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Generative artificial intelligence in medicine.

Zhen Ling Teo1,2, Arun James Thirunavukarasu3, Kabilan Elangovan1,2

  • 1Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.

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|October 6, 2025

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Summary
This summary is machine-generated.

Generative artificial intelligence (GAI) can automate biomedical tasks using advanced machine learning. New GAI models show promise for improving healthcare with less data and more complex problem-solving abilities.

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Area of Science:

  • Biomedical informatics
  • Artificial intelligence in healthcare
  • Machine learning applications

Background:

  • Generative artificial intelligence (GAI) leverages machine learning and transformer architectures to automate biomedical tasks, including clinical decision support and research analysis.
  • GAI models demonstrate potential for improved performance with smaller, domain-specific datasets compared to traditional deep learning methods.
  • Advancements in AI training techniques, such as weakly supervised and unsupervised learning, reduce reliance on extensive labeled data.

Purpose of the Study:

  • To provide an overview of recent technical advancements in Generative Artificial Intelligence (GAI).
  • To explore the potential of cutting-edge GAI models in enhancing healthcare for both clinicians and patients.
  • To discuss validation strategies, challenges, and opportunities for GAI implementation in biomedical fields.

Main Methods:

  • Overview of recent technical advancements in GAI, including transformer models, agents, mixture-of-expert models, and reasoning models.
  • Exploration of AI training methodologies, progressing from fully supervised to less label-intensive approaches like fine-tuning and reinforcement learning.
  • Analysis of GAI capabilities in generating text, images, and sound data for biomedical applications.

Main Results:

  • GAI models can automate diverse biomedical tasks, from clinical decision support to research design and analysis.
  • Evidence suggests GAI models may achieve better performance with less training data, utilizing smaller, domain-specific datasets.
  • Recent GAI iterations offer enhanced capabilities for complex, multistage tasks, improving efficiency and potential outcomes.

Conclusions:

  • GAI represents a significant technological advancement with the potential to revolutionize healthcare delivery and biomedical research.
  • Further research and validation are necessary to fully realize the benefits of GAI in clinical practice and patient care.
  • Addressing challenges and exploring opportunities in GAI validation will be crucial for its successful integration into the biomedical landscape.