scholarly journals Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

2021 ◽  
Vol 11 (4) ◽  
pp. 265
Author(s):  
Alfredo Cesario ◽  
Marika D’Oria ◽  
Francesco Bove ◽  
Giuseppe Privitera ◽  
Ivo Boškoski ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Scientific Literature ◽  
Genomic Medicine ◽  
Brain Disorders ◽  
Systems Medicine ◽  
National Leader ◽  
Biomedical Technologies ◽  
Practical Experiences ◽  
Clinical Phenotyping

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.

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.

Trailblazing Precision Medicine in Europe: a joint view by Genomic Medicine Sweden and the Centers for Personalized Medicine, ZPM, in Germany

2021 ◽  
Author(s):  
Albrecht Stenzinger ◽  
Anders Edsjö ◽  
Carolin Ploeger ◽  
Mikaela Friedman ◽  
Stefan Fröhling ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Precision Medicine ◽  
Genomic Medicine

scholarly journals Artificial intelligence to detect unknown stimulants from scientific literature and media reports

Food Control ◽  
2021 ◽  
pp. 108360
Author(s):  
Anand K. Gavai ◽  
Yamine Bouzembrak ◽  
Leonieke M. van den Bulk ◽  
Ningjing Liu ◽  
Lennert F.D. van Overbeeke ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scientific Literature ◽  
Media Reports

Cost-effectiveness of AI in medicine from a clinical, technical, and economic perspective: A scoping review and a framework of analysis (Preprint)

2021 ◽  
Author(s):  
Jesus Gomez Rossi ◽  
Ben Feldberg ◽  
Joachim Krois ◽  
Falk Schwendicke
Keyword(s):  
Artificial Intelligence ◽  
Cost Effectiveness ◽  
Scientific Literature ◽  
Economic Aspects ◽  
Inclusion Criteria ◽  
Economic Perspective ◽  
Health Policy Analysis ◽  
The Cost ◽  
The Relationship ◽  
Potential Use

BACKGROUND Research and Development (R&D) of Artificial Intelligence (AI) in medicine involve clinical, technical and economic aspects. Better understanding the relationship between these dimensions seems necessary to coordinate efforts of R&D among stakeholders. OBJECTIVE To assess systematically existing literature on the cost-effectiveness of Artificial Intelligence (AI) from a clinical, technical and economic perspective. METHODS A systematic literature review was conducted to study the cost-effectiveness of AI solutions and summarised within a scoping framework of health policy analysis developed to study clinical, technical and economic dimensions. RESULTS Of the 4820 eligible studies, 13 met the inclusion criteria. Internal medicine and emergency medicine were the most studied clinical disciplines. Technical R&D aspects have not been uniformly disclosed in the studies we analysed. Monetisation aspects such as payment models assumed have not been reported in the majority of cases. CONCLUSIONS Existing scientific literature on the cost-effectiveness of AI currently does not allow to draw conclusive recommendations. Further research and improved reporting on technical and economic aspects seem necessary to assess potential use-cases of this technology, as well as to secure reproducibility of results. CLINICALTRIAL Not applicable

Paying for Personalized Medicine

2016 ◽  
Author(s):  
Michael Snyder
Keyword(s):  
Personalized Medicine ◽  
Genome Sequencing ◽  
Genomic Medicine ◽  
Insurance Companies

Who pays for genome sequencing in treating disease? Although it can be easily argued that genome sequencing can be used beneficially, probably the biggest detriment to implementing genomic medicine is: Who pays? Presently, many insurance companies will reimburse for sequencing the tumor genomes of...

scholarly journals A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

2020 ◽  
Vol 12 (9) ◽  
pp. 3760 ◽  
Author(s):  
Manuel Woschank ◽  
Erwin Rauch ◽  
Helmut Zsifkovits
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Scientific Literature ◽  
Future Research ◽  
Industrial Enterprises ◽  
Smart Logistics ◽  
Research Findings ◽  
Ongoing Development ◽  
Research Initiatives

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

scholarly journals The DisGeNET knowledge platform for disease genomics: 2019 update

2019 ◽  
Author(s):  
Janet Piñero ◽  
Juan Manuel Ramírez-Anguita ◽  
Josep Saüch-Pitarch ◽  
Francesco Ronzano ◽  
Emilio Centeno ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Literature ◽  
Genomic Medicine ◽  
Multiple Sources ◽  
Full Spectrum ◽  
Genomic Variants ◽  
Management Platform ◽  
Knowledge Platform ◽  
Health And Disease ◽  
Disease Associated Genes

Abstract One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

scholarly journals Methodological Proposals for the Development of Services in a Smart City: A Literature Review

2020 ◽  
Vol 12 (24) ◽  
pp. 10249
Author(s):  
Joel Serey ◽  
Luis Quezada ◽  
Miguel Alfaro ◽  
Guillermo Fuertes ◽  
Rodrigo Ternero ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Scientific Literature ◽  
Smart Cities ◽  
The Internet ◽  
Urban Settlement ◽  
Urban Settlements ◽  
Analytical Frameworks ◽  
The Internet Of Things ◽  
Conceptual Research

This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.

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