Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics : Qualitative Study

Titelangaben

Hennrich, Jasmin ; Doctor, Eileen ; Körner, Marc-Fabian ; Lederman, Reeva ; Eymann, Torsten:
Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics : Qualitative Study.
In: Journal of Medical Internet Research. Bd. 27 (2025) . - e63668.
ISSN 1438-8871
DOI: https://doi.org/10.2196/63668

Volltext

Link zum Volltext (externe URL):

Abstract

Background: Artificial intelligence (AI) applications hold great promise for improving accuracy and efficiency in medical imaging diagnostics. However, despite the expected benefit of AI applications, widespread adoption of the technology is progressing slower than expected due to technological, organizational, and regulatory obstacles, and user-related barriers, with physicians playing a central role in adopting AI applications.
Objective: This study aims to provide guidance on enabling physicians to make an informed adoption decision regarding AI applications by identifying and discussing measures to address key barriers from physicians’ perspectives.
Methods: We used a 2-step qualitative research approach. First, we conducted a structured literature review by screening 865 papers to identify potential enabling measures. Second, we interviewed 14 experts to evaluate the literature-based measures and enriched them.
Results: By analyzing the literature and interview transcripts, we revealed 11 measures, categorized into Enabling Adoption Decision Measures (eg, educating physicians, preparing future physicians, and providing transparency) and Supporting Adoption Measures (eg, implementation guidelines and AI marketplaces). These measures aim to inform physicians’ decisions and support the adoption process.
Conclusions: This study provides a comprehensive overview of measures to enable physicians to make an informed adoption decision on AI applications in medical imaging diagnostics. Thereby, we are the first to give specific recommendations on how to realize the potential of AI applications in medical imaging diagnostics from a user perspective.