Evidence map›Paper›PMID 35704152›Full record

ReviewMedical oncology (Northwood, London, England)2022

Artificial intelligence and machine learning in precision and genomic medicine.

Sameer Quazi

RetractedOpen access · hybridAbstract readReviewRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Review in Medical oncology (Northwood, London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 124 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
124citing papers in PubMed, 8 pooled it
31.6field-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

124 citing papers in PubMed, 8 syntheses or guidelines pooled it, 387 citations in OpenAlex.

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  9. Review
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  11. Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026
    Review
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64 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 2 countries.

Sameer QuaziGenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India. colonel.quazi@gmail.com.ORCID http://orcid.org/0000-0002-1258-4088
Emergent BioSolutions (Canada) · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Indexed as

Artificial IntelligenceGenomic MedicineAlgorithmsHumansMachine LearningPrecision MedicineArtificial IntelligenceGenomic MedicineMachine LearningPrecision MedicineTherapeutic

Identifiers

PMID35704152
PMCPMC9198206
OpenAlexW4282935413

What Socratic holds

Textmetadata
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.