (GAD-7), sleep quality (SQS), and HRQoL (EQ-5D-5L). Secondary outcomes included the incidence of adverse events. Statistical significance was indicated by p < 0.050. RESULTS: One-hundred and thirty individuals met the inclusion criteria. GAD-7 (p < 0.001) and SQS (p < 0.001) scores improved from baseline to 18 months. EQ-5D-5L index values showed improvement from baseline (0.43 ± 0.30) to 18 months (0.51 ± 0.32, p < 0.001), and PGIC scores increased from 1 month (5.43 ± 1.49) to 18 months (5.65 ± 1.32, p = 0.013). Twenty-five participants (19.23%) reported a total of 232 (178.46%) adverse events, with most being mild (n = 88; 67.69%) or moderate (n = 99; 76.15%). CONCLUSION: Treatment with CBMPs was associated with improvements in HRQoL, anxiety, and sleep outcomes in autistic patients over an 18-month period. Given the absence of a control group, these findings represent associations rather than proven treatment effects. Further high-quality randomized controlled trials are needed to confirm the long-term efficacy and safety of CBMPs in ASD.
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Journal articleEzzat A, Zhu Z, Roddan A, et al., 2026,
Label-free classification of breast cancer subtypes in ex vivo human tissues using Raman spectroscopy and machine learning
, Scientific Reports, Vol: 16, ISSN: 2045-2322Breast conserving surgery (BCS) aims to excise breast tumors whilst preserving breast-related quality of life, but is complicated by the challenge of accurately identifying the margin between healthy and cancerous tissue. Raman spectroscopy (RS) has been shown to distinguish between normal breast tissue and breast cancer. Thus, this study aimed to further evaluate the diagnostic performance of RS in ex vivo breast tissue subtype classification via investigation of signals from healthy tissues and three breast cancer subtypes (invasive ductal carcinoma, IDC; invasive lobular carcinoma, ILC; and ductal carcinoma in situ, DCIS). A total of 80 tissue samples (46 normal and 34 cancerous) from 71 individuals were measured using a confocal Raman microscope. Spectral signatures wereinvestigated, and supervised classification was performed for both two-class (healthy vs. cancer) and four-class (healthy vs. IDC vs. ILC vs. DCIS) classification tasks. RS successfully differentiated cancerous from normal breast tissue (97.84% sensitivity, 97.18% specificity). For four-class classification, RS achieved in-class sensitivity ranging from 83-96% and specificity from 93-99%. These findings demonstrate that RS can accurately distinguish normal from cancerous tissue and capture clinically relevant differences among histological including invasive and pre-invasive disease, supporting its promise for intraoperative tissue characterization during BCS.
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Journal articleRanne A, Maier H, Zhao J, et al., 2026,
Thermally Drawn Bioelectric Catheters: Enabling Proprioceptive Endovascular Navigation
, Advanced Intelligent Systems, ISSN: 2640-4567<jats:p>To navigate medical instruments safely and accurately inside a patient’s vascular tree, combining X‐ray fluoroscopy with intermittent contrast injections is the gold standard. However, prolonged exposure to ionizing radiation poses health risks and necessitates the use of cumbersome lead vests for the clinicians, and contrast injections can lead to acute kidney injury in patients. Bioelectric Navigation, a non‐fluoroscopic tracking modality, aims to provide an alternative. It uses weak electric currents to detect local anatomical features in the vasculature and localize instruments without X‐ray imaging. In this work, we advance Bioelectric Navigation on two frontiers. Firstly, we introduce a new class of bespokely designed electrode catheters. They are fabricated using 3D printing, thermal drawing, and laser micromachining. Specifically, we manufacture a 6 Fr catheter incorporating 16 electrodes, a guidewire channel and an additional sensor compartment. We thoroughly assess the catheter’s mechanical and electrical properties. Secondly, we introduce an algorithm to localize the catheter along the centerline of a vascular phantom, for the first time fusing electric detection of vascular geometry with electric distance estimation. We report both tracking accuracy and usability evaluated by an expert endovascular surgeon, demonstrating the strong potential of this technology for integration into the existing clinical workflow.</jats:p>
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