Research Unmuted

Research Unmuted (beta) is an experimental audio platform for researchers, clinicians, and science enthusiasts. Focusing on diabetes, cardiometabolic diseases, data science and machine learning , we turn complex, peer-reviewed studies into clear audio summaries and podcasts.

Diabetes & Metabolism

Explore the latest research on diabetes, cardiometabolic diseases, and metabolic health.

Data & Technology

Insights from wearables, continuous monitoring, and digital health innovations.

AI & Modeling

Artificial intelligence and predictive models for clinical insights.

About

Research Unmuted is a scholarly audio journal presenting contemporary research in diabetes, cardiometabolic diseases, medical informatics, data science, AI, machine learning, and related topics. Episodes discuss peer-reviewed studies, methodological insights, and clinical implications, translating complex research into an accessible format. All content is produced by researchers under the guidance of the Editor-in-Chief and with advisory input from the Editorial Board. Generative AI is used solely to create high-quality audio productions, while the scientific content remains researcher-generated. The series is released as open access, DOI-archived via Zenodo, and intended for academic audiences as well as other interested listeners.

Title: Research Unmuted
Medium: Online-only serial
Type: Audio/podcasts
URL: Research-Unmuted.uk
Publication frequency: Irregular
First publication year: 2026-onwards
Indexed: OpenAIRE
eISSN: In progress

Simon Cichosz, PhD

Editor-in-Chief

Department of Health Science and Technology,
Aalborg University, Denmark

Associate Professor in Medical Informatics. Specializes in AI, ML, & CGM data analysis.

Test, PhD

Board Member

Department of Predictive Analytics,
Example University, Country

Senior Researcher in Predictive Analytics. Focused on machine learning.

Test2, PhD

Board Member

Department of Predictive Analytics,
Example University, Country

Senior Researcher in Predictive Analytics. Focused on machine learning.

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