Large Language Models May Help Patients and Providers Appeal Denied Radiology Claims
Radiology business reporting highlights a less visible use case for AI: administrative appeals. Large language models could help draft and organize appeals when claims are denied, reducing clerical burden in a heavily bureaucratic part of imaging care.
Much of the AI conversation in radiology focuses on image interpretation, but Radiology Business highlights a different pain point: reimbursement. Denied claims are not glamorous, yet they are a persistent drain on practices and patients, and they consume time that clinicians and staff would rather spend on care.
Large language models are a natural fit for this kind of work because appeal letters require synthesis, documentation, and structured argumentation more than novel medical reasoning. If AI can assemble the relevant facts and produce a coherent draft, it could meaningfully reduce the administrative load around radiology billing disputes.
That said, this is also a domain where hallucinations or subtle inaccuracies could cause real harm. An appeal letter must be precise, evidence-based, and aligned with payer rules, which means these systems will need strong oversight and clear source citation.
Still, the use case is important because it expands the definition of healthcare AI. The most valuable systems may not be the ones that diagnose disease first, but the ones that remove friction from the business of medicine.