Survey Shows Cautious Patient Support for AI in Mammography Screening, Prefer AI as Second Reader

A large survey conducted among a diverse patient population by researchers at the University of Texas Southwestern Medical Center has shed light on patient attitudes toward the use of artificial intelligence (AI) in screening mammography. The study, published in *Radiology: Imaging Cancer*, reveals that while patients are generally supportive of AI as a tool, they strongly prefer it to be used in conjunction with a radiologist's review rather than as a standalone diagnostic method. ### Key Survey Findings The survey, which was open for seven months in 2023, included 518 patients who had undergone breast cancer screening mammograms. It consisted of 29 closed-ended questions designed to assess patients' knowledge and perceptions of AI. Notably, 71% of respondents favored AI's use as a second reader, reinforcing the idea that patients see AI as a supplementary tool rather than a replacement for human expertise. ### Factors Influencing Patient Trust The survey uncovered several factors that influence patient trust and acceptance of AI: 1. **Education and AI Knowledge**: Patients with a college degree or higher and those who reported a higher level of AI knowledge were twice as likely to accept AI involvement in their mammographic screening. This suggests that education and familiarity with AI play significant roles in shaping patient perceptions. 2. **Racial and Ethnic Background**: Hispanic and non-Hispanic Black respondents expressed significantly higher concerns about AI bias and data privacy. This disparity in acceptance rates highlights the need for more inclusive and transparent AI systems that address these concerns. 3. **Personal and Familial Medical History**: Patients with a family history of breast cancer were more likely to request additional reviews, regardless of whether the initial abnormality was detected by AI or a radiologist. They generally trusted both methods equally when the mammogram results were normal. Conversely, patients with a history of abnormal mammograms were more likely to pursue further diagnostic follow-up if there was a conflict between AI and radiologist reviews, especially if AI flagged an abnormality. ### Implications and Recommendations The study underscores the importance of considering patient perspectives in the integration of AI into medical imaging. Trust and acceptance are critical for maintaining patient adherence to screening programs and ensuring that AI enhances rather than hinders patient care. ### Patient-Centric AI Integration Dr. Basak E. Dogan, the lead author and clinical professor of radiology at the University of Texas Southwestern Medical Center, emphasized the need for personalized AI strategies in mammographic screening. "Incorporating patient perspectives into AI implementation strategies ensures that these technologies improve and not hinder patient care, fostering trust and adherence to imaging reports and recommendations," she noted. ### Ongoing Engagement The researchers stressed the need for continuous engagement with patients to understand their evolving views as AI technology advances. This engagement is crucial for addressing concerns and building trust. By involving patients in the decision-making process and providing clear, transparent communication about how AI systems work, healthcare providers can help mitigate fears and ensure that AI is seen as a valuable tool in medical imaging. ### Industry Insights and Company Profile Industry insiders echo the findings, highlighting the importance of balanced AI integration. They stress that while AI can significantly improve diagnostic accuracy and efficiency, it is essential to address patient concerns through education and transparency. The University of Texas Southwestern Medical Center is known for its pioneering research in medical imaging and its commitment to patient-centered care. This study is a clear example of how the institution is working to advance both technological and ethical standards in the field. The study's findings are particularly relevant as AI adoption in healthcare continues to grow. By understanding and addressing the factors that influence patient trust, healthcare providers and technology developers can work together to create more effective and accepted AI solutions for mammographic screening and beyond.