Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. Conclusions Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. Study registration PROSPERO CRD42019123605.

Background— Trends in cardiovascular mortality across Europe demonstrate significant geographical variation, and an understanding of these trends has a central role in global public health. Methods and Results— Ischemic heart disease and cerebrovascular disease age-standardized death rates (as per International Classification of Diseases , ninth and tenth revisions) were collated from the World Health Organization mortality database for member states of the European Union. Trends were characterized by using Joinpoint regression analysis. An overall trend for reduction in ischemic heart disease mortality was observed, most pronounced in Western Europe (>60% for the Netherlands, United Kingdom, and Ireland) for both sexes from 1980 to 2009. Eastern European states, Romania, Croatia, and Slovakia, had modest mortality reductions. Most recently (2009), Lithuania had the highest mortality for males and females (318.1/100 000 and 166.1/100 000, respectively), followed by Latvia and Slovakia. France had the lowest mortality: 39.8/100 000 for males and 14.7/100 000 for females. Analysis of cerebrovascular disease mortality revealed that Austria had the largest reduction for both sexes (76.8% males, 76.5% females) from 1980 to 2009. The smallest improvement over this period was seen in Lithuania, Poland, and Cyprus (–5% to +20% approximately). France has the lowest present-day cerebrovascular disease mortality for both males and females (23.9/100 000 and 17.3/100 000, respectively). Conclusions— There is a growing disparity in cardiovascular mortality between Western and Eastern Europe, for which diverse explanations are discussed. The need for population-wide health promotion and primary prevention policies is emphasized.

Artificial intelligence (AI) research within medicine is growing rapidly. In 2016, healthcare AI projects attracted more investment than AI projects within any other sector of the global economy. 1 However, among the excitement, there is equal scepticism, with some urging caution at inflated expectations. 2 This article takes a close look at current trends in medical AI and the future possibilities for general practice.

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Surgically correctable pathology accounts for a sizeable proportion of the overall global burden of disease. Over the last decade the role of surgery in the public health agenda has increased in prominence and attempts to quantify surgical capacity suggest that it is a significant public health issue, with a great disparity between high-income, and low- and middle-income countries (LMICs). Although barriers such as accessibility, availability, affordability and acceptability of surgical care hinder improvements in LMICs, evidence suggests that interventions to improve surgical care in these settings can be cost-effective. Currently, efforts to improve surgical care are mainly coordinated by academia and intuitions with strong surgical and global health interests. However, with the involvement of various international organisations, policy makers, healthcare managers and other stakeholders, a collaborative approach can be achieved in order to accelerate progress towards improved and sustainable surgical care. In this article, we discuss the current burden of global surgical disease and explore some of the barriers that may be encountered in improving surgical capacity in LMICs. We go on to consider the role that international organisations can have in improving surgical care globally. We conclude by discussing surgery as a global health priority and possible solutions to improving surgical care globally.

The integration of medical and social care aims to address the fragmentation in patient services observed in many health care systems. Increasing rates of chronic disease and multimorbidity have drawn attention to the often significant reforms necessary to address these problems. In this article we discuss how integration may be achieved. To date there is no single best practice model or well-defined guidelines for integration. We suggest that three groups of patients with complex health needs would experience the greatest benefit: multimorbid patients with two or more chronic diseases, patients with moderate or severe mental health conditions, and the elderly. Integration has been demonstrated to achieve improvements in the coordination, quality, efficiency, and cost control of health care. Considering these benefits, a broad effort should be made to implement integrated care.

Increasing surgical case volume and years of practice are associated with improved performance, in a procedure-specific manner. Performance may deteriorate toward the end of a surgeon's career.

ObjectiveSince 2010, England has experienced relative constraints in public expenditure on healthcare (PEH) and social care (PES). We sought to determine whether these constraints have affected mortality rates.MethodsWe collected data on health and social care resources and finances for England from 2001 to 2014. Time trend analyses were conducted to compare the actual mortality rates in 2011–2014 with the counterfactual rates expected based on trends before spending constraints. Fixed-effects regression analyses were conducted using annual data on PES and PEH with mortality as the outcome, with further adjustments for macroeconomic factors and resources. Analyses were stratified by age group, place of death and lower-tier local authority (n=325). Mortality rates to 2020 were projected based on recent trends.ResultsSpending constraints between 2010 and 2014 were associated with an estimated 45 368 (95% CI 34 530 to 56 206) higher than expected number of deaths compared with pre-2010 trends. Deaths in those aged ≥60 and in care homes accounted for the majority. PES was more strongly linked with care home and home mortality than PEH, with each £10 per capita decline in real PES associated with an increase of 5.10 (3.65–6.54) (p<0.001) care home deaths per 100 000. These associations persisted in lag analyses and after adjustment for macroeconomic factors. Furthermore, we found that changes in real PES per capita may be linked to mortality mostly via changes in nurse numbers. Projections to 2020 based on 2009-2014 trend was cumulatively linked to an estimated 152 141 (95% CI 134 597 and 169 685) additional deaths.ConclusionsSpending constraints, especially PES, are associated with a substantial mortality gap. We suggest that spending should be targeted on improving care delivered in care homes and at home; and maintaining or increasing nurse numbers.