Patients’ Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review

Author:

Pesapane Filippo1ORCID,Giambersio Emilia2ORCID,Capetti Benedetta3ORCID,Monzani Dario34ORCID,Grasso Roberto35ORCID,Nicosia Luca1ORCID,Rotili Anna1ORCID,Sorce Adriana2,Meneghetti Lorenza1,Carriero Serena6ORCID,Santicchia Sonia6,Carrafiello Gianpaolo56,Pravettoni Gabriella35,Cassano Enrico1ORCID

Affiliation:

1. Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy

2. Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy

3. Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology, IRCCS, 20141 Milan, Italy

4. Department of Psychology, Educational Science and Human Movement (SPPEFF), University of Palermo, 90133 Palermo, Italy

5. Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy

6. Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy

Abstract

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.

Publisher

MDPI AG

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