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Purpose: There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digitised screen film mammography, 2D digital mammography and digital breast tomosynthesis) used in the development of AI technologies and present their characteristics, including their transparency of documentation, content, populations included and accessibility., Materials and Methods: MEDLINE and Google Dataset searches identified studies describing AI technology development and referencing breast imaging datasets up to June 2024. The characteristics of each dataset are summarised. In particular, the accompanying documentation was reviewed with a focus on diversity and inclusion of populations represented within each dataset., Results: 254 datasets were referenced in the literature search, 190 were privately held, 36 had barriers which prevented access, and 28 were accessible. Most datasets originated from Europe, East Asia and North America. There was poor reporting of individuals' attributes: 32 (12 %) datasets reported race or ethnicity; 76 (30 %) reported female/male categories with only one dataset explicitly defining whether these categories represented sex or gender attributes., Conclusion: Through this review, we demonstrate gaps in the data landscape for mammography, highlighting poor representation globally. To ensure datasets in breast imaging have maximum utility for researchers, their characteristics should be documented and limitations of datasets, such as their representativeness of populations and settings, should inform scientific efforts to translate data-driven insights into technologies and discoveries., Competing Interests: Declaration of competing interest MJC is Director of the Birmingham Health Partners Centre for Regulatory Science and Innovation, Director of the Centre for Patient Reported Outcomes Research, and is a National Institute for Health and Care Research (NIHR) Senior Investigator. MJC receives funding from the NIHR, UK Research and Innovation (UKRI), NIHR Birmingham Biomedical Research Centre, NIHR, Applied Research Collaboration (ARC) West Midlands, Research England, European Regional Development Fund, and the NIHR Blood and Transplant Research Unit in Precision Transplant and Cellular Therapeutics. MJC also receives funding from Innovate UK (part of UKRI), Macmillan Cancer Support, UCB Pharma, GSK, Anthony Nolan, Gilead Sciences, European Commission, European Federation of Pharmaceutical Industries and Associations, and The Brain Tumor Charity. MJC has received personal fees from Aparito, CIS Oncology, Gilead, Halfloop, Takeda Pharmaceuticals, Merck, Daiichi Sankyo, Glaukos, GSK, the Patient-Centered Outcomes Research Institute, Pfizer, Genentech, and Vertex Pharmaceuticals, outside of the submitted work. MMackintosh is an employee of Genomics England and Founder and Director of One HealthTech and Data Science for Health Equity (within One HealthTech Ltd). JG is a grantee of the 2022 Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program and declares support from RSNA Health Disparities Grant (EIHD2204), Lacuna Fund (67), Gordon and Betty Moore Foundation, and NIH (NIBIB) MIDRC grant under contracts 75N92020C00008 and 75N92020C00021. XL has received consulting fees from Hardian Health and was previously a Health Studies Scientist at Apple., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: To provide an insight into the current perspective of UK ophthalmic surgeons on the role of Descemetorhexis without endothelial keratoplasty (DWEK) for the management of Fuchs endothelial corneal dystrophy (FECD)., Materials and Methods: A Google Form (Google, UK) was electronically distributed to UK ophthalmologists with a special interest in cornea from November 2023 to June 2024. The survey consisted of 13 mandatory questions., Results: Responses were received from 36 ophthalmic surgeons from across the UK, with clinical experience ranging from 0.5 to 30 years. Descemet membrane endothelial keratoplasty (DMEK) and Descemet stripping (automated) endothelial keratoplasty (DSEK/DSAEK) were the most common approaches for managing FECD. Only four respondents had practiced DWEK, but 97.2% expressed interest in learning this technique (35/36). Additionally, 69.4% felt that DWEK would be beneficial only with RHO kinase inhibitors (25/36). All respondents agreed that the clinical field would benefit from randomised trials comparing DWEK to DMEK/DSAEK, and 97.2% were open to changing their clinical practice based on the findings of such trials (35/36)., Discussion: This article provides the first snapshot of UK ophthalmic surgeons' views on DWEK in FECD. DWEK has potential as a surgical technique in selective cases, particularly with RHO kinase inhibitors. Optimal visual outcomes with DWEK depend on careful patient selection, necessitating an evidence-based approach to define patient selection criteria. Future studies should compare visual and complication outcomes between DWEK and DMEK/DSAEK in clinical trials, incorporating RHO kinase inhibitors., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Sachdeva et al.)

