Update on Applications in Medical Imaging and Natural Language Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 253
Special Issue Editor
Interests: image (natural & medical) processing; machine learning (ML); deep learning (DL); video processing
Special Issue Information
Dear Colleagues,
In healthcare, the conjunction of Medical Imaging (MI) and Natural Language Processing (NLP) is showing transformative progress. The MI driven by artificial intelligence (AI) advancement is transfiguring diagnosis and treatment. The accuracy and interpretation speed of an image is enhanced by robust AI algorithms aiding physicians in the early detection of diseases and accurate treatment planning. Deep learning approaches are optimizing techniques like CT scans, ultrasound, and MRI leading to a refined and subtle understanding of patient conditions. Furthermore, NLP algorithms can diagnose and extract useful understandings from extensive amounts of clinical text data, automating tasks such as clinical documentation, electronic health record analysis, and medical transcription. However, there are still research gaps in terms of NLP applications in the area of MI, which are as follows: (1) understanding of convoluted semantics and inherited context in medical texts that can enhance the precision and applicability of NLP applications in MI; (2) development of NLP approaches adept at quick examination and understanding of clinical texts leading to real-time decision support; (3) addressing challenges such as disparity during the integration of MI data and textual clinical data; and (4) addressing the privacy and security concerns with MI data of patients in the NLP implementations, etc. This Special Issue will focus on the latest advancements of NLP to MI applications in terms of new approaches and concerns related to NLP applying to MI data.
Dr. Tariq Rahim
Guest Editor
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Keywords
- medical imaging
- natural language processing
- deep learning
- medical diagnosis
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