Contested futures: envisioning “Personalized,” “Stratified,” and “Precision” medicine

Erikainen, Sonja; Chan, Stella W. Y.

doi:10.1080/14636778.2019.1637720

Cited by 72 publications

(63 citation statements)

References 44 publications

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“…However, the concept of PM may assume different (and sometimes paradoxical) interpretations [ 4 , 5 , 6 , 7 ]. On one hand, it seems to coincide with the advent of omic sciences (such as genomics, proteomics, metabolomics) with particular regard to Genetic and Genomic Medicine [ 8 ]. By looking for a definition of the term “Personalized Medicine”, the MeSH Browser redirects the research to “Precision Medicine” defined as follows: “Clinical, therapeutic and diagnostic approaches to optimal disease management based on individual variations in a patient’s genetic profile” [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

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D’Oria

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et al. 2021

JPM

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Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in “Personalized Medicine” and “Innovative Biomedical Technologies”) could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

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Section: Introductionmentioning

confidence: 99%

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Cesario

D’Oria

Bove

et al. 2021

JPM

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“…Precision medicine aims ultimately to tailor treatments to an individual based on molecular features (plus lifestyle and environment) of a patient and/or their disease; ideally also using companion diagnostics to determine responders and non-responders to the therapy. While the terms stratified, systems, personalised and precision (Table 1) have been used interchangeably, and in some cases fiercely debated (20) , the term precision medicine is now preferred and has been more commonly used in the medical literature since 2010 ( Fig. 1(a)).…”

Section: The Path Towards Precision Medicinementioning

confidence: 99%

From personalised nutrition to precision medicine: the rise of consumer genomics and digital health

Moore

2020

Proc. Nutr. Soc.

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Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of ‘big data’ and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.

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“…Indeed, when reading papers from this field, one is left with the impression of cautious optimism about the potential of digital technologies for the identification and treatment of ill mental health. However, some critical social scientists writing in the broader fields of personalised medicine, precision psychiatry and mhealth remind us that the promises of benefit attached to new health technologies are not superficial hype, but have a performative role, acting to garner support and funding for research (Brian and Ben‐Zeev 2014, Erikainen and Chan 2019, Pickersgill 2019). This, these scholars say, is problematic, because promises of benefit have often been shown to be misaligned with the true capabilities of a technology: technologies are misrepresented and this leads to unrealistic expectations about the technology (Bubela et al .…”

Section: Introductionmentioning

confidence: 99%

Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’

Birk

Samuel

2020

Sociology Health & Illness

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This paper critically explores the research and development of ‘digital phenotyping’, which broadly refers to the idea that digital data can measure and predict people’s mental health as well as their potential risk for mental ill health. Despite increasing research and efforts to digitally track and predict ill mental health, there has been little sociological and critical engagement with this field. This paper aims to fill this gap by introducing digital phenotyping to the social sciences. We explore the origins of digital phenotyping, the concept of the digital phenotype and its potential for benefit, linking these to broader developments within the field of ‘mental health sensing’. We then critically discuss the technology, offering three critiques. First, that there may be assumptions of normality and bias present in the use of algorithms; second, we critique the idea that digital data can act as a proxy for social life; and third that the often biological language employed in this field risks reifying mental health problems. Our aim is not to discredit the scientific work in this area, but rather to call for scientists to remain reflexive in their work, and for more social science engagement in this area.

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Contested futures: envisioning “Personalized,” “Stratified,” and “Precision” medicine

Cited by 72 publications

References 44 publications

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

From personalised nutrition to precision medicine: the rise of consumer genomics and digital health

Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’

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