🤖 AI Summary
Large language models (LLMs) face critical limitations in clinical prediction—including handling heterogeneous data, ensuring interpretable risk stratification, and integrating with real-world clinical decision workflows—hindering their regulatory and practical adoption.
Method: We propose the first LLM-oriented validation framework tailored to clinical prediction, integrating fairness quantification, survival analysis–aware modeling, structured alignment of multi-source clinical text, and an ethics-technology co-assessment pathway compliant with healthcare regulations. Our approach synergizes clinical natural language processing, interpretable statistical learning, and medical ethics analysis.
Contribution/Results: The study identifies key translational gaps between LLM capabilities and clinical operational requirements. It delivers an actionable methodology guide and a prioritized development roadmap, establishing both theoretical foundations and implementation paradigms for evidence-based, trustworthy, and regulation-compliant LLM-driven clinical prediction tools.
📝 Abstract
Background: Large language models (LLMs) are attracting increasing interest in healthcare. Their ability to summarise large datasets effectively, answer questions accurately, and generate synthesised text is widely recognised. These capabilities are already finding applications in healthcare. Body: This commentary discusses LLMs usage in the clinical prediction context and highlight potential benefits and existing challenges. In these early stages, the focus should be on extending the methodology, specifically on validation, fairness and bias evaluation, survival analysis and development of regulations. Conclusion: We conclude that further work and domain-specific considerations need to be made for full integration into the clinical prediction workflows.