Patient-Facing Radiology Communication with LLMs: Calibration Deficit and the Metadata Paradox.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Radiology Patient Communication

Background:

• Patients increasingly access radiology reports online and require clarification.
• Large Language Models (LLMs) may aid patient comprehension but their clinical safety is uncharacterized.
• This study evaluates LLM performance in answering patient questions derived from radiology reports.

Purpose of the Study:

• To assess LLM performance heterogeneity, including factual synthesis vs. interpretive reasoning.
• To investigate the 'Metadata Paradox' where demographic priors degrade LLM performance.
• To evaluate LLM calibration characteristics in answering simulated patient queries.

Main Methods:

• Generated 2000 simulated patient inquiries from 200 MIMIC-IV radiology reports.
• Categorized inquiries into factual (e.g., terminology) and interpretive (e.g., diagnostic confidence) tasks.
• Evaluated three LLMs (GPT-4o mini, Grok, Claude 3.5 Sonnet) on 12,000 generated answers using expert-adjudicated quality scoring.

Main Results:

• Grok and Claude 3.5 Sonnet outperformed GPT-4o mini; GPT-4o mini had a 2.8-fold higher failure risk.
• LLMs excelled in factual tasks (Terminology: 98.1%) but struggled with interpretive tasks (Diagnostic Confidence: 82.3%).
• The 'Metadata Paradox' increased failure risk; significant calibration deficits were observed, with high confidence in safety-critical errors.

Conclusions:

• LLMs accurately answer factual radiology report queries but exhibit calibration deficits in interpretive tasks.
• The 'Metadata Paradox' and high confidence in misinformation necessitate caution.
• Independent verification frameworks are essential before deploying LLMs for patient communication regarding radiology reports.