Artificial intelligence, physiological genomics, and precision medicine

Williams, Anna; Liu, Yong; Regner, Kevin R.; Jotterand, Fabrice; Liu, Pengyuan; Liang, Mingyu

doi:10.1152/physiolgenomics.00119.2017

Cited by 92 publications

(48 citation statements)

References 51 publications

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“…The inadequacy of the underlying ideology driving such trends has not yet been adequately acknowledged in an era of high technology medicine in which hope for progress is increasingly focused on genomics, personalised medicine, Big Data and Artificial Intelligence (Williams et al, 2018).…”

Section: A Perspective On the Futurementioning

confidence: 99%

Disconnections between medical education and medical practice: A neglected dilemma

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Daneman

2020

Global Public Health

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Medical practice has changed profoundly over the past 60 years. Many changes have also been made in medical education, often with a view to countering adverse aspects of highly specialised, commercialised and bureaucratised modern medical practice. Regardless of the state of the world today and of the variety of changes that may occur in the years ahead, excellence in the application of bedside skills and technological advances, accompanied by excellence in humanistic aspects of caring for patients as people, will remain preeminent goals at the heart of medical practice. Powerful social forces that negatively influence practice cannot be counteracted through changes in medical education alone and need to be addressed directly within health systems. Shifting healthcare towards a valued social service is arguably essential for improving both public and individual health through more widespread universal access to high quality and effectively integrated health care. ARTICLE HISTORY

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Section: A Perspective On the Futurementioning

confidence: 99%

Disconnections between medical education and medical practice: A neglected dilemma

Benatar

Daneman

2020

Global Public Health

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“…ð Þ<E tol f g , is composed by the sets of high predictive networks with similar predictive accuracy; that is, those sets of genes g that classify the samples with a prediction error O g ð Þ lower than E tol . [6][7][8] As in any other inverse problem, the cost function topography in phenotype prediction problems is composed of several flat curvilinear valleys 20,21 where the genetic signatures are located, all of them with a similar predictive accuracy of the training set. Nevertheless, in the phenotype prediction problem, the size of the high discriminatory genetic signatures varies, that is, high discriminatory genetic networks of different complexity exist, and the optimization of O g ð Þ is not always performed in the same space dimension.…”

Section: Ai Genomics and The Phenotype Prediction Problemmentioning

confidence: 99%

“…[82][83][84] Bayesian networks are computationally much more expensive than the Fisher, Holdout and Random samplers. 6 Besides, the probabilistic parameterization of the uncertainty space is not unique. Considering all the plausible networks have to provide similar results in sampling the altered genetic pathways to other samplers.…”

Section: Ai Genomics and The Phenotype Prediction Problemmentioning

confidence: 99%

“…2,5 The great heterogeneity existing in the processes that contribute to disease and health, suggests a need for tailoring medical care. 6,7 Consequently, instead of making diagnostics according to classical medicine in which decisions are taken based on disease and patient's similar characteristics; precision medicine aims to shift medicine toward prevention, personalization, and precision through genomics, AI, and biotechnology. Provided how important these toolkits are in elucidating appropriate intervention targets and medical strategies for treating individual patients, AI can play an important role in the development of personalized medicines and treatments.…”

Section: Introductionmentioning

confidence: 99%

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<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Álvarez-Machancoses¹,

deAndrés-Galiana²,

Cernea³

et al. 2020

PGPM

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The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

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“…The use of predictive risk intelligence through the combination of Artificial Intelligence (AI) and Big Data is reaching new horizons, alternatively known as smart information systems (SIS). SIS are widely used to provide intelligence in many areas predicting risk, such as supply chain management (SCM) (Bendoly, 2016), sustainability (Kant & Sangwan, 2015), medicine (Williams et al, 2018), finance (Xia, Liu, & Chen, 2013) and insurance (Baecke & Bocca, 2017). All these approaches involve the use of advanced machine learning techniques that integrate data to deliver predictions of risks affecting the critical elements of enterprises and communities.…”

Section: Introductionmentioning

confidence: 99%

Ethical Implications of Predictive Risk Intelligence

Jiya

2019

The ORBIT Journal

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This paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. Thepaper covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews.Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelligence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as social media sites. Also, there are issues relating to the transparency and accountability of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In response to these issues,the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Responsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees.This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society.ORBIT Journal DOI: https://doi.org/10.29297/orbit.v2i2.112 2

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Artificial intelligence, physiological genomics, and precision medicine

Cited by 92 publications

References 51 publications

Disconnections between medical education and medical practice: A neglected dilemma

Disconnections between medical education and medical practice: A neglected dilemma

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Ethical Implications of Predictive Risk Intelligence

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