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Machine learning

Artificial Intelligence in Healthcare: Transfer Learning and Large Language Models Showcase Significant Developments

Strategic Insights

— 2 min read

Research conducted at Sindh Madressatul Islam University has unveiled the substantial impact of Transfer Learning (TL) and large language models on the healthcare sector. The study demonstrates the applications of these models in medical diagnostics, patient services, and clinical process automation, highlighting their potential to boost accuracy and efficiency. However, the research also emphasizes the need for multidisciplinary collaboration to address technical and ethical limitations, such as data protection vulnerabilities and interpretability issues.

Key Takeaways:

  • The study investigates the applications of Transfer Learning and large language models in healthcare systems, focusing on imaging procedures, disease identification, and natural language processing functions for electronic health records analysis and medical decision-making assistance.
  • The research employs pre-trained models through TL to overcome the problems caused by scarce labeled datasets, resulting in effective performance despite low data availability.
  • The study analyzes different transfer learning methods, including inductive, transductive, and unsupervised techniques, demonstrating their effectiveness in detecting COVID-19 from chest X-rays and multi-source disease evaluation.
  • The study presents avenues for future investigation, including domain-specific training approaches and privacy-preserving federated systems with reduced processing needs.
  • The research emphasizes the need for researchers to work across disciplines to resolve technical and ethical limitations, such as data protection vulnerabilities and interpretability issues.
  • Anmol Rahujo, Department of Artificial Intelligence and Mathematical Sciences, Sindh Madressatul Islam University, and his co-authors, Daniya Atif, Syed Azeem Inam, Abdullah Ayub Khan, and Sajid Ullah, conducted the research.
  • The study demonstrates effective healthcare solutions based on TL and LLMs, urging researchers to work together to address technical and ethical limitations.

Statistics:

  • The study analyzes 17 research papers on Transfer Learning and large language models in healthcare systems.
  • The research promotes the use of pre-trained models through TL to enhance the performance of LLMs in healthcare systems.
  • The study highlights the effectiveness of inductive, transductive, and unsupervised transfer learning methods in medical diagnostics and disease identification.
  • The research underscores the importance of resolving technical and ethical limitations, such as data protection vulnerabilities and interpretability issues, to advance the development of AI in healthcare.
  • The publisher of the research paper is Springer, and a free version is available at https://doi-org.sdpl.idm.oclc.org/10.1007/s44163-025-00339-0.

Sources:

  • A survey on the applications of transfer learning to enhance the performance of large language models in healthcare systems. Discover Artificial Intelligence, 2025, 5(1): 1-17. The publisher for Discover Artificial Intelligence is Springer.
  • NewsRx. Sindh Madressatul Islam University Researchers Report Recent Findings in Artificial Intelligence (A survey on the applications of transfer learning to enhance the performance of large language models in healthcare systems). Medical Letter on the CDC & FDA. June 29, 2025; p 394.

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