标题： 超越黑盒AI：用于痴呆症护理的可解释混合系统 显示英文标题

标题： Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care

摘要： 大型语言模型（LLMs）的近期爆发重新点燃了人工智能（AI）系统可能有助于医学诊断的希望。 然而，尽管基准测试成绩令人眼花缭乱，LLM助手尚未在病床边带来可衡量的改进。 这项范围综述旨在突出AI在临床环境中有限的实际贡献领域，特别是在痴呆症诊断和护理方面。 独立的机器学习模型在模式识别方面表现出色，但很少提供可操作的、可解释的指导，从而削弱了临床医生的信任。 医生相邻使用LLM并未导致诊断准确性或速度的提高。 关键限制源于数据驱动范式：缺乏透明度的黑箱输出，容易出现幻觉，以及薄弱的因果推理。 结合统计学习与专家规则知识的混合方法，并在整个过程中涉及临床医生，有助于恢复可解释性。 它们也更符合现有的临床工作流程，如PEIRS和ATHENA-CDS等例子所示。 未来的决策支持应通过将预测与临床上有意义的原因联系起来，优先考虑解释的一致性。 这可以通过神经符号或混合AI实现，将LLM的语言能力与人类因果专业知识相结合。 AI研究人员已经解决了这一方向，可解释AI和神经符号AI是进一步推进AI的下一步逻辑步骤。 然而，它们仍然基于数据驱动的知识整合，而不是人机协同的方法。 未来的研究应不仅通过准确性来衡量成功，还要通过临床医生理解的改善、工作流程的契合度和患者结果的改善来衡量。 对于AI系统成为临床实践的一部分，迫切需要更好地理解什么有助于改善人机交互。 显示英文摘要

摘要： The recent boom of large language models (LLMs) has re-ignited the hope that artificial intelligence (AI) systems could aid medical diagnosis. Yet despite dazzling benchmark scores, LLM assistants have yet to deliver measurable improvements at the bedside. This scoping review aims to highlight the areas where AI is limited to make practical contributions in the clinical setting, specifically in dementia diagnosis and care. Standalone machine-learning models excel at pattern recognition but seldom provide actionable, interpretable guidance, eroding clinician trust. Adjacent use of LLMs by physicians did not result in better diagnostic accuracy or speed. Key limitations trace to the data-driven paradigm: black-box outputs which lack transparency, vulnerability to hallucinations, and weak causal reasoning. Hybrid approaches that combine statistical learning with expert rule-based knowledge, and involve clinicians throughout the process help bring back interpretability. They also fit better with existing clinical workflows, as seen in examples like PEIRS and ATHENA-CDS. Future decision-support should prioritise explanatory coherence by linking predictions to clinically meaningful causes. This can be done through neuro-symbolic or hybrid AI that combines the language ability of LLMs with human causal expertise. AI researchers have addressed this direction, with explainable AI and neuro-symbolic AI being the next logical steps in further advancement in AI. However, they are still based on data-driven knowledge integration instead of human-in-the-loop approaches. Future research should measure success not only by accuracy but by improvements in clinician understanding, workflow fit, and patient outcomes. A better understanding of what helps improve human-computer interactions is greatly needed for AI systems to become part of clinical practice.