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Rationale, aims and objectives: Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias.Methods: In this historical and conceptual article, we focus on two main problems: first, the data and the problem of its validity; second, the inference drawn from the data by AI, and the establishment of correlations through the use of algorithms. We use examples from the contemporary use of mobile health (mHealth), i.e. the practice of medicine and public health supported by mobile or wearable devices such as mobile phones or smart watches.Results: We show that the validity of the data and of the inferences drawn from these mHealth data are likely to be biased. As biases are insensitive to the size of the sample, even if the sample is the whole population, artificial intelligence and big data cannot avoid biases and even tend to increase them. Conclusions:The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.

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The availability of high-speed solid-state storage has introduced a new tier into the storage hierarchy. Low-latency and high-IOPS solid-state drives (SSDs) cache data in front of high-capacity disks. However, most existing SSDs are designed to be a drop-in disk replacement, and hence are mismatched for use as a cache.This paper describes FlashTier, a system architecture built upon solid-state cache (SSC), a flash device with an interface designed for caching. Management software at the operating system block layer directs caching. The FlashTier design addresses three limitations of using traditional SSDs for caching. First, FlashTier provides a unified logical address space to reduce the cost of cache block management within both the OS and the SSD. Second, FlashTier provides cache consistency guarantees allowing the cached data to be used following a crash. Finally, FlashTier leverages cache behavior to silently evict data blocks during garbage collection to improve performance of the SSC.We have implemented an SSC simulator and a cache manager in Linux. In trace-based experiments, we show that FlashTier reduces address translation space by 60% and silent eviction improves performance by up to 167%. Furthermore, FlashTier can recover from the crash of a 100 GB cache in only 2.4 seconds.