Martin Eklund

Associate professor

Karolinska Institutet

Sweden

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About

Martin Eklund is an Associate Professor at Karolinska Institutet in Sweden. His research primarily focuses on prostate cancer diagnostics, the interplay between human and artificial intelligence in medical screening, and the clinical evaluation of treatment options for severe diseases, including COVID-19. He has contributed to several significant studies, including the development of predictive models for prostate cancer and the examination of AI-driven tools in breast cancer screening.

Articles (19)

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2025

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2021

Collaborators (15)

Anna Lantz

Karolinska University Hospital

SWEDEN

Martin Eklund

Research Interests

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About

Articles (19)

Evaluating the performance of existing tools to predict clinically significant prostate cancer in men with indeterminate lesions on biparametric MRI and development of a novel multiplex model: a prospective cohort study

Data Resource Profile: STHLM0, the Stockholm Prostate Cancer Diagnostics Register

Gleason scores provide more accurate prognostic information than grade groups

Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader

Anti-CD14 Treatment in Patients With Severe COVID-19 Clinical and Biological Effects in a Phase 2 Randomized Open-Label Adaptive Platform Clinical Trial

Stockholm3 in a Multiethnic Cohort for Prostate Cancer Detection (SEPTA): A Prospective Multicentered Trial

Prostate cancer incidence and mortality in men exposed to α1-adrenergic receptor antagonists

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial

Tomorrow’s patient management: LLMs empowered by external tools

Side effects of low-dose tamoxifen

Detection of perineural invasion in prostate needle biopsies with deep neural networks

Mortality in men with castration‐resistant prostate cancer—A long‐term follow‐up of a population‐based real‐world cohort

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice

Progression on active surveillance for prostate cancer in Black men: a systematic review and meta-analysis

Clinical trial design during and beyond the pandemic: the I-SPY COVID trial

The emerging role of artificial intelligence in the reporting of prostate pathology

Biomarker discrimination and calibration with MRI-targeted biopsies: an analysis with the Stockholm3 test

Incorporating Magnetic Resonance Imaging and Biomarkers in Active Surveillance Protocols - Results From the Prospective Stockholm3 Active Surveillance Trial (STHLM3AS)

Collaborators (15)

Anna Lantz

Hari Vigneswaran

Lars Björnebo

Signe Borgquist

Pekka Ruusuvuori

Jan Adolfsson

Johan Lindberg

Kimmo Kartasalo

Mark Clements

Philip Yang

Peter Ström

Tobias Nordström

Rebecca Hubbard

Fady A Youssef

Hans Garmo

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