During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals' country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies., Competing Interests: Declaration of interests JEA is a named researcher on grants from the Medical Research Council (MRC) and the Engineering & Physical Sciences Research Council with payments made to his institution for the delivery of other research projects; and is the co-organiser of Alan Turing Institute Clinical AI interest group (unpaid). ELe has received grants from the NHS AI Lab. MCa has received grants from the National Institute for Health and Care Research (NIHR), Health Data Research UK (HDR-UK), Innovate UK, Macmillan Cancer Support, GlaxoSmithKline, UCB Pharma, Research England as part of United Kingdom Research and Innovation (UKRI), European Commission, European Federation of Pharmaceutical Industries and Associations, Brain Tumour Charity, Gilead, Janssen, UKRI, and Merck for the delivery of other research projects; has received payment for delivering lectures from the University of Maastricht; has received a speaker fee from Cochrane Portugal; payments for reviewing from the South-Eastern Norway Regional Health Authority and Singapore National Medical Research Council; consulting fees from Aparito, CIS Oncology, Takeda, Merck, Daiichi Sankyo, Glaukos, GlaxoSmithKline, Patient-Centered Outcomes Research Institute, Genentech, Vertex, ICON, Halfloop, and Pfizer; and is associated with Proteus Consortium. MG has received grants from the Canadian Institute for Advanced Research, Helmsley Trust, Wellcome Trust, Moore Foundation, Volkswagen Foundation, I-Clinic, IBM-AI, Janssen research and development, Takeda, Quanta Computing, and Microsoft Research; and has acted as an advisor for the Symposium on Artificial Intelligence for Learning Health Systems and Conference on Health, Inference, and Learning. MDM has received grants from the Canadian Institutes of Health Research, AMS Healthcare, and the SickKids Foundation. JO works or has previously worked with AdvaMed AI Framework Group member (unpaid), AdvaMed Software Working Group member (unpaid), MedTech Europe AI Working Group member (unpaid), and MedTech Europe Digital Health Committee member (unpaid). CS has received grants from NIHR, UKRI, MRC, and NIHR Cambridge Biomedical Research Centre. RNM has received grants from MRC, British Heart Foundation, United States Agency for International Development, and HDR-UK. AKD has received grants from MRC and NIHR. XL has received grants from NIHR, Wellcome Trust, Research England, Moorfields Eye Hospital Charity; has received consulting fees from Hardian Health; has received payment or honoraria from the University of Turku; and is employed by Apple as a health scientist. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The seventh leading cause of cancer-related death globally, pancreatic ductal adenocarcinoma (PDAC) involves the exocrine pancreas and constitutes greater than 90% of all pancreatic cancers. Surgical resection in combination with systemic chemotherapy with or without radiation remains the mainstay of treatment and the only potentially curative treatment option. While there has been improvement in systemic chemotherapy, long-term survival among patients with PDAC remains poor. Improvement in the understanding of tumorigenesis, genetic mutations, the tumor microenvironment (TME), immunotherapies, as well as targeted therapies continued to drive advances in PDAC treatment. We herein review the TME, genetic landscape, as well as various metabolic pathways associated with PDAC tumorigenesis relative to emerging therapies., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Pancreatic ductal adenocarcinomas (PDAC) represent one of the deadliest cancers worldwide. Survival is still low due to diagnosis at an advanced stage and resistance to treatment. Herein, we review the main types of liquid biopsy able to help in both prognosis and adaptation of treatments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Osei-Bordom, Sachdeva and Christou.)

Background: Corneal transplantation is the most frequently performed transplantation in the UK. Despite this, the therapeutic value of matching human leukocyte antigen (HLA) subtypes for transplanted corneas remains controversial. Ocular immune privilege was originally deemed to render matching unnecessary; however, more recently, matching has demonstrated improved outcomes including graft success, amongst others. This systematic review aims to evaluate the effectiveness of major and minor antigen matching on graft outcomes in corneal transplantation., Methods: Standard systematic review methodology will be used to identify, select and extract data from observational studies and clinical trials assessing the effects of HLA matching on corneal graft outcomes. Bibliographic databases (Cochrane Library, EMBASE, MEDLINE, Web of Science, Scopus), clinical trial registers, abstract and conference proceedings, in addition to dissertation, thesis and grey literature will be searched. Neither date of publication nor language will be restricted, and non-English articles will be translated where necessary. The primary outcome will be to assess corneal graft success for different degrees of HLA matching/mismatching. The precise end outcome measure varies amongst studies and includes graft rejection, immunoreaction, failure and survival. Therefore, data will be extracted across all relevant outcome parameters and grouped for subsequent statistical tests. Risk of bias assessment will be completed, appropriate to each study design. Study selection, data extraction and risk of bias assessment will be independently completed by two reviewers. Data will be tabulated, and a narrative synthesis presented. Meta-analysis will be performed where there is sufficient homogeneity between studies to warrant its effective completion. Subgroup and sensitivity analysis will be undertaken if appropriate., Discussion: Many studies have investigated the effectiveness of HLA matching for corneal transplantation. A systematic review is needed to collate and analyse this evidence. Findings of this systematic review may form the basis of evidence-based recommendations on pre-operative HLA typing and matching of corneal grafts for transplantation., Systematic Review Registration: PROSPERO reference CRD42020198882.