A survey on the applications of transfer learning to enhance the performance of large language models in healthcare systems

The healthcare field experiences significant developments through transfer learning and large language models that boost medical diagnosis accuracy while improving patient services and clinical process automation. This survey investigates the significant impact of Transfer Learning and large language models on medical systems by explaining their applications in imaging procedures, disease identification, and natural language processing functions for electronic health records analysis and medical decision-making assistance. Pre-trained models employed through TL solve the problems caused by scarce labeled datasets, so systems perform effectively despite low data availability. This research analyzes different transfer learning methods, including inductive, transductive, and unsupervised techniques, while demonstrating their effectiveness in detecting COVID-19 from chest X-rays and multi-source disease evaluation. The remarkable progress of transfer learning cannot overcome crucial obstacles involving data protection vulnerabilities, interpretability issues, and unfavorable knowledge transfer scenarios. The study presents avenues for future investigation, including domain-specific training approaches and privacy-preserving federated systems with reduced processing needs. This study demonstrates effective healthcare solutions based on TL and LLMs but urges researchers to work across disciplines to resolve technical and ethical limitations.