Precision and Personalization: How Large Language Models Redefining Diagnostic Accuracy in Personalized Medicine - A...

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Personalized Medicine

Background:

• Personalized medicine requires high diagnostic accuracy to address individual variability in disease.
• Large language models (LLMs) offer advanced capabilities for data analysis and pattern recognition.

Purpose of the Study:

• To systematically review the role of LLMs in enhancing diagnostic precision for personalized medicine.
• To analyze the applications, performance, and challenges of LLMs in this domain.

Main Methods:

• Systematic literature review following PRISMA guidelines.
• Searched Web of Science, Science Direct, Scopus, and IEEE Xplore (Jan 2020 - Mar 2025).
• Analyzed 39 peer-reviewed articles on LLMs in personalized medicine.

Main Results:

• LLMs are increasingly integrated into clinical informatics, medical imaging, and decision support.
• LLMs demonstrate potential in identifying subtle patterns for accurate diagnosis and treatment planning.
• Identified challenges include generalizability, dataset biases, data privacy, interpretability, and reliability.

Conclusions:

• LLMs show significant promise for improving diagnostic accuracy and advancing personalized medicine.
• Addressing ethical standards, model validation, and interpretability is crucial for clinical integration.
• Future research should focus on refining LLM technologies for responsible healthcare applications.