Evidence map›Paper›PMID 36289707›Full record

ArticleBiomedicines2022

Digital Technologies: Advancing Individualized Treatments through Gene and Cell Therapies, Pharmacogenetics, and Disease Detection and Diagnostics.

Peter R Corridon, Xinyu Wang, Adeeba Shakeel, Vincent Chan

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 32 citations in OpenAlex.

  1. Transforming meat waste into sustainable corneal keratoplasty models.Frontiers in bioengineering and biotechnology · 2025
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  7. From waste to wealth: Repurposing slaughterhouse waste for xenotransplantation.Frontiers in bioengineering and biotechnology · 2023
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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

4 authors at 1 institution in 1 country.

Peter R CorridonDepartment of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.ORCID 0000-0002-6796-4301
Xinyu WangDepartment of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.
Adeeba ShakeelDepartment of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.
Vincent ChanBiomedical Engineering and Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.
Khalifa University of Science and Technology · AE

Funding

Khalifa University of Science and Technology RC2-2018-022 (HEIC)Khalifa University's College of Medicine and Health Sciences FSU-2020-25
6 · The paper itself

Abstract

Digital technologies are shifting the paradigm of medicine in a way that will transform the healthcare industry. Conventional medical approaches focus on treating symptoms and ailments for large groups of people. These approaches can elicit differences in treatment responses and adverse reactions based on population variations, and are often incapable of treating the inherent pathophysiology of the medical conditions. Advances in genetics and engineering are improving healthcare via individualized treatments that include gene and cell therapies, pharmacogenetics, disease detection, and diagnostics. This paper highlights ways that artificial intelligence can help usher in an age of personalized medicine.

Indexed as

cell therapiesdiagnosticsdigital technologiesdisease detectionpersonalized medicinepharmacogenetics

Identifiers

PMID36289707
PMCPMC9599083
OpenAlexW4303613761

What OpenQuestion 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 OpenQuestion graph.