临床试验管道揭示医疗保健领域人工智能的下一波浪潮：对8532项注册研究的多维分析
The Clinical Trial Pipeline Reveals the Next Wave of Artificial Intelligence in Healthcare: A Multidimensional Analysis of 8,532 Registered Studies
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中文总结 AI 辅助
该研究通过对8532项注册研究多维分析，揭示医学人工智能临床试验格局。用关键词搜索和分类器识别试验，从多维度分类，发现虽从回顾转向前瞻，但在规模、专业覆盖等方面有差距，未来进展取决于弥合这些差距。
中文摘要 AI 辅助
医学中人工智能的前瞻性临床评估迅速扩展，但全球人工智能临床试验格局仍未完全明确。我们通过广泛的关键词搜索和基于大语言模型的分类器，系统地识别了在此http URL上注册的人工智能相关试验。每个试验从七个维度进行分类：临床功能、数据模式、专业、人工智能集成与自主性、工作流程位置、转化成熟度和认知作用。我们识别出32个专业领域的8532项人工智能临床试验，80%于2019年以后注册，30.5%采用随机对照设计。基于成像的人工智能是最大的模式，有2475项试验（29%），而临床文本和自然语言处理试验在2018年至2025年间增长了七倍。预后人工智能（4324项试验）略超过诊断人工智能（3828项试验），表明从疾病检测向风险分层和轨迹预测的转变。治疗推荐仍不太发达，有768项试验（9%）。转化成熟度仍然有限：3259项试验（38%）是回顾性验证研究，1802项（21%）是无声前瞻性评估，这表明许多试验仍在产生算法而非临床证据。只有184项试验涉及4级半自主或闭环人工智能，其中68%专注于血糖管理。多模态人工智能占试验的33.6%，主要结合成像、组学、生理信号和可穿戴数据。这些发现表明临床人工智能已从回顾性开发转向前瞻性评估，但尚未进行有足够动力、具有地理代表性的长期结果试验。未来的进展将取决于弥合试验规模、专业覆盖、地理代表性和转化成熟度方面的差距。
英文摘要
The prospective clinical evaluation of artificial intelligence in medicine has expanded rapidly, but the global AI clinical trial landscape remains incompletely characterized. We systematically identified AI-related trials registered in ClinicalTrials.gov using a broad keyword search followed by an LLM-based classifier. Each trial was classified across seven dimensions: clinical function, data modality, specialty, AI integration and autonomy, workflow position, translational maturity, and epistemic role. We identified 8,532 AI clinical trials across 32 specialties, with 80% registered from 2019 onward and 30.5% using a randomized controlled design. Imaging-based AI was the largest modality, with 2,475 trials (29%), while clinical text and NLP trials increased seven-fold between 2018 and 2025. Prognostic AI (4,324 trials) slightly exceeded diagnostic AI (3,828 trials), suggesting a shift from disease detection toward risk stratification and trajectory prediction. Treatment recommendation remained less developed, with 768 trials (9%). Translational maturity remained limited: 3,259 trials (38%) were retrospective validation studies and 1,802 (21%) were silent prospective evaluations, indicating that much of the pipeline still produces algorithmic rather than clinical evidence. Only 184 trials involved Level 4 semi-autonomous or closed-loop AI, 68% of which focused on glucose management. Multimodal AI accounted for 33.6% of trials, mainly combining imaging, omics, physiological signals, and wearable data. These findings indicate that clinical AI has moved from retrospective development to prospective evaluation, but not yet to adequately powered, geographically representative, long-term outcome trials. Future progress will depend on closing gaps in trial scale, specialty coverage, geographic representation, and translational maturity.
