When ChatGPT launched, it seemed like a universal problem-solver. However, ask it to review a workers’ compensation file with conflicting medical opinions, pre-existing conditions, and jurisdiction-specific compliance requirements, and the limitations become clear. Generic AI wasn’t built for this.

Claims work operates in a different universe from everyday AI tasks. It’s not about writing emails or creating to-do lists; it’s about interpreting complex medical terminology, understanding legal liability, ensuring regulatory compliance, and producing documentation that withstands scrutiny in litigation. The stakes are high, the variables are endless, and the margin for error is razor-thin.

What is the difference between generalist and domain-trained AI?

Generalist AI models like ChatGPT are trained on large amounts of publicly available text from across the internet. They’re designed to handle a wide range of conversational tasks reasonably well, but they lack the depth needed for specialized, high-stakes work.

Domain-trained AI, by contrast, is purpose-built. It’s trained on industry-specific data and fine-tuned with subject-matter expertise. In the claims space, that means training on millions of medical records and legal documents, not generic web content. It means understanding the difference between a work status report and a radiology summary, recognizing jurisdiction-specific terminology, and knowing when a document requires escalation to human review.

Why is AI for claims different?

Claims documentation isn’t just complicated; it’s uniquely demanding. A single claim file can span thousands of pages across dozens of document types: medical records, treatment histories, legal dispositions, billing statements, and more. Each document type has its own structure, vocabulary, and relevance to the claim outcome. This isn’t even taking into account the technical aspects of fine tuning the LLM for accurate entity extraction of these various document types (see our tech blog for more on this topic!)

Consider when a claims adjuster needs to identify pre-existing conditions, assess causation, track treatment patterns, evaluate provider behavior, or ensure decisions are defensible in litigation of audits. Generic AI simply wasn’t trained to navigate this landscape.

Human-in-the-loop: the essential safety net

Domain training alone isn’t enough; expert validation is vital. This is where Human-in-the-Loop (HITL) models become non-negotiable. HITL ensures that subject matter experts review AI outputs before they’re used in decision-making. For claims, that means medically trained professionals validating AI medical summaries, AI medical chronology, and extracted data points.

This isn’t about slowing down automation; it’s about ensuring trust. When an AI model flags a discrepancy or encounters a complex edge case, human oversight provides the judgment that no algorithm can replicate. It’s the difference between “processed” and “defensible.”

What’s at stake?

The risks of using generic AI in claims are substantial. Misinterpreted medical terminology can lead to incorrect causation determinations. Failure to meet compliance requirements can expose carriers to regulatory penalties. Inaccurate documentation can undermine litigation defense. Perhaps most critically, errors in claims decisions directly impact people’s lives.

One study found that health insurers averaged nearly a 20% error rate, costing the industry an estimated $17B annually in unnecessary administrative expenses. For individual carriers, the financial and reputational consequences can be devastating.

The Expert-in-the-Loop approach

This is where platforms like Wisedocs demonstrate what’s possible when domain training and expert validation converge. Trained on more than 100 million medical claims documents, Wisedocs isn’t just processing them; it’s understanding them. The domain-trained AI distinguishes between 1,500 distinct document types, from medical visits to depositions, and even work status reports, capturing nuances that generic AI would miss entirely.

However, training alone isn’t the whole story. Every output is validated through Wisedocs’ HITL system, with medically trained professionals reviewing results to ensure they’re defensible and audit-ready. This expert oversight doesn’t slow down processing; in fact, the platform delivers turnaround times up to 80% faster than manual review while maintaining accuracy and compliance that high-stakes workflows demand.

The result is claims intelligence that scales: up to 8x faster page processing, complex case reports configured to specific lines of business, and cross-case insights that surface patterns previously buried in unstructured files. Generic AI was built for everyone. Domain-Trained AI is built for claims. When the stakes involve people’s health, “good enough” isn’t acceptable. The question isn’t whether to use AI in claims; it’s whether you’re using AI that was actually built for the job.