Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions

Dear Colleagues,

In recent years, we have witnessed a rapid growth in generative artificial intelligence (AI) technologies and their clinical applications. Large language models (LLMs) have been applied in different aspects of modern medical science, including medical education, bibliographic analysis, pharmacological analysis, construction of knowledge maps, and simulated diagnosis. In the meantime, the development of electronic health records (EHRs), radiomics, wearable sensors, wireless communication, cloud computing, and advanced data fusion algorithms are generating more AI models based on multimodal data for the diagnosis and monitoring of diseases in the context of the Internet of Medical Things (IoMT). These advanced AI-enhanced technologies are reshaping the landscape of modern diagnosis, enabling the early screening of accurate diagnosis of acute and chronic diseases. Meanwhile, data protection, privacy, and other ethics issues are emerging in this new era, with efforts in regulatory and technical aspects, including cryptography, biometrics, watermarking, and Blockchain-based security techniques.

The aim of this Special Issue entitled “Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions” is to share information on cutting-edge AI technologies and multimodal medical data analysis. The scope of this Special Issue will include studies on LLMs, AI-enhanced multimodal medical data analysis, IoMT, as well as data security in AI-enhanced diagnostics.

Dr. Haipeng Liu
Dr. Rajesh K. Tripathy
Dr. Shonak Bansal
Dr. Prince Jain
Guest Editors

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