Organ damage is often detected late, when treatment options are limited. The use of artificial intelligence to continuously monitor a patient's medical data can identify people at risk of imminent ...kidney injury.
High-Definition Medicine Torkamani, Ali; Andersen, Kristian G.; Steinhubl, Steven R. ...
Cell,
08/2017, Letnik:
170, Številka:
5
Journal Article
Recenzirano
Odprti dostop
The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of ...resolution—what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.
The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution—what is referred to as high-definition medicine.
Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected ...over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model
that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.
Novel types of data at the individual level and the ability to analyse them with artificial intelligence (AI) models have the potential to make cancer screening more efficient and cost-effective. Yet ...continued development of AI models to efficiently integrate an increasing number of data sources and the validation of AI models in diverse populations, including in randomised controlled trials, will be needed. Looking ahead, health-care systems could harness a shift towards more informative screening to improve efficiency and cost-effectiveness—with improved accuracy and outcomes at the individual and population levels.
The last few years have seen extensive efforts to catalogue human genetic variation and correlate it with phenotypic differences. Most common SNPs have now been assessed in genome-wide studies for ...statistical associations with many complex traits, including many important common diseases. Although these studies have provided new biological insights, only a limited amount of the heritable component of any complex trait has been identified and it remains a challenge to elucidate the functional link between associated variants and phenotypic traits. Technological advances, such as the ability to detect rare and structural variants, and a clear understanding of the challenges in linking different types of variation with phenotype, will be essential for future progress.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Many of the outcome definition elements—eg, low blood pressure, vasopressor requirements, and antibiotic administration—also serve as potential predictors for the AI algorithm or downstream treatment ...recommendations. Given that these assessments and treatments can differ between hospitals, the effectiveness of these algorithms can vary widely on the basis of local clinical practice and documentation patterns. Besides place, the other important and dynamic dimension is time. ...all AI implementations must include scheduled local evaluation, algorithm surveillance, and updating.
Digitising the outbreak Page, Brady; Topol, Eric J
The Lancet (British edition),
12/2023, Letnik:
402, Številka:
10418
Journal Article
Recenzirano
Remote monitoring showed improved safety outcomes for individuals with COVID-19, meanwhile minimising potentially infectious trips to health-care settings and protecting precious hospital beds. The ...need for public health institutions to make dedicated efforts to rebuild public trust was highlighted during the COVID-19 pandemic. ...WhatsEpi would need to ensure that its methods are sufficiently transparent so that people across class, cultural, and political lines will recognise what is being done with their data. ...a system would only become a reality if technologies like machine learning, GIS, environmental sampling, genomics, and remote monitoring are leveraged to present the general public with real-time personalised alerts that would have real implications for their wellbeing.
Its often said that data are the new gold, or the new oil, but they are much more like a New World distinguished, at least in part, by new maps. Indeed, the planet is becoming a new world of ...relationships, descriptive data and information flows. There are now over 1.5 billion registrants on Facebook (Menlo Park, CA, USA), and a Swedish startup called Truecaller (Stockholm) has assembled a phone directory of >1.6 billion human beings, with the intent of having every person on the planet in its directory.
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