Artificial intelligence in medicine: current trends and future possibilities

Buch, Varun; Ahmed, Irfan; Maruthappu, Mahiben

doi:10.3399/bjgp18x695213

Cited by 276 publications

(255 citation statements)

References 7 publications

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“…There has even been suggestion more recently that it can be used to predict when AF will occur up to an hour before the event in those with non-permanent AF [82,83]. AI has many more potential benefits, from helping gather the most useful data on a patient before a consultation to outpatient monitoring and subsequent prioritisation [36, [84][85][86][87].…”

Section: Patient Centred Care and The Role Of Technologymentioning

confidence: 99%

Screening for Atrial Fibrillation and the Role of Digital Health Technologies

Richardson¹,

Hall²,

Mitchell³

2020

Epidemiology and Treatment of Atrial Fibrillation

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Atrial fibrillation is the commonest clinical arrhythmia and a leading cause of hospital admission, morbidity and mortality. New digital health technologies are now allowing patients and the general population to identify heart rhythm abnormalities before any encounter with a medical professional. This chapter will include an overview of the prevalence of atrial fibrillation and explore the current recommendations on methods for arrhythmia screening. We discuss different risk factors as well as physiological and structural markers for atrial fibrillation onset. We explore in detail the application of novel digital health technologies such as wearables, watches and mobile devices which may have an impact on screening detection rates. The article concludes with a discussion about how to manage patients with screen detected atrial fibrillation.

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Section: Patient Centred Care and The Role Of Technologymentioning

confidence: 99%

Screening for Atrial Fibrillation and the Role of Digital Health Technologies

Richardson¹,

Hall²,

Mitchell³

2020

Epidemiology and Treatment of Atrial Fibrillation

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“…First, they tend to be relatively concrete and specific, and an important feature of 'near-term' issues is the fact that they are already fairly well-understood. Second, these examples tend to be directly related to recent progress in machine learning which have enabled increasing real-world applications of narrow and specialised AI systems: for example in medical diagnosis and predictive policing [8,28].…”

Section: 'Near-term' Issuesmentioning

confidence: 99%

Beyond Near- and Long-Term

Prunkl

Whittlestone

2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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One way of carving up the broad 'AI ethics and society' research space that has emerged in recent years is to distinguish between 'near-term' and 'long-term' research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction. We unpack the near/long-term distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas. CCS CONCEPTS• Social and professional topics → Computing / technology policy.

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“…It is now clear that a system that can recognize and detect damages from image capturing and another one that extract and classify them as a function of common variables could be very useful for large scale diagnosing. In fact, AI with this framework has been successfully used in human medicine in the last years [40]. Some attempt to use NN for structural automated inspection of steel bridges [41] and for general damage detection [42] has been recently proposed.…”

Section: Artificial Intelligence: Computer Vision and Pattern Recognimentioning

confidence: 99%

Considerations over the Italian road bridge infrastructure safety after the Polcevera viaduct collapse: past errors and future perspectives

Bazzucchi

Restuccia

Ferro

2018

Frattura Ed Integrità Strutturale

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In the last four years, Italy experienced the collapse of five road bridge: Petrulla viaduct (2014), Annone (2016) and Ancona (2017) overpasses, Fossano viaduct (2017) and Polcevera (2018) bridge. Although for deeply different reasons, the collapses occurred can all been gathered into the same common cause: the (lack of) knowledge of the effective structural condition, a serious problem that affects existing constructions. As it will be shown in the paper, different problems such as missing of the asbuilt designs, an appropriate construction and movement precautions, a heavy vehicle checking, and a material decay monitoring can nevertheless be addressed as an inadequate knowledge of what is happening to/in the structure. In the first section, the paper will report a short description of the failures for the five bridges, while in the second part a main set of problems involved in bridge safety and maintenance will be discussed. Finally, in the third part, a review on innovative and peculiar investigation and monitoring techniques will be illustrated. The collected results can shed new light on future perspectives for the Civil Engineering sector, sector that has to be ready for facing the challenges of preservation, restoration and/or replacement of the existing infrastructural constructions, worldwide.

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Artificial intelligence in medicine: current trends and future possibilities

Cited by 276 publications

References 7 publications

Screening for Atrial Fibrillation and the Role of Digital Health Technologies

Screening for Atrial Fibrillation and the Role of Digital Health Technologies

Beyond Near- and Long-Term

Considerations over the Italian road bridge infrastructure safety after the Polcevera viaduct collapse: past errors and future perspectives

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