Evidence map›Paper›PMID 31203421›Full record

ReviewEuropean journal of nuclear medicine and molecular imaging2019

Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.

Andreas Holzinger, Benjamin Haibe-Kains, Igor Jurisica

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. VFLING: Vertical Federated Learning for Multi-Omics Data Integration with Graphs.Interdisciplinary sciences, computational life sciences · 2026
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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

3 authors.

Andreas HolzingerInstitute for Medical Informatics / Statistics, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria.
Benjamin Haibe-KainsPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Igor JurisicaDepartments of Medical Biophysics and Computer Science, University of Toronto, Toronto, Canada. juris@ai.utoronto.ca.ORCID http://orcid.org/0000-0002-2507-946X

Funding

Ontario Research Fund 34876
6 · The paper itself

Abstract

Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imaging, and molecular profile data is necessary to understand complex diseases, and to achieve accurate diagnosis in order to provide the best possible treatment. In addition to the need for sufficient computing resources, suitable algorithms, models, and data infrastructure, three important aspects are often neglected: (1) the need for multiple independent, sufficiently large and, above all, high-quality data sets; (2) the need for domain knowledge and ontologies; and (3) the requirement for multiple networks that provide relevant relationships among biological entities. While one will always get results out of high-dimensional data, all three aspects are essential to provide robust training and validation of ML models, to provide explainable hypotheses and results, and to achieve the necessary trust in AI and confidence for clinical applications.

Indexed as

Artificial IntelligenceComputational BiologyMolecular ImagingBiomarkersHumansImage Processing, Computer-AssistedBiomarkersArtificial intelligenceDecision supportIntegrative computational biologyMachine learningNetwork-based analysisPrecision medicineRadiomics

Identifiers

PMID31203421

What OpenQuestion 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 OpenQuestion graph.