Guidelines for optimal cancer screening in older adults remain unclear, particularly for adults over the age of 75. While cancer screening in older adults may benefit some in good health, it may cause unnecessary burdens in others with limited life expectancy. Thus, a systematic approach to enable individualized cancer screening decisions in older adults is needed. We suggest a framework that guides such decisions through evidence-based approaches from multiple interactions, and that involves the patient, clinician, and healthcare system. An individualized approach considers differences in disease risk rather than the chronological age of the patient. This paper presents a comprehensive framework that depicts the independent and converging levels of influences on individualized cancer screening decisions in older adults. This Individualized Decisions for Screening (IDS) framework recognizes the reality of these interrelationships, including the tensions that arise when behaviors and outcomes are valued differently at the patient, clinician, and healthcare organization levels. Person-centered approaches are essential to advancing multilevel research of individualized cancer screening decisions among older adults.

Text-to-image artificial intelligence (AI) programs are popular public-facing tools that generate novel images based on user prompts. Given that they are trained from Internet data, they may reflect societal biases, as has been shown for text-to-text large language model programs. We sought to investigate whether 3 common text-to-image AI systems recapitulated stereotypes held about surgeons and other health care professionals. All platforms queried were able to reproduce common aspects of the profession including attire, equipment, and background settings, but there were differences between programs most notably regarding visible race and gender diversity. Thus, historical stereotypes of surgeons may be reinforced by the publics use of text-to-image AI systems, particularly those without procedures to regulate generated output. As AI systems become more ubiquitous, understanding the implications of their use in health care and for health care-adjacent purposes is critical to advocate for and preserve the core values and goals of our profession.