Futures

UC Davis Health Leads $16 Million Study on AI in Mammogram Interpretation for Breast Cancer Detection, (from page 20251012d.)

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Summary

UC Davis Health is co-leading a $16 million national study, the PRISM trial, to assess the effectiveness of Artificial Intelligence (AI) in interpreting mammograms to improve accuracy and patient experience. This large-scale randomized trial aims to enhance breast cancer detection while minimizing unnecessary callbacks and anxiety for patients. Conducted across several states, the trial will evaluate whether AI can assist radiologists without replacing them. Results will influence clinical practices and patient communications, with a focus on collecting data, analyzing cancer detection rates, and understanding patient and radiologist perceptions of AI-assisted care. This initiative combines efforts from leading academic centers and emphasizes a collaborative approach to AI research with patient perspectives prioritized.

Signals

name description change 10-year driving-force relevancy
AI in Mammogram Interpretation AI aiding radiologists in reading mammograms for better accuracy. Shifting from traditional mammogram interpretation to AI-assisted methods. AI could lead to standardized and enhanced mammogram readings across healthcare settings. The need for improved accuracy in breast cancer detection and reduced anxiety for patients. 4
Collaborative AI Research Partnerships among various institutions to study AI’s effectiveness in mammograms. Transitioning towards collaborative, multi-center studies in medical AI research. Increased interdisciplinary collaboration could lead to breakthroughs in AI applications in healthcare. The drive for more comprehensive and trustworthy evidence on AI’s impact in health settings. 3
Patient-Centric AI Evaluation Involving patient feedback in assessing AI’s role in mammography. Moving from radiologist-centric evaluations to incorporating patient perspectives in AI assessments. Patient engagement could redefine how AI tools are developed and assessed in clinical settings. Recognizing the importance of patient experiences and trust in AI applications in healthcare. 4
National Scale Trials for AI Conducting the first large-scale trial evaluating AI in mammography. From isolated studies to large-scale national research on AI’s medical effectiveness. Establishing a precedent for extensive nationwide trials assessing AI in various health domains. The increasing need to validate AI tools with robust, evidence-based trials for clinical care. 5
Awareness and Education on AI in Healthcare Focus on educating stakeholders about AI’s role in breast cancer screenings. Shifting communication from traditional methods to including AI literacy for stakeholders. Enhanced understanding of AI in healthcare could lead to better acceptance and integration of AI solutions. The demand for transparency and clarity around AI technologies in health care industries. 4

Concerns

name description
Reliability of AI in Mammogram Interpretation Concerns about AI’s effectiveness in accurately interpreting mammograms and the potential for misdiagnosis.
Impact on Patient Anxiety AI could potentially reduce or increase anxiety for patients depending on its accuracy and the communication surrounding it.
Human-AI Collaboration in Medicine The challenge of integrating AI into existing medical workflows without undermining radiologist expertise.
Long-term Effects of AI in Clinical Settings Unclear long-term impacts of AI on clinical practices and patient outcomes as AI technologies evolve.
Trust in AI by Patients and Clinicians Concerns regarding how much trust patients and healthcare professionals will place in AI-assisted diagnostics.
False Positives and Unnecessary Procedures Potential for AI to either reduce or inadvertently increase false positives and unnecessary follow-up tests.
Ethical Implications of AI in Healthcare Ethical considerations arising from reliance on AI technology for critical health decisions.

Behaviors

name description
AI-assisted mammogram interpretation Using AI tools to support radiologists in reading mammograms aims to improve accuracy and efficiency in breast cancer screenings.
Patient-centric research approach Integrating patient advocates in clinical trials emphasizes the importance of patient perspectives in evaluating AI effectiveness.
Collaboration among institutions Bringing together multiple academic medical centers reflects a collaborative effort to enhance research quality and resource sharing in healthcare.
Focus on real-world effectiveness Conducting randomized trials to measure AI’s performance in actual clinical settings highlights a shift towards evidence-based practices.
Enhanced communication about AI in healthcare Evaluating how to effectively communicate AI’s role in mammography screening to patients underlines the need for transparency and trust.
Comprehensive outcomes analysis Incorporating patient and radiologist surveys along with clinical data to assess perceptions of AI-assisted care represents a holistic evaluation of technology’s impact.

Technologies

name description
Artificial Intelligence (AI) in Mammography AI is being used to assist radiologists in interpreting mammograms, aiming to improve accuracy and patient outcomes in breast cancer screenings.
AI-assisted Interpretation A collaborative approach where AI tools support radiologists in reading mammograms, potentially enhancing the detection of breast cancer.
Real-world Effectiveness Trials for AI Conducting large-scale randomized trials to evaluate the real-world effectiveness of AI in healthcare applications, particularly in screening.
Patient-centered AI Research Engaging patients, clinicians, and stakeholders in research to generate trustworthy evidence on AI’s role in healthcare.
Collaborative Healthcare Innovations Bringing together academic medical centers to improve cancer screening practices through collaborative research efforts.

Issues

name description
AI in Medical Imaging The effectiveness of AI in assisting radiologists with mammogram interpretations is being rigorously evaluated through the PRISM trial.
Impact on Patient Experience The trial seeks to understand how AI affects patient perceptions and trust in care during mammography screenings.
Collaborative AI Research Models The study showcases a collaborative approach, integrating patient advocates and policymakers in AI research.
Cancer Screening Technology Exploration of AI’s role in enhancing mammography accuracy could influence future cancer detection technologies.
Patient-Centered Outcomes Emphasis on generating independent evidence with the patient perspective central to decision-making in AI-assisted care.