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Bioengineering
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Applications of Large Language Models in Medical and Biomedical Data...
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Applications of Large Language Models in Medical and Biomedical Data Processing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 8652

Special Issue Editor

Dr. Ramin Ranjbarzadeh

E-Mail Website
Guest Editor
School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland
Interests: deep learning; machine learning; large language models; medical data processing; biomedical data processing; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large language models, such as GPT-3.5, have shown tremendous potential in natural language processing and have emerged as a valuable tool for medical and biomedical data processing. This Special Issue aims to explore the diverse applications of large language models in these domains, highlighting their impact, methodologies, and potential for advancing healthcare and biomedical research. The proposed Special Issue will feature research articles, case studies, and review papers that delve into the application of large language models in medical and biomedical data processing. We invite researchers, practitioners, and industry experts to contribute their original work, focusing on the innovative use of these models in areas such as clinical text analysis, electronic health records (EHR) processing, medical image analysis, biomedical literature mining, drug discovery, patient monitoring, and personalized medicine.

The Special Issue will provide a platform to showcase the latest advancements, discuss challenges and opportunities, and share best practices in leveraging large language models to enhance medical diagnosis, treatment, and healthcare decision making. This Special Issue is expected to attract a wide range of readers, including researchers, clinicians, data scientists, and healthcare professionals, who are interested in the intersection of natural language processing and medicine. By bringing together cutting-edge research and practical applications, this Special Issue will contribute to disseminating knowledge and foster collaborations between the medical and natural language processing communities.

Potential topics include, but are not limited to, the following:

  • Deep learning;
  • Machine learning;
  • Large language models;
  • Medical data processing.

Dr. Ramin Ranjbarzadeh
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. Bioengineering 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 2700 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
  • machine learning
  • large language models
  • medical data processing
  • biomedical data processing
  • natural language processing
  • clinical text analysis,
  • electronic health records
  • medical image analysis
  • biomedical literature mining
  • drug discovery
  • patient monitoring
  • personalized medicine
  • healthcare decision-making
  • data analytics
  • artificial intelligence
  • healthcare applications
  • healthcare informatics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 3111 KiB  
Article
IDCC-SAM: A Zero-Shot Approach for Cell Counting in Immunocytochemistry Dataset Using the Segment Anything Model
by Samuel Fanijo, Ali Jannesari and Julie Dickerson
Bioengineering 2025, 12(2), 184; https://doi.org/10.3390/bioengineering12020184 - 14 Feb 2025
Viewed by 858
Abstract
Cell counting in immunocytochemistry is vital for biomedical research, supporting the diagnosis and treatment of diseases such as neurological disorders, autoimmune conditions, and cancer. However, traditional counting methods are manual, time-consuming, and error-prone, while deep learning solutions require costly labeled datasets, limiting scalability. [...] Read more.
Cell counting in immunocytochemistry is vital for biomedical research, supporting the diagnosis and treatment of diseases such as neurological disorders, autoimmune conditions, and cancer. However, traditional counting methods are manual, time-consuming, and error-prone, while deep learning solutions require costly labeled datasets, limiting scalability. We introduce the Immunocytochemistry Dataset Cell Counting with Segment Anything Model (IDCC-SAM), a novel application of the Segment Anything Model (SAM), designed to adapt the model for zero-shot-based cell counting in fluorescent microscopic immunocytochemistry datasets. IDCC-SAM leverages Meta AI’s SAM, pre-trained on 11 million images, to eliminate the need for annotations, enhancing scalability and efficiency. Evaluated on three public datasets (IDCIA, ADC, and VGG), IDCC-SAM achieved the lowest Mean Absolute Error (26, 28, 52) on VGG and ADC and the highest Acceptable Absolute Error (28%, 26%, 33%) across all datasets, outperforming state-of-the-art supervised models like U-Net and Mask R-CNN, as well as zero-shot benchmarks like NP-SAM and SAM4Organoid. These results demonstrate IDCC-SAM’s potential to improve cell-counting accuracy while reducing reliance on specialized models and manual annotations. Full article
(This article belongs to the Special Issue Applications of Large Language Models in Medical and Biomedical Data Processing)
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24 pages, 13091 KiB  
Article
EfficientUNetViT: Efficient Breast Tumor Segmentation Utilizing UNet Architecture and Pretrained Vision Transformer
by Shokofeh Anari, Gabriel Gomes de Oliveira, Ramin Ranjbarzadeh, Angela Maria Alves, Gabriel Caumo Vaz and Malika Bendechache
Bioengineering 2024, 11(9), 945; https://doi.org/10.3390/bioengineering11090945 - 21 Sep 2024
Cited by 4 | Viewed by 2618
Abstract
This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet framework. The UNet architecture, commonly employed for biomedical image segmentation, is further enhanced with depthwise separable convolutional [...] Read more.
This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet framework. The UNet architecture, commonly employed for biomedical image segmentation, is further enhanced with depthwise separable convolutional blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The ViT, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our UNet model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model’s ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient UNet topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation. Full article
(This article belongs to the Special Issue Applications of Large Language Models in Medical and Biomedical Data Processing)
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16 pages, 1626 KiB  
Article
ChatGPT in Occupational Medicine: A Comparative Study with Human Experts
by Martina Padovan, Bianca Cosci, Armando Petillo, Gianluca Nerli, Francesco Porciatti, Sergio Scarinci, Francesco Carlucci, Letizia Dell’Amico, Niccolò Meliani, Gabriele Necciari, Vincenzo Carmelo Lucisano, Riccardo Marino, Rudy Foddis and Alessandro Palla
Bioengineering 2024, 11(1), 57; https://doi.org/10.3390/bioengineering11010057 - 6 Jan 2024
Cited by 8 | Viewed by 3630
Abstract
The objective of this study is to evaluate ChatGPT’s accuracy and reliability in answering complex medical questions related to occupational health and explore the implications and limitations of AI in occupational health medicine. The study also provides recommendations for future research in this [...] Read more.
The objective of this study is to evaluate ChatGPT’s accuracy and reliability in answering complex medical questions related to occupational health and explore the implications and limitations of AI in occupational health medicine. The study also provides recommendations for future research in this area and informs decision-makers about AI’s impact on healthcare. A group of physicians was enlisted to create a dataset of questions and answers on Italian occupational medicine legislation. The physicians were divided into two teams, and each team member was assigned a different subject area. ChatGPT was used to generate answers for each question, with/without legislative context. The two teams then evaluated human and AI-generated answers blind, with each group reviewing the other group’s work. Occupational physicians outperformed ChatGPT in generating accurate questions on a 5-point Likert score, while the answers provided by ChatGPT with access to legislative texts were comparable to those of professional doctors. Still, we found that users tend to prefer answers generated by humans, indicating that while ChatGPT is useful, users still value the opinions of occupational medicine professionals. Full article
(This article belongs to the Special Issue Applications of Large Language Models in Medical and Biomedical Data Processing)
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