Evidence map›Paper›PMID 33298113›Full record

ReviewJournal of translational medicine2020

Precision medicine in the era of artificial intelligence: implications in chronic disease management.

Murugan Subramanian, Anne Wojtusciszyn, Lucie Favre, Sabri Boughorbel, Jingxuan Shan, Khaled B Letaief, Nelly Pitteloud, Lotfi Chouchane

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
127citing papers in PubMed, 9 pooled it
10.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

127 citing papers in PubMed, 9 syntheses or guidelines pooled it, 317 citations in OpenAlex.

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  3. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
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67 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 4 countries.

Murugan SubramanianDepartment of Microbiology and Immunology, Weill Cornell Medicine, New York, USA.
Anne WojtusciszynService of Endocrinology, Diabetology and Metabolism, Lausanne University Hospital, Lausanne, Switzerland.
Lucie FavreService of Endocrinology, Diabetology and Metabolism, Lausanne University Hospital, Lausanne, Switzerland.
Sabri BoughorbelClinical Bioinformatics Section, Research Division, Sidra Medicine, Doha, Qatar.
Jingxuan ShanGenetic Intelligence Laboratory, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar.
Khaled B LetaiefDepartment of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong.
Nelly PitteloudService of Endocrinology, Diabetology and Metabolism, Lausanne University Hospital, Lausanne, Switzerland. Nelly.Pitteloud@chuv.ch.
Lotfi ChouchaneDepartment of Microbiology and Immunology, Weill Cornell Medicine, New York, USA. loc2008@med.cornell.edu.ORCID 0000-0002-2082-7924
Cornell University · USUniversity of Lausanne · CHHong Kong University of Science and Technology · HKUniversity Hospital of Lausanne · CH

Funding

Weill Cornell Medicine-Qatar BMRP
6 · The paper itself

Abstract

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

Indexed as

Artificial IntelligenceDiabetes MellitusChronic DiseaseDisease ManagementHumansPrecision MedicineArtificial intelligenceBig-data analyticsChronic diseasesChronic inflammationDeep phenotypingExposomeMachine leaningPersonalized treatmentPrecision medicine

Identifiers

PMID33298113
PMCPMC7725219
OpenAlexW3112526052

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.