Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development
TL;DR
To improve integration, stakeholders should begin any new POCUS AI development project by first examining the different domains where POCUS AI applications are most needed, including education, clinical practice, workflow, research, and administration.
Abstract
The use of artificial intelligence (AI) within medicine has increased dramatically in the past few years, and applications for point-of-care ultrasound (POCUS) have followed a similar trend. Although physicians believe that POCUS AI applications have the potential to improve clinical practice, adoption of current applications remains limited. A lack of outcomes-based evidence, bias within datasets, poor assimilation within current workflows, and regulatory uncertainty are some of the major barriers that lead to poor adoption rates. To improve integration, stakeholders (physician leaders, researchers, health care executives, and industry partners) should begin any new POCUS AI development project by first examining the different domains where POCUS AI applications are most needed, including education, clinical practice, workflow, research, and administration. A gap analysis with clearly defined outcomes should come next, followed by an examination of how the new POCUS AI application will integrate into existing workflows. Training data sets that are reflective of real-world scenarios, including limitations encountered by end users, are essential. POCUS AI applications that integrate with existing workflows, have explainable outputs, and have been codeveloped with end users will improve adoption. Although regulatory pathways are evolving, engaging regulators early in the process and identifying viable reimbursement pathways are key strategies that will improve both the development and adoption of POCUS AI applications in the future.
