Identification of Globally Leading Researchers in the Field of AI Medical Devices

Abstract

This study aims to identify leading researchers in the field of AI medical devices using bibliometric methods. Papers related to AI medical devices were searched in the core collection of Web of Science, based on which the main research directions in this field were obtained through a term cluster analysis, and the leading researchers in each direction were identified according to the number of publications. About 80% of published papers in the field of AI medical devices focus on AI-assisted image analysis, and about 20% focus on AI-assisted physiological signal analysis. In the direction of AI-assisted image analysis, outstanding researchers include U. Rajendra Acharya, Tian Jie, Zheng Hairong, Wang Yuanyuan, Daniel Rueckert, etc., who apply AI technology to the analysis of various medical images; in addition, some researchers focus on applying AI to specific types of medical image analysis based on their own research expertise. For example, Yang XiaoFeng focuses on tumor radiotherapy, Shen Dinggang focuses on brain medical image analysis, and Daniel S. Berman focuses on cardiovascular image analysis. In the direction of AI-assisted physiological signals, outstanding researchers include Gao Xiaorong, Niels Birbaumer, Gernot R. Mueller-Putz, Tzyy-Ping Jung, Scott Makeig and Cuntai Guan, etc. Research in the direction of AI medical devices is active. Many researchers apply AI to various types of medical image analysis, some apply AI to specific types of medical image analysis based on their professional expertise, and some apply AI to the analysis of physiological signals such as EEG, ECG, and EMG.

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