A Bibliometric Analysis of Research Hotspots in the Field of Artificial Intelligence and Medical Diagnosis

Li Yueping

School of Management Science and Engineering, Anhui University of Finance & Economics

Abstract：

Currently, research on artificial intelligence and medical diagnosis is advancing rapidly. However, most existing studies focus on isolated scenarios such as specific diseases or particular technologies, lacking a systematic overview of the field, which makes it difficult to grasp the overall research landscape and development trends. To address this gap, this paper, based on relevant literature from the Web of Science database spanning 2023–2025, employs bibliometric methods to analyze research hotspots in this field from three dimensions: journal distribution, document co‑citation, and keyword co‑occurrence. The results indicate that the field exhibits significant interdisciplinary characteristics, with a large number of publications appearing in high‑impact international journals. The core knowledge is rooted in deep learning and computer vision, facilitating a deep integration of clinical medicine and artificial intelligence technologies. Four major research directions emerge: technology‑enabled diagnosis, healthcare system optimization, trustworthy AI exploration, and specialized precision applications. At present, the research focus has shifted from algorithmic iteration to clinical value extraction; however, technical deployment still faces bottlenecks such as explainability, algorithmic fairness, and ethical norms. Future efforts should deepen interdisciplinary collaboration, promote specialty‑specific translation, and improve governance for trustworthy AI.

Key Words：

Artificial Intelligence; medical diagnosis; bibliometric analysis; visualization research