Special Issue "Application of Artificial Intelligence in Medical Assisted Decision System"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Pharmaceutical Processes".

Deadline for manuscript submissions: 20 August 2023 | Viewed by 746

Special Issue Editor

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Interests: medical laboratory technology based on machine vision and image processing; medical Internet of things and big data application; intelligent in vitro diagnostic instrument
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of artificial intelligence (AI) in many medical fields and specialities is starting to be introduced. Artificial intelligence, machine learning, natural language processing and deep learning enable medical professionals to quickly and accurately identify intelligent medical needs and solutions, and quickly make intelligent medical or business decisions based on data models. AI can analyze a large amount of data stored by smart medical applications in the form of images, clinical research trials, and medical claims. With the help of in-depth learning, data are analyzed and explained with the help of computer extended knowledge. The impact of these tools is enormous, and the use of AI is assisting many stakeholders in the field of intelligent medicine.

  1. Clinicians, researchers, or data management teams participating in clinical trials can speed up the process of searching for the confirmation of medical code, which is crucial for the development and completion of clinical research.
  2. Patients can personalize their health plans by connecting virtual agents with members interested in customized health solutions through conversational AI.
  3. Clinicians can predict or diagnose diseases faster by combining medical data to improve and customize patient care.

This Special Issue on the “Application of Artificial Intelligence in Medical Assisted Decision System” aims to introduce the application of AI technology in the medical field. The adoption of AI in smart medicine still faces challenges. Topics include but are not limited to:

  • Intelligent recognition and analysis of medical images;
  • Intelligent auxiliary diagnosis of disease;
  • Medical robot;
  • Research and development of drug intelligence;
  • Intelligent health management;
  • Unstructured medical data analysis;
  • Intelligent discovery and development of genetic medicine.

Prof. Dr. Lei Wang
Guest Editor

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. Processes is an international peer-reviewed open access monthly 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 2000 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.


  • artificial intelligence
  • medical care
  • auxiliary diagnosis
  • data analysis
  • decision-making system
  • medical imaging
  • medical robot

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


GCCSwin-UNet: Global Context and Cross-Shaped Windows Vision Transformer Network for Polyp Segmentation
Processes 2023, 11(4), 1035; https://doi.org/10.3390/pr11041035 - 29 Mar 2023
Viewed by 483
Accurate polyp segmentation is of great importance for the diagnosis and treatment of colon cancer. Convolutional neural networks (CNNs) have made significant strides in the processing of medical images in recent years. The limited structure of convolutional operations prevents CNNs from learning adequately [...] Read more.
Accurate polyp segmentation is of great importance for the diagnosis and treatment of colon cancer. Convolutional neural networks (CNNs) have made significant strides in the processing of medical images in recent years. The limited structure of convolutional operations prevents CNNs from learning adequately about global and long-range semantic information interactions, despite the remarkable performance they have attained. Therefore, the GCCSwin-UNet framework is suggested in this study. Specifically, the model utilizes an encoder–decoder structure, using the patch-embedding layer for feature downsampling and the CSwin Transformer block as the encoder for contextual feature extraction. To restore the feature map’s spatial resolution during upsampling operations, a symmetric decoder and patch expansion layer are also created. In order to help the backbone module to do better feature learning, we also create a global context module (GCM) and a local position-enhanced module (LPEM). We conducted extensive experiments on the Kvasir-SEG and CVC-ClinicDB datasets, and compared them with existing methods. GCCSwin-UNet reached remarkable results with Dice and MIoU of 86.37% and 83.19% for Kvasir-SEG, respectively, and 91.26% and 84.65% for CVC-ClinicDB, respectively. Finally, quantitative analysis and statistical tests are applied to further demonstrate the validity and plausibility of our method. Full article
Show Figures

Figure 1

Back to TopTop