Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients’ attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients’ perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists’ expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

PURPOSE: Artificial intelligence (AI) is fast becoming a vital part of health care, dramatically affecting physicians' workflows and patients' outcomes. Understanding patients' opinions on its use is thus essential to ensure its successful adoption. This study aims to evaluate public perceptions of AI in health care and explore patient feedback through a survey. METHODS: From January 2023 to June 2024, a survey on AI in health care was distributed to the public via a QR code shared through social media, posters, and videos, reaching 454 participants, of whom 240 completed the survey. Adapted from a validated 2020 model by Esmaeilzadeh et al, the survey underwent careful translation and cultural adjustments for the Italian population, including forward-backward translation and pilot testing. The survey assessed topics like willingness to use AI, performance anxiety, liability concerns, privacy issues, and its effect on doctor-patient communication. Responses were scored, with lower scores indicating greater acceptance of AI. RESULTS: The survey showed that 96% supported AI as a tool to assist radiologists and 92% were open to using AI for diagnostics and treatments. Concerns included reliability (61%) and reduced personal interaction (58%). Seventy-two percent trusted AI with data privacy. Overall, 90.4% viewed AI positively. CONCLUSION: The study highlights a balanced perspective on AI in health care. While recognizing its potential to enhance diagnostics and treatments, participants raised concerns about reliability, accountability, and interpersonal impacts. Most supported AI as a tool to complement, not replace, human expertise, emphasizing the need for transparent, reliable systems.