Patients Support AI as Radiologist Backup in Screening Mammography

A study published in Radiology: Imaging Cancer by Dr. Basak E. Dogan and colleagues examined patient perceptions of artificial intelligence (AI) use in screening mammography, revealing cautious support for AI as a radiologist backup. Conducted among 518 patients at the University of Texas Southwestern Medical Center, the survey found that while 71% preferred AI to serve as a second reader, less than 5% were comfortable with AI alone interpreting their mammograms.

Factors such as education level, racial background, and personal or familial medical history significantly influenced trust in AI, with higher concerns about bias and privacy among Hispanic and non-Hispanic Black respondents. The findings underscore the need for personalized AI integration strategies to address diverse patient perspectives and ensure trust in AI adoption within breast imaging.

Patient Support for AI in Screening Mammography

A recent survey highlights patient support for AI in screening mammography, with 71% preferring AI as a second reader. Conducted among diverse patients, the study revealed cautious optimism despite concerns about data privacy and bias.

Demographic factors significantly influence trust in AI. Patients with higher education or greater AI knowledge were more accepting, while Hispanic and Black respondents expressed heightened concerns about bias and privacy, affecting their acceptance levels.

Personal medical history also plays a role. Patients with a family history of breast cancer showed varied trust levels in AI compared to radiologists. This underscores the need for personalized strategies when integrating AI into healthcare settings.

The findings emphasize the importance of addressing patient concerns and tailoring AI implementation to accommodate diverse backgrounds and experiences, ensuring equitable and effective use of AI in mammography screenings.

Implications for AI Integration in Healthcare

The integration of AI as a second reader in healthcare diagnostics presents both opportunities and challenges. While the majority of patients support its use, concerns about data privacy and algorithmic bias must be addressed to ensure trust and acceptance.

Demographic variations in trust levels highlight the need for targeted strategies. Patients with higher education or familiarity with AI are more accepting, whereas Hispanic and Black respondents exhibit greater skepticism towards potential biases and privacy issues. Addressing these community-specific concerns is crucial for fostering broader acceptance of AI technologies.

Personal medical history significantly influences patient perceptions of AI. Those with a family history of breast cancer demonstrate varying levels of confidence in AI compared to radiologists, underscoring the importance of personalized approaches when integrating AI tools into clinical settings.

To effectively implement AI, healthcare providers must prioritize transparent communication and equitable design. Understanding diverse patient backgrounds is essential for ensuring that AI applications are both accepted and effective across different demographic groups. Addressing privacy concerns, particularly in more skeptical communities, will be key to successful integration.