PurposeTo identify the nature of microbial keratitis in corneal grafts and the clinical outcomes at a tertiary hospital in the United Kingdom.Patients and methodsA retrospective case series of microbial keratitis in corneal grafts at the Royal Victoria Infirmary, Newcastle upon Tyne over a 17-year period (1997-2014).ResultsA total of 759 consecutive corneal grafts were identified from the Cornea Transplantation database. Of these, 59 episodes of microbial keratitis occurred in 41 eyes of 41 patients (5.4%; 19 male, 46.3%). Median patient age was 73 years (SD=19.4 years). The most common indication for corneal transplantation was bullous keratopathy (11/41, 26.8%). There were 34/59 (57.6%) episodes of culture-positive graft keratitis; Streptococcus pneumoniae and Staphylococcus aureus were each isolated in 5/34 (14.7%) culture-positive episodes. In all, 35/59 (59.3%) episodes of microbial keratitis occurred in 22 previously failed grafts and 3 de novo graft failures. Gram-negative keratitis was more likely to cause reduced BCVA after (χ-test, P=0.02). Median graft duration was 49.5 months (SD=43.7 months). Failed grafts were significantly older (median 69 vs 27 months, P=0.009).ConclusionThis represents the longest published follow-up data on microbial keratitis and is the only of its kind in the United Kingdom. The incidence of 5.4% is comparable to that within the developed world. Graft age was significantly associated with graft failure in microbial keratitis; the ongoing risk of microbial keratitis warrants providing patients with long-term open access to hospital eye services.

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Background The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. Objective In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. Methods Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals’ perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning–enabled or non–rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. Results The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non–rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. Conclusions Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non–rule-based clinical AI implementation. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005 International Registered Report Identifier (IRRID) RR2-10.2196/33145

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