scholarly journals Artificial intelligence, physiological genomics, and precision medicine

2018 ◽  
Vol 50 (4) ◽  
pp. 237-243 ◽  
Author(s):  
Anna Marie Williams ◽  
Yong Liu ◽  
Kevin R. Regner ◽  
Fabrice Jotterand ◽  
Pengyuan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Precision Medicine ◽  
Ethical Issues ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Common Diseases ◽  
Powerful Approach ◽  
Genetic And Environmental Factors ◽  
Analytical Approaches

Big data are a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning toward machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

scholarly journals “Who is watching the watchdog?”: ethical perspectives of sharing health-related data for precision medicine in Singapore

2020 ◽  
Author(s):  
Tamra Lysaght ◽  
Angela Ballantyne ◽  
Vicki Xafis ◽  
Serene Ong ◽  
Gerald Owen Schaefer ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Data Sharing ◽  
Data Security ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Public Trust ◽  
International Perspective ◽  
Research Participants ◽  
Related Data ◽  
Trade Offs

Abstract Background We aimed to examine the ethical concerns Singaporeans have about sharing health-data for precision medicine (PM) and identify suggestions for governance strategies. Just as Asian genomes are under-represented in PM, the views of Asian populations about the risks and benefits of data sharing are under-represented in prior attitudinal research.Methods We conducted seven focus groups with 62 participants in Singapore from May to July 2019. They were conducted in three languages (English, Mandarin and Malay) and analysed with qualitative content and thematic analysis. Results Four key themes emerged: nuanced understandings of data security and data sensitivity; trade-offs between data protection and research benefits; trust (and distrust) in the public and private sectors; and governance and control options. Participants were aware of the inherent risks associated with data sharing for research. Participants expressed conditional support for data sharing, including genomic sequence data and information contained within electronic medical records. This support included sharing data with researchers from universities and healthcare institutions, both in Singapore and overseas. Support was conditional on the perceived social value of the research and appropriate de-identification and data security processes. Participants suggested that a data sharing oversight body would help strengthen public trust and comfort in data research for PM in Singapore.Conclusion Maintenance of public trust in data security systems and governance regimes can enhance participation in PM and data sharing for research. Contrary to themes in much prior research, participants demonstrated a sophisticated understanding of the inherent risks of data sharing, analysed trade-offs between risks and potential benefits of PM, and often adopted an international perspective.

scholarly journals The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

2021 ◽  
Vol 22 ◽  
pp. 88-100
Author(s):  
Adomas Vincas Rakšnys ◽  
Dangis Gudelis ◽  
Arvydas Guogis
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Policy ◽  
Ethical Issues ◽  
Policy Formation ◽  
Modern World ◽  
Business Systems ◽  
Public Governance ◽  
Advantages And Disadvantages ◽  
Data Fragmentation

This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.

Reactive Artificial Intelligence Using Big Data in the Era of Precision Medicine

JAMA Surgery ◽  
2020 ◽  
Vol 155 (7) ◽  
pp. 671
Author(s):  
Ankita Kar ◽  
Anand Subash ◽  
Vishal U. S. Rao
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Precision Medicine

scholarly journals “Who is watching the watchdog?”: ethical perspectives of sharing health-related data for precision medicine in Singapore

2020 ◽  
Author(s):  
Tamra Lysaght ◽  
Angela Ballantyne ◽  
Vicki Xafis ◽  
Serene Ong ◽  
Gerald Owen Schaefer ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Data Sharing ◽  
Data Security ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Public Trust ◽  
International Perspective ◽  
Research Participants ◽  
Related Data ◽  
Trade Offs

Abstract Background We aimed to examine the ethical concerns Singaporeans have about sharing health-data for precision medicine (PM) and identify suggestions for governance strategies. Just as Asian genomes are under-represented in PM, the views of Asian populations about the risks and benefits of data sharing are under-represented in prior attitudinal research.Methods We conducted seven focus groups with 62 participants in Singapore from May to July 2019. They were conducted in three languages (English, Mandarin and Malay) and analysed with qualitative content and thematic analysis. Results Four key themes emerged: nuanced understandings of data security and data sensitivity; trades-offs between data protection and research benefits; trust (and distrust) in the public and private sectors; and governance and control options. Participants were aware of the inherent risks associated with data sharing for research. Participants expressed conditional support for data sharing, including genomic sequence data and information contained within electronic medical records. This support included sharing data with researchers from universities and healthcare institutions, both in Singapore and overseas. Support was conditional on the perceived social value of the research and appropriate de-identification and data security processes. Participants suggested that a data sharing oversight body would help strengthen public trust and comfort in data research for PM in Singapore.Conclusion Maintenance of public trust in data security systems and governance regimes can enhance participation in PM and data sharing for research. Contrary to themes in much prior research, participants demonstrated a sophisticated understanding of the inherent risks of data sharing, analysed trade-offs between risks and potential benefits of PM, and often adopted an international perspective.

Big data, artificial intelligence, and cardiovascular precision medicine

2018 ◽  
Vol 3 (5) ◽  
pp. 305-317 ◽  
Author(s):  
Chayakrit Krittanawong ◽  
Kipp W. Johnson ◽  
Steven G. Hershman ◽  
W.H. Wilson Tang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Precision Medicine

Digital Ethics

2022 ◽  
pp. 526-551
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Ethical Issues ◽  
Personal Data ◽  
Digital Technologies ◽  
Codes Of Ethics ◽  
Digital Ethics ◽  
Management Association ◽  
Legal Challenges ◽  
Right Of Access

The purpose of this chapter is to discuss strategies that can be applied in the domain of cyberlaw. The chapter begins by distinguishing between ethics, morality, and law. It then focuses on the relation between ethics and digital technologies. The chapter then examines proposals for what should be included in codes of ethics as well as examples of codes of ethics for IT companies. The examples include the British Computer Society, the Association for Computer Machinery, and the Data Processing Management Association. Next, ethical codes for regulating automation, computerization, and artificial intelligence are summarized. The chapter then discusses ethical issues surrounding privacy, anonymity, and personal data, including the EU's right of access by data subjects as well as issues connected with big data. The chapter then focuses on crimes caused by digitization and the protection of intellectual property. The chapter concludes by considering recent laws of ecommerce as well as social and international legal challenges of regulating cyberspace.

scholarly journals Precision Medicine and Big Data

2019 ◽  
Vol 11 (3) ◽  
pp. 275-288 ◽  
Author(s):  
G. Owen Schaefer ◽  
E Shyong Tai ◽  
Shirley Sun
Keyword(s):  
Big Data ◽  
Precision Medicine ◽  
Ethical Issues ◽  
Disease Risk ◽  
Ethical Framework ◽  
Harm Minimization ◽  
Causal Pathways ◽  
Full Explanation ◽  
Effective Diagnosis

Abstract As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological (including genetic), medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in the Ethical Framework for Big Data in Health and Research (Xafis et al. 2019) to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner.

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