Topic: Harnessing Large Language Models for Innovations in Cancer Diagnosis and Treatment

Cancer remains one of the leading causes of morbidity and mortality worldwide, posing persistent challenges in early detection, accurate diagnosis, personalized treatment, and long-term care. Recent advances in artificial intelligence (AI)—particularly the development of large language models (LLMs)—have created new opportunities to address these complexities in oncology. Trained on vast biomedical literature and clinical datasets, LLMs exhibit sophisticated natural language processing (NLP) capabilities that enable the interpretation of unstructured medical data, generation of clinical insights, and support for evidence-based decision making.

The integration of LLMs into cancer research and clinical workflows holds the potential to transform multiple aspects of oncology. These models can assist in synthesizing the ever-expanding scientific literature, identifying novel biomarkers, predicting treatment responses, and stratifying patient risk. In clinical settings, LLMs can contribute to improved diagnostic precision, individualized treatment planning, streamlined medical documentation, and enhanced communication between healthcare providers and patients.

This Special Issue aims to showcase cutting-edge research, comprehensive reviews, and forward-looking perspectives on the application of LLMs in oncology. We welcome contributions that explore both the transformative potential of these technologies and the associated limitations, including ethical, technical, and regulatory considerations. By fostering interdisciplinary collaboration among researchers, clinicians, and AI experts, this issue seeks to provide a thorough and insightful overview of how LLMs are reshaping cancer care and improving patient outcomes across a wide spectrum of malignancies.

Potential topics:

● Natural Language Processing in Clinical Decision Support;

● Literature Review Automation;

● Patient-Provider Communication;

● Predictive Analytics for Patient Outcomes;

● Biomarker Discovery;

● Ethical Considerationsin AI Applications;

● Case Studies onClinical Implementation;

● Training LLMs with Real-World Clinical Data;

● Cross-Disciplinary CollaborationsBetween Oncology and AI;

● Future Directions and Challenges.

Large language models, cancer, artificial intelligence, clinical decision support, predictive analytics, biomarker discovery, patient communication, ethical considerations