Rafael Misse

Data preparation for the implementation of artificial intelligence models in medical images

The implementation of artificial intelligence (AI) models in the context of radiology have shown unprecedented potential in terms of aiding in the diagnosis and treatment of diseases [1]. However, to ensure only the most high quality, and robust AI algorithms are implemented in clinical practice , there is an imminent need to prepare the training

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Women who are shaping the future of artificial intelligence in medical imaging

Women are and have been instrumental to the advancement of A.I. In medicine, fundamental contributions from women working extensively with artificial intelligence applied to radiology revolve around the use of tools such as machine learning to improve diagnostic accuracy, thus contributing to the rapid institution of pharmacological treatment and non-pharmacological corroborating respectively for better clinical

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How much data is needed to get FDA clearance?

Obtaining Food and Drug Administration (FDA) authorization to market a new medical device in the United States can be time-consuming and challenging. An especially critical step during this process is determining the amount of data needed to demonstrate device safety and effectiveness. This article will explore the question, “How much data is needed to get

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Cardiovascular diseases new perspectives for the use of AI in management and diagnosis?

Cardiovascular diseases (CVDs) are currently one of the main causes of morbidity and mortality worldwide, affecting millions of people annually. Data prepared by the World Health Organization (WHO) show the prevalence of 31% of deaths in the general population are caused by CVDs, which is, respectively, equivalent to 17.9 million deaths per year [1]. In

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The AI era: How artificial intelligence is used to improve patient data safety?

As a result of the significant advances in the field of medicine and technology worldwide, health centers, hospitals and clinical and scientific research institutes have called this period the “era of artificial intelligence (AI)”, the era of AI has brought numerous advances to the field of medicine, especially in the field of patient data security.

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Real-world evidence (RWE) applied to Medical Imaging: Where are we going?

Real-world evidence (RWE) is a term applied to describe the use of data collected from routine clinical practice, as opposed to data collected in clinical trials, which prioritized environmental control to understand the potential effects of an intervention or exposition on variables dependent on results [1]. Essentially, RWE covers the use of data from medical

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The main challenges in Medical Imaging IA

The last few years have seen significant changes in the healthcare industry due to the COVID-19 pandemic. These changes have led to increased investment in artificial intelligence (AI)-based medical devices, such as those used in telemedicine, precision medicine (remote surgical procedures), and integrative algorithms for clinical, laboratory, and imaging contexts. According to data from the

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Artificial intelligence: what’s behind it?

Artificial intelligence (AI) can be understood essentially as the use of devices which have the capacity to mimic human cognitive processes, in turn, capable of learning and solving complex problems [1]. The AI nomenclature was publicly introduced at the Dartmouth conference in 1956, since then, there have been significant advances, especially in the application of

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