We are pleased to invite submissions for a Special Issue of the Journal of Artificial Intelligence in Bioinformatics on Large Language Models for Bioinformatics and Intelligent Healthcare. This special topic aims to showcase high-quality research at the intersection of large language models, artificial intelligence, bioinformatics, biomedical data science, and intelligent healthcare. 

Large language models are rapidly transforming biomedical research and healthcare by enabling natural language understanding, biomedical knowledge discovery, clinical decision support, multimodal data analysis, medical imaging interpretation, drug discovery, workflow automation, and translational research. Their ability to integrate and reason over complex biomedical information provides new opportunities for connecting scientific literature, clinical records, molecular data, imaging data, healthcare knowledge bases, and real-world evidence.

At the same time, the use of large language models in biomedical and healthcare domains raises important challenges, including hallucination, bias, privacy, reproducibility, interpretability, fairness, and clinical safety. Therefore, this special issue particularly welcomes studies that promote responsible innovation, rigorous evaluation, transparent reporting, and trustworthy deployment of LLM-based biomedical systems.

Scope of the Special Issue

We seek original contributions that address theoretical developments, methodological advances, benchmark studies, and practical applications of large language models and artificial intelligence in bioinformatics and intelligent healthcare. Topics of interest include, but are not limited to:

• Large language models for biomedical knowledge discovery

• Biomedical literature mining and scientific question answering

• Evidence synthesis, hypothesis generation, and biomedical knowledge graph construction

• Biomedical natural language processing and clinical text mining

• Electronic health records, clinical notes, medical reports, and clinical summarization

• Biomedical entity recognition, terminology normalization, and text classification

• Medical dialogue systems and patient-centered communication

• Multimodal foundation models for biomedical applications

• Integration of text with medical images, pathology slides, radiology scans, biosignals, wearable data, laboratory reports, structured clinical data, and molecular representations

• LLM-based diagnosis, prediction, interpretation, and clinical decision support

• LLMs for bioinformatics workflows and research automation

• LLM-assisted data analysis, code generation, database querying, pipeline construction, visualization, and statistical interpretation

• Human-in-the-loop biomedical research systems

• LLM agents and autonomous or semi-autonomous biomedical systems

• LLM-based literature review, data preprocessing, model selection, experiment design, medical reasoning, and decision-making support

• Responsible AI, privacy protection, bias evaluation, interpretability, fairness, and clinical safety in biomedical LLM applications