The paper highlights key barriers to AI adoption in point-of-care ultrasound, such as population bias and explainability, and proposes strategies to ensure clinical effectiveness.
Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians in the acquisition and interpretation of the images; however, there are growing concerns on its effectiveness and trustworthiness. Here, we address key issues such as population bias, explainability and training of artificial intelligence in this field and propose approaches to ensure clinical effectiveness.
Vega et al. (Sat,) studied this question.