Artificial Intelligence in Public Health and Epidemiology

Thiébaut, Rodolphe; Thiessard, Frantz; Informatics, Epidemiology

doi:10.1055/s-0038-1667082

Cited by 34 publications

(13 citation statements)

References 23 publications

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“…Artificial intelligence (AI) has become a topic of central importance to the ways in which health care will change in the coming decades, with recent commentaries addressing potential transformations in clinical care [1,2], public health [3], and health system planning [4]. AI is a general purpose technology (GPT), which means it represents a core set of capabilities that can be leveraged to perform a wide variety of tasks in different contexts of application [5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and the Implementation Challenge

Shaw¹,

Rudzicz²,

Jamieson³

et al. 2019

J Med Internet Res

265

152

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Background Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. Objective In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). Methods After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. Results Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. Conclusions If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and the Implementation Challenge

Shaw¹,

Rudzicz²,

Jamieson³

et al. 2019

J Med Internet Res

265

152

View full text Add to dashboard Cite

show abstract

“…32–42 Existing methods have been applied ‘as is’ or modified slightly for use in RWD signal detection; for example, traditional epidemiologic focused surveillance adapted for hypothesis-free use 41 and prescription symmetry analysis 43 ; methods historically more associated with disease epidemiology have been implemented, 35 informatics approaches, 44,45 SRS-based methods, 46 and approaches novel for this application 42 ; as have a combination of the above approaches, as well as ‘advanced analytics’ such as Q-methodology 47 and other machine learning and artificial intelligence approaches. 44,48,49 Use of these methods will improve over time as experience with each approach accrues. For example, propensity score matching for signal detection might theoretically be improved by the use of calendar-specific propensity scores, use of multiple comparison groups, and data visualization to better understand characteristics of the study populations, although method testing would be essential to determine whether such changes would translate into meaningful routine performance improvement.…”

Section: Comparing Methods For Signal Detection In Rwdmentioning

confidence: 99%

“…There have been only a limited number of studies 26,48,50–52 examining the performance (such as sensitivity and specificity) of methods, and only some studies conducted comparative evaluation of more than one method. 35,38 No method has emerged as superior across the range of different exposure and outcomes (with varying covariates of focus) from which emerging signals need to be detected.…”

Section: Comparing Methods For Signal Detection In Rwdmentioning

confidence: 99%

Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance

Bate

Hornbuckle

Juhaeri

et al. 2019

Therapeutic Advances in Drug Safety

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“…AI technology can analyze and process very large amounts of genetics, environment and lifestyle data, and this allows for the ability of precision medicine to be applied in clinical practice. In addition, it may play an important role in health system management and public health [17,27,30,[44][45][46].…”

Section: The Applications Of Ai In Medicinementioning

confidence: 99%

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

Mishra¹,

Li²

2020

Aging and disease

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Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

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Artificial Intelligence in Public Health and Epidemiology

Abstract: Surveillance is still a productive topic in public health informatics but other very important topics in Public Health are appearing. For example, the use of artificial intelligence approaches is increasing.

Cited by 34 publications

References 23 publications

Artificial Intelligence and the Implementation Challenge

Artificial Intelligence and the Implementation Challenge

Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

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