Habib Zaidi is an expert in Nuclear medicine at University Hospital of Geneva (Switzerland). As of October 2026, ScienceLeadR ranks Habib Zaidi in the top 1% of experts worldwide for Machine Learning, Deep Learning and Artificial Intelligence. Habib Zaidi has authored 726 publications, the most recent in 2026, and is an investigator in 1 clinical trial. SLR score: 2,617 (community average: 125). How the SLR is computed.

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Habib Zaidi University Hospital of Geneva Switzerland
2 617 ScienceLeadR Reputation 2617
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Nuclear medicine

Subspecialties: Medical Imaging - Oncology - Informatics

Full career – Last 3 years

Main Topics

Publications and Clinical Studies

Publications – Clinical Studies

Profile data updated on

Expert Rankings

Top experts on the MeSH terms this expert works on

Organizations

Career arc: 1996 - 2035

Source: PubMed

University Hospital of Geneva

Publications Synthesis

Sources: PubMed, OpenAlex

Breakdown by type

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Latest Publications

5 most recent indexed articles

Source: Pubmed

Title Journal Year Citations
Impact of dose reduction and multi-parametric harmonization on multi-organ radiomics robustness in total-body PET imaging: Methodological implications for quantitative theranostics.
PMID 42922149 — biophysics, positron-emission tomography, multiple chronic conditions, drug tapering
Med Phys20260
Nonlinear stability phenotypes and collapse dynamics of multi-organ radiomics in total-body PET imaging: toward digital twin-oriented robustness assessment.
PMID 42899676 — physics, positron-emission tomography, phenotype, multiple chronic conditions, drug tapering, digital twin
Phys Med Biol20260
Enhanced Lymphoma Subtype Classification and Prognosis Using Machine Learning with 18F-FDG PET/CT Radiomics: Beyond SUV max.
PMID 42824644 — lymphoma, neoplasms, classification, positron emission tomography computed tomography, liver, machine learning, fluorodeoxyglucose f18, learning, prognosis
Nucl Med Mol Imaging20261
Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.
PMID 42728548 — intelligence, workflow, neoplasms, radiopharmaceuticals, therapeutics, learning, artificial intelligence
Mol Imaging Biol20261
Deep learning-guided late-phase dynamic frame prediction for accelerated Patlak analysis in 18F-FDG-PET brain imaging.
PMID 42637063 — positron-emission tomography, brain, fluorodeoxyglucose f18, diagnostic imaging, deep learning, learning
Neuroimage20260

Clinical Studies 1

Sources: ClinicalTrials, EU CTIS

Synthesis

By phase:

N/A 1

By status:

Completed 1

By type:

Interventional 1

By Monocentric/Multicentric:

monocentric 1
multicentric2to4 0
multicentric5to9 0
multicentricup10 0

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