Large Language Models Offer Potential for Helping Appeal Denied Radiology Claims

Why It Matters

AI‑generated appeal letters could slash the time and cost radiology staff spend on insurance denials, but unchecked errors risk claim rejections and legal exposure.

Key Takeaways

• Four LLMs generated 12 appeal letters for simulated radiology denials
• Mean content score 3.9/5, grammar score 4.3/5 across models
• Hallucinations appeared in 16 of 48 letters; 80% offline references fabricated
• 73% of reviewers found letters useful as template drafts
• Human review remains essential before submitting AI‑generated appeals

Pulse Analysis

The administrative load of insurance denials has long plagued radiology departments, diverting clinicians from patient care to paperwork. Large language models, already proving useful for report drafting and decision support, are now being explored as a shortcut for appeal letter creation. By leveraging zero‑shot, few‑shot, and retrieval‑augmented prompting, researchers can produce near‑ready drafts that capture clinical nuance while adhering to payer language, promising a faster turnaround for contested claims.

In the Academic Radiology study, four prominent LLMs were evaluated on 12 simulated appeal scenarios. Content accuracy averaged 3.9 out of 5, and grammar clarity reached 4.3, indicating that the models can generate coherent, persuasive narratives. However, the analysis also uncovered a significant hallucination problem: 33% of letters contained fabricated details, and offline models cited bogus references in 80% of cases. Even the cloud‑based ChatGPT‑4o was not immune, with nearly a third of its citations fabricated. These findings highlight the technology’s promise but also its current unreliability when unchecked.

For health‑system leaders, the takeaway is clear: AI can dramatically reduce the manual effort of appeal preparation, potentially lowering operational costs and accelerating reimbursement cycles. Yet, the risk of misinformation mandates a robust oversight framework, where radiologists or trained staff verify AI output before submission. As LLMs become more integrated with domain‑specific knowledge bases and retrieval tools improve, the balance between efficiency and accuracy is expected to shift, making AI‑assisted appeals a viable component of future radiology revenue‑cycle management.