Alimenté par : Claudia (ADFI Alsace), Gaëlle (ADFI Alsace), Isabelle
Cet outil s'appuie sur PubMind
Un accès direct à la littérature scientifique via la base PubMed permettant de faciliter la veille sur les enjeux complexes de la santé mentale et du fait religieux : de la neuroscience des croyances à l'étude des abus spirituels, en passant par la prise en charge des traumatismes et des processus de déconversion.
Dernière synchronisation le 07/06/2026
J Pathol Inform . 2025;19 :100526
The rise of artificial intelligence (AI)-driven tools like ChatGPT is transforming professional fields, including pathology. This study provides early insights into how pathology trainees and practicing pathologists are integrating AI into their training and clinical practice. To assess adoption, usage patterns, perceptions, and challenges related to AI-driven tools, including large language models and vision-language models, among pathology professionals. The study also explores future directions for AI integration. A cross-sectional, anonymous survey was distributed electronically to pathology residents, fellows, and attending pathologists through the Accreditation Council for Graduate Medical Education program director registry, professional organizations, and social media (X, Reddit, LinkedIn, and email listserv). The survey included multiple-choice, Likert-scale, and open-ended questions on AI familiarity, usage, perceived benefits/risks, and institutional policies. Data were analyzed using descriptive and inferential statistics, with qualitative responses categorized thematically. A total of 268 respondents participated, primarily residents (41%), attendings (39%), and fellows (7%), representing 23 countries (65% from the USA). Most were affiliated with academic medical centers (72%) and aged 25-44. Whereas 73% reported some familiarity with AI, actual use was limited, 31% reported rare use and 29% no use at all, especially among residents and attendings. ChatGPT was the most used tool (84%), applied mainly for document drafting (57%), research (54%), and administrative tasks (34%). Diagnostic use was minimal. Top concerns included accuracy (81%), over-reliance (65%), and data security (63%). Only 10% reported having clear institutional AI guidelines. Familiarity was strongly associated with usage frequency (