Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

中文翻译：

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医疗诊断从特定任务人工智能到通用人工智能的路径：文献计量分析


人工智能（AI）已经彻底改变了许多领域，其在医疗保健领域的潜力日益得到认可。基于影像、实验室检测、病历、电生理数据等多种数据源的诊断人工智能近年来发展迅速。全面了解人工智能在医疗诊断应用中的发展现状、影响因素及其相互关系，对于进一步推动其在临床实践中的应用至关重要。在这项研究中，我们进行了文献计量分析，以探索用于医疗诊断的特定任务到通用人工智能的演变。我们使用Web of Science数据库搜索2010年至2023年间发表的相关文章，并应用VOSviewer、R包Bibliometrix和CiteSpace来分析协作网络和关键词。我们的分析表明，医疗诊断中的人工智能领域近年来经历了快速增长，重点是图像分析、疾病预测和决策支持等任务。研究人员和机构之间的合作网络表明了该领域全球合作的趋势。此外，我们还确定了有助于医疗诊断领域人工智能发展的几个关键因素，包括数据质量、算法设计和计算能力。该领域取得进展的挑战包括模型的可解释性、稳健性和平等性，这需要多利益相关者、跨学科合作来解决。我们的研究提供了对从特定任务、单模态人工智能到医疗诊断通用、多模态人工智能的路径的全面理解。 随着AI技术的不断完善和医疗数据的积累，我们相信未来AI将在医疗诊断中发挥更大的作用。