理解动物到人类药物开发证据：利益相关者的实践、挑战及对AI工具的需求
Making Sense of Animal-to-Human Drug Development Evidence: Stakeholder Practices, Challenges, and Requirements for AI Tools
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中文总结 AI 辅助
本研究通过访谈13位利益相关者，揭示动物到人类药物开发证据评估中的实践与挑战，提出支持定位、筛选和提取证据并强调透明度与人工监督的角色敏感型AI工具设计启示。
中文摘要 AI 辅助
动物模型被广泛用于研究人类生物学和健康干预措施，然而将动物研究结果转化到人类仍然具有挑战性。临床前和临床研究中的证据为实验和转化决策提供信息。人工智能（AI）工具正日益重塑这些证据的搜索、综合和使用方式，但目前尚不清楚它们应如何支持参与评估动物到人类证据的多样化利益相关者。我们对13位利益相关者进行了半结构化访谈，以考察他们的证据实践、挑战以及对AI支持的期望。我们发现，利益相关者以不同的目标、专业知识和启发式方法对待同一不完整的证据基础。参与者特别看重AI在定位、筛选和提取证据方面的能力，但对自动化的解释和质量判断更为谨慎。他们强调透明度、来源可追溯性、不确定性沟通和人工监督。基于这些发现，我们得出了对角色敏感的AI工具的设计启示，这些工具支持对动物到人类转化进行更系统和透明的推理。
英文摘要
Animal models are widely used to study human biology and health interventions, yet translating findings from animal studies to humans remains challenging. Evidence across preclinical and clinical research informs experimental and translational decisions. Artificial Intelligence (AI) tools are increasingly reshaping how this evidence is searched, synthesized, and used, but it remains unclear how they should support the diverse stakeholders involved in assessing animal-to-human evidence. We conducted semi-structured interviews with 13 stakeholders to examine their evidence practices, challenges, and expectations for AI support. We found that stakeholders approach the same incomplete evidence base with different goals, expertise, and heuristics. Participants valued AI particularly for locating, screening, and extracting evidence, but were more cautious about automated interpretation and quality judgments. They emphasized transparency, source traceability, uncertainty communication, and human oversight. Based on these findings, we derive design implications for role-sensitive AI tools that support more systematic and transparent reasoning about animal-to-human translation.
发表机构
- University of Bern（伯尔尼大学）
机构由 AI 辅助整理，请以论文原文为准。
