LLM research on public biosignals data is needed to protect patients

Area of Science:

• Biomedical informatics
• Artificial intelligence in medicine
• Digital health

Background:

• Large language models (LLMs) are advancing AI capabilities, presenting new medical applications.
• Public biosignals datasets are essential for developing safe, innovative digital health tools and establishing research benchmarks.
• Current data use agreements often restrict LLM research, hindering progress.

Purpose of the Study:

• To propose recommendations for the ethical release of public biosignals datasets.
• To facilitate research and development of large language models in the medical field.
• To ensure public data supports AI innovation while maintaining ethical standards.

Main Methods:

• Analysis of existing data use agreements and their impact on LLM research.
• Development of a framework for ethical data release.
• Formulation of best practices for public biosignals dataset curation.

Main Results:

• Identification of specific restrictions in current data use agreements that impede LLM studies.
• A set of actionable recommendations for policymakers and data curators.
• Guidelines for balancing data accessibility with ethical considerations and privacy.

Conclusions:

• Ethical and strategic release of public biosignals data is vital for advancing AI in medicine.
• Implementing the proposed recommendations will foster LLM research and the development of novel digital health solutions.
• Ensuring data accessibility while upholding ethical standards is key to unlocking the full potential of AI in healthcare.