Evaluation Frameworks for Clinical Foundation Models in Specific Tasks of Unstructured Medical Text Analysis: A...

Area of Science:

• Artificial Intelligence in Medicine
• Natural Language Processing in Healthcare
• Clinical Informatics

Background:

• Clinical foundation models (CFMs) are emerging tools for analyzing unstructured clinical text.
• Applications include clinical reasoning, documentation generation, and information extraction.
• Standardized evaluation frameworks for CFMs are currently lacking, posing challenges for safe healthcare integration.

Purpose of the Study:

• To review current evaluation frameworks and methodologies for CFMs applied to electronic health record text.
• To assess how performance, clinical utility, safety, and generalization are evaluated.
• To identify gaps and challenges in CFM evaluation.

Main Methods:

• A scoping review following PRISMA-ScR guidelines.
• Systematic search and selection of relevant records.
• Synthesis of 16 eligible studies on CFM evaluation.

Main Results:

• Progress noted in structured methods, evaluation schemes, and simulated environments for CFMs.
• Inconsistencies persist in operational robustness, particularly concerning clinical reasoning and hallucinations.
• Significant ethical concerns identified, including potential biases and regulatory gaps.

Conclusions:

• No comprehensive framework currently exists for evaluating CFMs for safe use in clinical settings.
• A structured conceptual framework integrating methodological, technical, clinical, and ethical criteria is needed.
• Responsible implementation of CFMs in healthcare requires addressing identified evaluation gaps.