1st Sep 2026

In partnership with the Australasian Sonographers Association (ASA), and Sonography Canada, we are pleased to offer a range of free access educational and learning resources. 

BMUS is working in partnership throughout September to provide a selection of educational resources focusing on AI and Innovation.

We have come together with the ASA and Sonography Canada to provide a range of resources free of charge to ultrasound communities around the world. They include Conference recordings, Journal Articles, Webinars, Top tips and YouTube videos all in one place. 

These resources will be available until  30th September 2026.

British Medical Ultrasound Society

Journal articles:

Investigation of artificial intelligence–based clinical decision support system’s performance in reducing the fine needle aspiration rate of thyroid nodules: A pilot study - Amy Barnes, Rebecca White, Heather Venables, Vincent Lam, Ram Vaidhyanath, 2025

Evaluating the use of BodyWorks Eve® high-fidelity ultrasound simulation equipment in formative clinical assessments - Jane Arezina, Sandra Morrissey, Wendy Harrison, 2025

An ultrasound-based simulation to improve clinical competency in evaluation of first-trimester bleeding in undergraduate medical education - Anna Marie Pacheco Young, Ismely Minaya, Russell Horowitz, Weronika Armstrong, 2025

Artificial intelligence-assisted focused cardiac ultrasound training: A survey among undergraduate medical students - Hatem Soliman-Aboumarie, Jolien Geers, Dominic Lowcock, Trisha Suji, Kimberley Kok, Matteo Cameli, Eftychia Galiatsou, 2025

LogMyScan: A pilot evaluation study of a mobile phone–based ultrasound logbook application - Robert David Jarman, David Anderson, Simon Richards, Tyrone Davison, Siobhan Fenton, Alan Batterham, Cormac Ryan, Philip Cosson, 2024

An initial framework for use of ultrasound by speech and language therapists in the UK: Scope of practice, education and governance - Jodi Elizabeth Allen, Joanne Cleland, Mike Smith, 2023

Presentation:

The role of Artificial Intelligence (AI) learning algorithms in enhancing quantitative image analysis measurements.

The Australasian Sonographers Association

Journals:

Deep learning: Integrating artificial intelligence into the sonography curriculum

Introduction to special issue ‘Evolution of Sonography: Into the future

Diagnostic accuracy using artificial intelligence and ultrasound for rotator cuff pathology: A narrative review

Effective abdominal ultrasonographic detection of pancreatic cystic lesions using artificial intelligence-assisted noise reduction

Integration of artificial intelligence with medical diagnostic sonography

The Advancement of Artificial Intelligence in Point-of-Care Ultrasound (POCUS): A Bibliometric Analysis

Artificial Intelligence Guided Nonexpert Echocardariogram in the COVID-19 Health Action Response in Marines 2.0 Study

Webinars:

Disruptive trends in medical imaging

AI in the scan room

Podcast:

Emerging tech in the UK

Quantitative ultrasound research

Micro-ultrasound prostate cancer

Robotics in cardiac ultrasound

New PortraitVue AI ultrasound technology

Sonography Canada

Journal:

Artificial Intelligence in Point-of-Care Ultrasound

Artificial Intelligence and Technology in Cardiac Disease

Can Artificial Intelligence Assess Image Quality in Point-Of-Care Ultrasound?

Canadian Association of Radiologists

The Canadian Association of Radiologists’ (CAR) HAIVN (Health AI Validation Network) is an independent, clinician-led oversight framework designed to validate, monitor, and optimize artificial intelligence (AI) tools used across Canadian healthcare. Proposed by the CAR and supported by Sonography Canada, it aims to ensure that medical AI applications are safe, effective, and ethically compliant.

The CAR’s Priorities for the Federal Government

From uncertainty to trust: Investment in HAIVN to safely scale AI in Canadian healthcare

Study on the Impact of Artificial Intelligence in Canada

HAIVN's Framework for Safe, Ongoing AI Oversight

News

Portable ultrasound tool uses AI to detect arm fractures more quickly

Making health care more equitable one ultrasound image at a time

uOttawa-led team develops AI-enabled model to diagnose birth defect in fetal ultrasound images

Bringing AI to the heart of cardiac care

Combining ultrasound and AI to better customize cancer treatment

Podcast

How AI is Making Ultrasounds Accessible for Everyone with Jacob Jaremko