Special Session: Artificial Intelligence in Healthcare: Leveraging Transformer Models

Call for papers for IJCNN 2026 Special Session

Paper submission by January 31, 2026 via submission link 

Description

Artificial intelligence transformer-based models such as the BERT model (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), vision transformers (ViT) and many others have found significant applications in healthcare. These powerful models are based on the transformer architecture, which uses self-attention mechanisms to learn contextual relationships between input data. This makes them particularly well-suited for analysing complex and heterogeneous healthcare data, such as electronic health records (EHRs), medical images, and genomics data. One of the most promising applications of artificial intelligence transformer-based models in healthcare is in medical image analysis. Convolutional neural networks (CNNs) have been widely used for image analysis tasks, but they have limitations in processing large volumes of data due to the fixed-size receptive field. In contrast, transformer-based models can handle variable-sized inputs and learn more complex features from the image data. For example, transformer-based models have achieved state-of-the-art performance in tasks such as lesion segmentation, classification, and anomaly detection in medical imaging. Another important application of AI transformer-based models in healthcare is in natural language processing (NLP) tasks, particularly in analysing clinical notes and electronic health records (EHR). These documents contain large amounts of unstructured data, making it challenging to extract relevant information. However, transformer-based models can learn to extract semantic meaning from the text and make predictions based on that information. For instance, they can be used to predict patient outcomes or identify disease risk factors from EHRs. In addition to medical imaging and NLP, AI transformer-based models can also be used in genomics data analysis, drug discovery, and personalised medicine. Genomics data contains a large amount of variability, making it challenging to identify patterns and make accurate predictions. Transformer-based models can learn to capture complex relationships between genes and diseases, making it possible to identify new drug targets and develop personalised treatments. Furthermore, transformer-based models have been shown to be extremely effective in handling multi-modality data in healthcare applications, such as clinical text and image integration (e.g., Med-BERT, BioBert, etc.).

Scope and Topics: 

We invite papers addressing, but not limited to, the following topics:

• State-of-the-art performance achieved by transformer-based models.

• Applications of machine learning transformer-based models in healthcare.

• Medical image analysis using transformer-based models.

• Genomics data analysis with transformer-based models.

• Integrating Medical Images and Clinical Text with Transformer-based models for Enhanced Disease Diagnosis.

• Multi-Modal Deep Learning transformer-based Models for Radiology.

• Advancing Precision Medicine with Multimodal Transformers.

• Drug discovery using machine learning transformers.

• Personalized medicine with machine learning transformers.

• Pre-trained transformer-based language models and their application in healthcare.

• Analyzing EHRs using transformer-based models.

• State-of-the-art performance achieved by transformer-based models.

• Limitations and challenges of using machine learning transformers in healthcare.

• Transfer learning with machine learning transformer-based models for healthcare applications.

• Ethical considerations in using machine learning transformer-based models in healthcare.

• Hybrid models combining machine learning transformer-based models with other algorithms for healthcare applications.

• Interpretability and explainability of machine learning transformer-based models for healthcare decision-making. 

Important dates

• Paper Submission Deadline: January 31, 2026

• Paper Acceptance Notification: March 15, 2026

Submission

Papers should be submitted online through the conference submission system

To submit the paper click here and choose special session "Artificial Intelligence in Healthcare: Leveraging Transformer Models" 

All submissions should be original and not previously published or under consideration elsewhere and papers should follow the format in IJCNN’s submission guidelines, which can be found on the IJCNN 2026 webpage.

Papers will undergo a rigorous peer-review process

Authors are encouraged to submit supplementary materials, such as code and data, where applicable.

Any issues or further queries, please contact us by sending email to ali.braytee@uts.edu.au 

Organisers