Artificial Intelligence Adoption in Medical Imaging Diagnostics: Research Highlights New Barriers and Solutions

Strategic Insights -

Artificial intelligence (AI) applications hold great promise for improving accuracy and efficiency in medical imaging diagnostics, but widespread adoption is progressing slower than expected due to technological, organizational, and regulatory obstacles, as well as user-related barriers. Researchers at the University of Bayreuth conducted a study to identify measures to enable physicians to make informed adoption decisions regarding AI applications. The study revealed 11 measures categorized into two types: Enabling Adoption Decision Measures and Supporting Adoption Measures.

Key Takeaways:

  • The study found that physicians play a central role in adopting AI applications, and their adoption decisions are influenced by various barriers, including technological, organizational, and regulatory obstacles, as well as user-related barriers.
  • The researchers conducted a structured literature review of 865 papers and interviewed 14 experts to identify potential enabling measures for AI adoption.
  • The study revealed 11 measures to enable physicians to make an informed adoption decision on AI applications in medical imaging diagnostics, including educating physicians, preparing future physicians, and providing transparency.
  • The researchers categorized the measures into two types: Enabling Adoption Decision Measures and Supporting Adoption Measures.
  • The study aims to provide guidance for physicians and healthcare organizations to overcome the barriers to AI adoption.

Statistics:

  • 865 papers were screened in the literature review.
  • 14 experts were interviewed to evaluate the literature-based measures.
  • 11 measures were identified to enable physicians to make an informed adoption decision on AI applications.
  • The study found that AI applications hold great promise for improving accuracy and efficiency in medical imaging diagnostics, but widespread adoption is progressing slower than expected.

Sources:

  • Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study. Journal of Medical Internet Research, 2025;27.
  • Eileen Doctor, University of Bayreuth, Bayreuth, Germany.
  • Jasmin Hennrich, Marc-Fabian Korner, Reeva Lederman, and Torsten Eymann, co-authors of the study.