Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 1042
Special Issue Editors
Interests: computational simulation of the cardiovascular system; AI-assisted diagnosis; medical data analysis; wearable sensors
Special Issues, Collections and Topics in MDPI journals
Interests: healthcare data; machine learning; deep learning; signal processing; image processing
Special Issues, Collections and Topics in MDPI journals
Interests: computational simulation of photodetectors; graphene-based photodetectors; machine learning; antennas; wearable sensors; nanowires; deep learning; signal processing; IoT
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep learning
- artificial intelligence (AI)
- internet of medical things (IoMT)
- AI-assisted diagnostics
- multimodal clinical data
- data-driven healthcare
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