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Background: Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making., Main Body: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations., Conclusion: The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Electronic medical records, administrative and health insurance databases, mega national biobanks, crowd source data from mobile applications and social media, and international epidemiology consortia are emerging forms of "big data" in ophthalmology. In this review, we discuss the characteristics of big data, its potential applications in ophthalmology, and the challenges in leveraging and using these data. Importantly, in the next phase of work, it will be pertinent to further translate "big data" findings into real-world applications, to improve quality of eye care, and cost-effectiveness and efficiency of health services in ophthalmology.

The recent opening of massive health databases, as well as the development of methods and tools adapted to their data processing, questions the French model of "morbidity registry". In France in 2019, nearly 61 health registries were operating. As defined by law, these registries identify exhaustively all patients with a given disease in a given territory. Established several decades ago, these registries are part of the French surveillance system that is used for research and evaluation purposes. Since the advent of recent technological progress, large-scale databases are made available to researchers and it is possible with these databases to answer questions initially assigned to the registries. What is the place of such registries in this new context: are they obsolete or still useful? Should they be opposed to the new tools or are they complementary to them, and if so, what is their place in the new French public health ecosystem? The objective of this work was to assess the roles and missions of existing registries and to reflect on their positioning in this new environment. The French model of registry is sometimes questioned because of the complexity of its circuits, requiring a significant amount of human resources. However, the data that constitute them, validated by cross-checking information from several sources, are of very high quality, and make it possible to validate the data in the new databases (National Health Data System (NSDS) or Hospital Data Warehouses). Registries and new databases are in fact complementary, and far from jeopardizing this model, the recent opening of these databases represents an opportunity for registries to modernize their operations and respond to new missions., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)