인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Artificial Intelligence (AI) holds promise for advancing the field of neurotechnology and accelerating its clinical translation. AI-driven clinical neurotechnologies leverage the power of non-linear algorithms to analyze complex brain data and enable adaptive, closed-loop neurostimulation. Despite these promises, the integration of AI into clinical practice remains limited, with lack of explainability being commonly cited as one main obstacle. This raises the question of whether opacity and lack of explainability also hinder the adoption of AI in closed-loop medical neurotechnologies. We investigated the attitudes, informational needs and preferences of clinicians regarding AI-driven closed-loop neurotechnologies and explored what forms of explanation they consider necessary for clinical use. We conducted semi-structured expert interviews with twenty clinicians (including neurologists, neurosurgeons, and psychiatrists) from Germany and Switzerland. Using reflexive thematic analysis, we explored their understanding of and expectations for explainability in the context of AI-driven closed-loop neurotechnology systems. Clinicians consistently emphasized the importance of context-sensitive, clinically meaningful forms of explainability such as understanding what input data were used to train the system and how the output relates to clinically relevant outcomes. By contrast, detailed knowledge of the model's inner architecture or technical mechanics were of limited interest. Several participants specifically called for Explainable AI (XAI) techniques, particularly feature importance and relevance measures, to support their interpretation of system outputs. Our findings suggest that the clinical utility of AI-driven neurotechnologies can be improved by focusing on intuitive, user-centered and clinically meaningful forms of explainability rather than full algorithmic transparency. Designing systems that meet these pragmatic needs may help bridge the translational gap between AI development and clinical implementation.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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