Tariq Osman Andersen

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Associate Professor

University of Copenhagen

Denmark

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About

Tariq Osman Andersen is an Associate Professor at the University of Copenhagen, Denmark. His research focuses on the intersection of artificial intelligence and healthcare, particularly in the use of wearable technology for monitoring patient outcomes and predicting cardiac events. Recent articles highlight his work on AI support in clinical settings, activity and sleep correlation with medical interventions, and behavioral digital biomarkers in patient care.

Recent Grants

Grant: Close

SCAUT DECISIO: Preventive and Personalised Remote Care for Heart Patients

Open Date: 2020-01-01

Close Date: 2022-01-01

Grant: Close

DataPitch: Prediction of cardiac arrest using machine learning

Open Date: 2019-01-01

Close Date: 2019-01-01

Grant: Close

SCAUT

Open Date: 2014-10-01

Close Date: 2018-01-01

Articles (12)

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2021

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2020

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2020

Collaborators (9)

Jesper Hastrup Svendsen

Professor

University of Copenhagen

DENMARK

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Enrico Coiera

Macquarie University

AUSTRALIA

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Yvonne Rogers

University College London

UNITED KINGDOM

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Reinoud E. Knops

NETHERLANDS

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Søren Zöga Diederichsen

DENMARK

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Fleur V Y Tjong

NETHERLANDS

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Peter Karl Jacobsen

Head of Rigshospitalets Heart Tech Team

Rigshospitalet

DENMARK

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Henriette Langstrup

Københavns Universitet

DENMARK

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Stina Matthiesen

Københavns Universitet

DENMARK

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Tariq Osman Andersen

Research Interests

Contact this professor

About

Recent Grants

Articles (12)

Towards Clinically Useful AI: From Radiology Practices in Global South and North to Visions of AI Support

Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population

Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter-defibrillator interventions: a prospective SafeHeart substudy

Behavioural digital biomarkers enable real-time monitoring of patient-reported outcomes: a substudy of the multicentre, prospective observational SafeHeart study

Artificial intelligence-enhanced wearable technology enables ventricular arrhythmia prediction

Introduction to the Special Issue on Human-Centred AI in Healthcare: Challenges Appearing in the Wild

Clinician-Facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study

Interpretation as luxury: Heart patients living with data doubt, hope, and anxiety

Collaborators (9)

Jesper Hastrup Svendsen

Enrico Coiera

Yvonne Rogers

Reinoud E. Knops

Søren Zöga Diederichsen

Fleur V Y Tjong

Peter Karl Jacobsen

Henriette Langstrup

Stina Matthiesen

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