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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Holzinger, A; Haibe-Kains, B; Jurisica, I.
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
EUR J NUCL MED MOL I. 2019; 46(13): 2722-2730.
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Autor/innen der Med Uni Graz:
Holzinger Andreas

Dimensions Citations:

Plum Analytics:
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.

Find related publications in this database (Keywords)
Precision medicine
Artificial intelligence
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Integrative computational biology
Network-based analysis
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